KAUNO TECHNOLOGIJOS UNIVERSITETAS

E2TA–2019

ELEKTRONIKA, ELEKTRA, TELEKOMUNIKACIJOS, AUTOMATIKA

16-OSIOS STUDENTŲ MOKSLINĖS KONFERENCIJOS PRANEŠIMŲ MEDŽIAGA

KAUNAS 2019

ISSN 2351-6275

ORGANIZACINIS KOMITETAS

Valinevičius Algimantas Andriukaitis Darius Baranauskas Virginijus Bliudžius Tomas Brūzgienė Rasa Chaziachmetovas Andrius Čepėnas Mindaugas Dervinienė Alma Dervinis Gintaras Eidukas Danielius Girdvilis Regimantas Grimaila Vitas Gudžius Saulius Kuzas Pranas Markevičius Vytautas Marozas Vaidotas Narbutaitė Lina Navikas Dangirutis Rimkus Kęstas Sinkevičius Gerdas Svinkūnas Gytis Šeštokas Mindaugas Urniežius Renaldas Židokas Aleksandras

KTU Elektros ir elektronikos fakulteto (EEF) dekanas, org. komiteto pirmininkas EEF prodekanas EEF docentas KTU ESA pirmininkas EEF docentė EEF docentas EEF lektorius EEF prodekanė Automatikos katedros vedėjas Profesorius, LMA akademikas UAB „Lemona“ direktorius UAB „NT Service“ Elektros energetikos sistemų katedros vedėjas EEF docentas EEF profesorius Biomedicininės inžinerijos instituto direktorius Telekomunikacijų katedros docentė Elektronikos inžinerijos katedros vedėjas EEF lektorius UAB „Elgerta Group“ direktorius EEF docentas UAB „Kitron“ gen. direktorius EEF docentas UAB „Belam“ direktoriaus pavaduotojas

Atsakingasis redaktorius Prof. Darius Andriukaitis el. paštas: darius.andriukaitis@ktu.lt Kauno technologijos universitetas, Lietuva

Atsakingasis sekretorius Neringa Dubauskienė el. paštas: neringa.dubauskiene@ktu.lt Kauno technologijos universitetas, Lietuva El. paštas: eta@ktu.lt Pranešimų medžiaga skelbiama tinklo svetainėje: http://eta.ktu.edu

© Kauno technologijos universitetas, 2019

KAUNAS UNIVERSITY OF TECHNOLOGY

E2TA–2019

ELECTRONICS, ENERGY, TELECOMMUNICATIONS AND AUTOMATION

16TH STUDENT SCIENTIFIC CONFERENCE

PROCEEDINGS OF THE 16TH STUDENT SCIENTIFIC CONFERENCE E2TA–2019

KAUNAS 2019

ISSN 2351-6275

ORGANISING COMMITTEE

Valinevičius Algimantas

Andriukaitis Darius Baranauskas Virginijus Bliūdžius Tomas Brūzgienė Rasa Chaziachmetovas Andrius Čepėnas Mindaugas Dervinienė Alma Dervinis Gintaras Eidukas Danielius Girdvilis Regimantas Grimaila Vitas Gudžius Saulius Kuzas Pranas Markevičius Vytautas Marozas Vaidotas Narbutaitė Lina Navikas Dangirutis Rimkus Kęstas Sinkevičius Gerdas Svinkūnas Gytis Šeštokas Mindaugas Urniežius Renaldas Židokas Aleksandras

Dean of Electrical and Electronics Engineering Faculty (EEF), chairman of organising committee

Faculty Vice Dean EEF assoc. prof. Chairman of KTU ESA EEF assoc. prof. EEF assoc. prof. EEF lector Faculty Vice Dean Head of Automation Department Professor, LMA academic Lemona UAB director NT Service UAB Head of Electrical Power Systems Department EEF assoc. prof. EEF professor Director of Biomedical Engineering Institute EEF assoc. prof. Head of Electronics Engineering Department EEF lector Elgerta Group UAB director EEF assoc. prof. Kitron UAB Managing Director EEF assoc. prof. Belam UAB Deputy director

Executive editor Prof. Darius Andriukaitis E-mail: darius.andriukaitis@ktu.lt Kaunas University of Technology, Lithuania

Executive secretary

Neringa Dubauskienė E-mail: neringa.dubauskiene@ktu.lt

Kaunas University of Technology, Lithuania E-mail: eta@ktu.lt Conference papers will be published in website: http://eta.ktu.edu

Registracijos pradžia 2019 m. gegužės 16 d. 9.00 val. Konferencijos pradžia 2019 m. gegužės 16 d. 10 val. Pranešimo trukmė (pristatymas ir klausimai) – 10 min. Profesoriaus Alfonso Jurskio auditorijoje /336 aud., III a., Studentų g. 50, 51368 Kaunas/ Pirmininkauja: Neringa Dubauskienė Konferencijos E2TA 2019 atidarymas. 1. Andrius Simonavičius, Povilas Rūgys, Linas Minkevičius, Aidas Gaška, (Supervisor R. Urniežius) Kaunas University of Technology, Faculty of Electrical and Electronics Engineering Simultaneous object tracking and gimbal control Sinchroninis objektų stebėjimas ir gimbalo valdymas

2. Mantas Mikulėnas, (Supervisor D. Jegelevičius) Kaunas University of Technology, Faculty of Electrical and Electronics Engineering Influence of electrode positioning on impedance plethysmography signal quality Elektrodo padėties įtaka impedanso pletizmografijos signalo kokybei

3. Bingzhong Peng, (Supervisor V. Markevičius) Kaunas University of Technology, Faculty of Electrical and Electronics Engineering

Design and Development of Polarization Device for Polyvinylidene Fluoride (PVDF) Polivinilideno fluorido (PVDF) poliarizacijos įtaiso projektavimas ir kūrimas

4. Himanshu Patel Tuniki, (Supervisor A. Jurelionis) Kaunas University of Technology, Faculty of Civil Engineering and Architecture A Review on Cost Benefit Analysis of Building Automation and Control Systems Pastatų automatizavimo ir valdymo sistemų sąnaudų ir naudos analizės apžvalga

5. Stanislav Ignatavičius, (Vadovas G. Dervinis) Kauno technologijos universitetas, Elektros ir elektronikos fakultetas Pastato suvartojamos elektros energijos statistinis prognozavimo modelis Building electricity consumption statistical forecasting model

6. Andrius Simonavičius, Vygintas Vytartas, (Supervisor R. Urniežius) Kaunas University of Technology, Faculty of Electrical and Electronics Engineering Development of mass airflow system for bioreactor oxygen uptake rate estimation Masės oro srauto sistemos sukūrimas bioreaktoriaus deguonies suvartojimo greičio įvertinimui

7. Juozas Balamutas, Chandana Ravikumar, Povilas Bendinskas, (Supervisor V. Markevičius) Kauno technologijos universitetas, Elektros ir elektronikos fakultetas Heart rate R-R interval monitoring in real time Širdies ritmo R-R intervalo stebėjimas realiu laiku

8. Stanislav Ignatavičius, (Vadovas G. Dervinis) Kauno technologijos universitetas, Elektros ir elektronikos fakultetas Objekto su minimalia duomenų istorija suvartojamos elektros energijos adaptyvaus prognozavimo modelio sukūrimas Creating an adaptive electricity consumption forecasting model for object with minimum data history

9. Saulius Štarolis, (Vadovas S. Japertas) Kaunas University of Technology, Faculty of Electrical and Electronics Engineering Informacinių technologijų ir telekomunikacijų tinklo kibernetinės saugos vertinimo algoritmas Algorithm of evaluation of cyber security of information technology and telecommunications network

10. Povilas Bendinskas, Juozas Balamutas, (Supervisor V. Markevičius) Kauno technologijos universitetas, Elektros ir elektronikos fakultetas Žemės virpesių aptikimas TMR jutikliu Ground vibrations sensing using TMR sensor

11. Deividas Jurgaitis, Arnas Survyla, Andrius Simonavičius, (Vadovas R. Urniežius) Kauno technologijos universitetas, Elektros ir elektronikos fakultetas Stop juostos atpažinimo vaizdų analizės metodu algoritmo sukūrimas ir eksperimentinis tyrimas Creation and testing of stop line recognition algorithm

Kavos pertraukėlė 336 aud. 13.00 Coffee break 336 aud. 13.00 Geriausių darbų apdovanojimai Best papers awards

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I. INTRODUCTION

Real time automatic control of the mechanical gimbal system with 3 BLDC motors is a performance-wise challenging task when the gimbal must track an object of interest while at the same time the mechanical structure, on which gimbal is mounted, moves in the spatial environment. The mechanical motion nature dictates the usage of camera module which has wider field of view, for this purpose fisheye camera system has been proposed. The results show the applicability of the procedure for the application when the target object is located very close to the gimbal system.

II. OBJECTIVES AND EQUIPMENT

Prior to modelling the spatial motion model using Kalman or entropic filters [1]–[2], sensor fusion of the sensors systems [3]–[4] or optimal control planning [5]–[7], the vision analysis to detect the target object has to be performed in the application which is analysed in this text. The main objective was to process an image received from a fisheye lens camera, getting all objects in the requested colour range. The user picks the object to follow that meets HSV criterion and the gimbal control is automatically started so that the object’s position at any time is approximately at the centre of the image and the gimbal itself is oriented horizontally no matter what is the real time position of the mechanical system on which the gimbal is mounted on. The tracking by colour was chosen to determine the applicability of the tracking procedures and gimbal’s response behaviour.

Fig. 1. The experimental gimbal system consisting of three BLDC motors mounted on the test mechanical structure.

The current gimbal implementation infrastructure code enables face detection and tracking in the future applications. The equipment used in this work consists of: • A set of three gimbal BLDC motors mounted on the

construction, offering three degrees of freedom: roll, pitch and yaw as shown in Fig. 1.

• A STorM32 BGC controller [8].

• Fisheye Lens Camera Module ELP-USBFHD01M-L170, with ~180° horizontal field of view (FoV) and ~120° vertical FoV.

• Lenovo IdeaPad 100-15IBD with i3-5005U Intel(R) processor.

III. IMPLEMENTATION

The set of three gimbal BLDC motors is controlled using the BLDC controller and having configured a separate PID for each gimbal. A windows program written in C# (Fig. 2) is used for communication with the controller, image processing and tracking.

Fig. 2. The C# program left side view presents the current video frame with object detected and tracked; the right-side view shows the detected region with image background subtracted.

The program gets a frame from the fisheye lens camera, finds all the objects in the specified colour range, gets the yaw and pitch target angles (by assuming the angle of the object from the camera‘s optical axis is proportional to its distance from the image centre) and sends them through their respective PIDs to the controller, which then controls all gimbal BLDC motors. OpenCV library is used for several image processing methods [9].

IV. A SINGLE FRAME PROCESSING PROCEDURE

When the tracking algorithm is running, the position of

Simultaneous object tracking and gimbal control

Andrius Simonavičius1, Povilas Rūgys1, Linas Minkevičius1, Aidas Gaška1, (Supervisor R. Urniežius1)

1Kaunas University of Technology, Faculty of Electrical and Electronics Engineering linas.minkevicius@ktu.edu

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the target in the image is detected. The calculation of the distance of the target position with respect to the image centre, helps to acquire new gimbal target angles values, which are eventually sent to the gimbal system BLDC motors controller. The overall scheme of a single frame procedure to find the target and its position is shown below (Fig. 3).

Fig. 3. The overview of a single frame processing procedure to detect the target and position the gimbal system.

In the BLDC motors controller, the reference point is the target angle. When subtracting the current angle, the error occurs, which goes through its respective PID block inside the corresponding BLDC controller. The controller then sets the appropriate control signals for all three BLDC motors.

V. EXTRACTION OF OBJECTS BY CRITERION

The objects are extracted from the image by applying a mask of the specified colour range. Colour range is defined by 2 sets of hue, saturation and lightness values (Fig. 4).

Fig. 4. The selection drop-down list-boxes for configuring the target object.

Hue defines the colour, saturation defines the intensity of the colour (values of 0 mean greyscale), and lightness defines its brightness (values of 0 mean black, values of 255 mean white). This results in a matrix of the same resolution as the image, where each respective pixel is

either white (value of 1, in the specified colour range) or black (value of 0, outside of the criterion range). After applying the mask, the matrix goes through a couple of filters that remove all the noise (stray white pixels and stray black pixels). Finally, all the clusters of white pixels are extracted as objects, also referred to as contours, defined by their centre positions in the image, size (pixel count) and several other parameters. The algorithm for getting the mask of the image is shown in Fig. 5.

Fig. 5. The masked selection of the interest regions in a frame.

VI. DETERMINING THE OBJECT TO TRACK

After extracting all the contours, the largest object of a minimum size is chosen from the entire image. The tracking is then started. As long as there is a target acquired, in each frame the largest object within the vicinity of the target from the previous frame is picked, assuming it meets the minimum size criterion. If no objects are found within the specified timeout, the tracking is stopped and the algorithm restarts. This algorithm’s scheme is shown in the scheme of Fig. 6.

After extracting all the contours, the largest object of a minimum size is chosen from the entire image. The tracking is then started. As long as there is a target acquired, in each frame the largest object within the vicinity of the target from the previous frame is picked, assuming it meets the minimum size criterion.

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Fig. 6. Detection of the contour either helps to track the last valid contour from previous frame or picks a new contour which better matches the selection criterion.

Fig. 7. The minimum size criterion allows global detection of the partially occluded target object.

The minimum size criterion allows detection of the target object after it has been partially or fully occluded. If no objects are found within the specified timeout, the tracking is stopped, and the algorithm is restarted.

VII. TUNING OF BLDC CONTROLLER PID PARAMETERS

The final task was to calibrate the PID coefficients as well as acceleration and speed limits. Prior to calibration

the tracking was slow, and while the response rate to changes was good, overall speed was insufficient. The tuning method chosen for optimal behaviour was manual, and each coefficient was changed by taking into account its respective effects on the system. [10] The biggest change in speed was achieved after increasing the acceleration limits of the controller. The final tuned configuration was:

Pitch PID: P = 2.60, I = 40.0, D = 0.0700, Vmax = 140 Roll PID: P = 3.20, I = 85.0, D = 0.1750, Vmax = 140 Yaw PID: P = 4.50, I = 25.0, D = 0.2000, Vmax = 140 Pitch Limits: Speed = none, Acceleration = 0.010 Roll Limits: Speed = none, Acceleration = 0.020 Yaw Limits: Speed = 100.0 °/s, Acceleration = 0.010 The gimbal position closed loop system layout is

presented in Fig. 8.

Fig. 8. The cascade PID closed loop system for positioning gimbal so that the target object is displayed at the centre of the current video frame.

The mechanical control system overview is presented in Fig. 9.

The experimental system has been tested with manual motion of the mechanical structure, on which the gimbal system has been mounted Fig. 10.

The response of the gimbal actuators and the hardware and software system showed that it is applicable for real time applications. The detection and tracking properties of the algorithm behaviour potentially are applicable for future applications in industrial or household installations.

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Fig. 9. The cascade PID closed loop system for recalculated target yaw, roll, and pitch setpoint.

Fig. 10. The simultaneous yaw, roll, pitch control by preserving gimbal horizontal orientation and object tracking.

VIII. CONCLUSIONS

The following points have been concluded: 1. The system can follow an object of any colour as long

as that colour is distinct enough from the environment.

2. The lighting has a big effect on object detection.

3. Tracking colours of low saturation requires a very favourable environment.

4. While the follow motion is smooth, there can be overshooting in faster movements.

5. The estimation error of an angle can go up to 15 degrees but gets insignificantly small as the angle gets smaller.

6. By using OpenCV library’s functions and processing 640 x 480 resolution frames, the program can process up to 30 images per second, which adds up to a 50 ms–100 ms delay in the end (communication with the controller delay also contributes to this). However, such a delay is hardly noticeable practically.

Potential applications:

• By adding face recognition, it can be used in various security systems, where one would not want to lose sight of a particular person in the area.

• In various sports which use balls, with a well written algorithm and cameras from several angles, such a system could easily keep track of the game ball and film it with high precision.

REFERENCES

[1] Giffin, A.; Urniezius, R. The Kalman Filter Revisited Using Maximum Relative Entropy. Entropy 2014, 16, 1047-1069.

[2] Giffin, A.; Urniezius, R. Simultaneous State and Parameter Estimation Using Maximum Relative Entropy with Nonhomogenous Differential Equation Constraints. Entropy 2014, 16, 4974-4991.

[3] Urniezius, R. Online robot dead reckoning localization using maximum relative entropy optimization with model constraints. In AIP Conference Proceedings of Bayesian Inference and Maximum Entropy Methods in Science and Engineering; Mohammad-Djafari, A., Ed.; American Institute of Physics: Melville, NY, USA, 2011; Volume 1305, p. 274.

[4] Renaldas Urniezius, "Research and Development of Dead Reckoning Localization Method", pp. 134, 2012.

[5] Root, Karolis, and Renaldas Urniezius. "Research and development of a gesture-controlled robot manipulator system." In 2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), pp. 353-358. IEEE, 2016.

[6] Urniezius, R.; Galvanauskas, V.; Survyla, A.; Simutis, R.; Levisauskas, D. From Physics to Bioengineering: Microbial Cultivation Process Design and Feeding Rate Control Based on Relative Entropy Using Nuisance Time. Entropy 2018, 20, 779.

[7] Urniezius R, Geguzis E. Hybrid fuzzy logic and adaptive LQR controller for swing-up, positioning and stabilization of inverted pendulum. Elektronika ir Elektrotechnika. 2014 Mar 1;20(3):11-6.

[8] https://github.com/shimat/opencvsharp/releases/tag/2.4.10.20170126

[9] http://www.olliw.eu/2013/storm32bgc/?en [10] http://www.olliw.eu/storm32bgc-wiki/Tuning_Recipe

ABSTRACT

A. Simonavicius, P. Rugys, L. Minkevicius, A. Gaska. Simultaneous object tracking and gimbal control.

This article presents colour object tracking using a set of 3 gimbal BLDC motors. Simultaneously the gimbal system is controlled to its horizontal position no matter what is the orientation of the mechanical system, on which the gimbal system itself is mounted on. Image processing is performed by a computer using OpenCV library. The results show that such tracking can work acceptably well in real life scenarios.

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I. INTRODUCTION

Electrical bioimpedance measurements are characterized by the possibility of assessing the condition of all tissue medium under effect. Body tissues have different conductivity. Blood conductivity is almost 3 times greater than vessel wall or muscle and many times greater than fat or skin. Penetration into the body depends on applied current frequency. In lower frequencies, current incline to flow in extracellular fluid without crossing cell wall. At low frequencies, the cell wall is near infinite isolator so current flow around the cells. Increasing frequency will create a capacitive effect which minimizes cell wall resistance and creates an opportunity for current to cross tissue [1]. However, increased frequency creates reactance and measured values starting to represent body fluid resistance [1]. In frequencies above 100 kHz, current because of capacitive properties overcomes body resistance which becomes near zero [1]. While photo plethysmography used radiation of the red and near-infrared spectral penetrates a few millimetres into the body at the specific spot [2], supplied current penetrates deep into the tissue between measurement electrodes and is affected by electrochemical and biomechanical changes [3]. Those changes in time can be read by impedance plethysmography (IPG) [4].

The IPG has a low amplitude signal that is easily inhibited by other signals generated by the human body. Impedance measurements on the body are affected by multiple factors containing resistance of the skin, the impedance of electrode-skin interface, mechanical movements, breathing, muscle contractions with continues electromyogram (EMG) signal and blood flow in different layers of tissue [1]. EMG signal has most significant effect in the frequency range from 5 Hz to 450 Hz [5]. Its magnitude varies from 0 mV to 10 mV [6]. The normal respiratory rate for an adult is from 16 to 20 breaths/min [7]. Furthermore, noise picked up could have EMI origins too. The larger area monitored should suffer more significant distortions from unwanted signals. This should be caused by increased number of objects in the body which can generate noise.

During the IPG, all pulmonary vasculature is summed up. The purpose of the study was to investigate experimentally how changing the position of the electrodes and the inter-distance will change the quality of the IPG signal. Additionally, find the most suitable arrangement of electrodes on the tissue with which the

strongest IPG signal is obtained and best signal quality is achieved.

II. METHOD

For applying current on the subject, BIOPAC MP150 data acquisition and analysis systems together with EBI100C electro-bioimpedance amplifier were used. Both impedance amplitude and phase were measured. Very small (400 µA) sinusoidal current was injected through the measured tissue volume defined by the placement of a set of current source electrodes and measured by using separated set of monitoring electrodes. Because current value is constant, voltage measurements are proportional to the characteristics of the biological impedance of the tissue volume. Four electrodes (tetrapolar) method was used for tissue resistivity measurements. Two electrodes for applying the current were placed on the body. Voltage drop was measured on electrodes placed between current electrodes. Using tetrapolar method, current flows only through the sample and not through voltage meter reducing distortions. However, at lower frequencies, the common voltage caused by electrodes cannot be completely rejected. It causes reduction of current flow through the sample, reduction of differential gain and amount of current injected into differential amplifier [8].

For assessing signal quality parameters such signal to noise ratio (SNR), harmonic distortions (HD), signal to noise and harmonic distortion ratio (SINAD), relative power of the signal (RPS) and impedance perfusion index (IPI) will be used.

For measurements lower, the leg was selected. It contains long and large vessels in which blood volume changes should have a significant influence on measurement Fig. 1. Muscles mass changes from large to low moving from shank to ankle Fig. 1. Surface area for placing the expanded grid of electrodes on the limb is larger than on the arm.

Fig. 1. Leg cut from top in shank (right) and ankle (left) areas [9].

General purpose disposable electrodes with a resistance of 0.04 Ω were used. Chloride salt gel

Influence of electrode positioning on impedance plethysmography signal quality

Mantas Mikulėnas1, (Supervisor D. Jegelevičius1) 1Kaunas University of Technology, Faculty of Electrical and Electronics Engineering

mantas.mikulenas@ktu.edu

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electrolyte used to mimic the natural biopotential of the skin surface and create an electrode-skin medium. 49 electrodes were divided into 7 lines by 7 electrodes placing them around the lower leg. A grid of electrodes covered the large surface area of the lower leg Fig. 2.

Fig. 2. Electrodes placement [10].

The first digit indicates line number, while second digit – electrode number. The distance between electrodes set to 50 mm creating 300 mm distance between 1st and 7th electrodes in the same line. 19th different measurements by changing distance between current and voltage electrodes were performed at the single frequency on one line of electrodes. Positions and distances between electrodes in the same line presented in Table II. PPG measurements on the finger and 3rd derivation ECG measurements performed as a reference.

Table I. Placement of electrodes. Electrode No.

Distance between voltage

electrodes

Distance between current

electrodes

1st current

(C1)

2nd current

(C2)

1st voltage

(V1)

2nd voltage

(V2)

50 mm

300 mm

1 7 2 3 1 7 3 4 1 7 4 5 1 7 5 6

250 mm 2 7 3 4 2 7 4 5 2 7 5 6

200 mm 3 7 4 5 3 7 5 6

150 mm 4 7 5 6 150 mm 3 6 4 5 150 mm 2 5 3 4 150 mm 1 4 2 3

100 mm 300 mm 1 7 4 6 250 mm 2 7 4 6 200 mm 3 7 4 6

150 mm 300 mm 1 7 3 6 250 mm 2 7 3 6

200 mm 300 mm 1 7 2 6

Before every recording, the settling time of 15 seconds

used for the magnitude of the impedance to stabilize. Magnitude considered settled when changes in phase end. For every placement of electrodes 10s length signals collected. Before filtering Discrete Fourier transform used to show a variety of extraneous frequency components in the IPG signal. Considering that the most signal power is concentrated in IPG signal relative power (𝑃𝑟𝑎𝑡𝑖𝑜)of the signal calculated as the ratio of main harmonic (𝑃𝑚𝑎𝑥) strength with rest of the signal (𝑃𝑠𝑢𝑚) strength

𝑃𝑟𝑎𝑡𝑖𝑜 =𝑃𝑚𝑎𝑥

𝑃𝑠𝑢𝑚. (1)

Total harmonic distortion (THD) calculated as a ratio of extraneous (𝑆𝑒𝑥𝑡) components with main component (𝑆𝑚𝑎𝑖𝑛)

𝑇𝐻𝐷(𝑑𝐵) = 20 log𝑆𝑒𝑥𝑡

𝑆𝑚𝑎𝑖𝑛. (2)

Distortions amplitude (𝐴𝑑𝑖𝑠) calculated multiplying extraneous components ratio (𝑆𝑒𝑥𝑡) with maximum signal amplitude (𝐴𝑚𝑎𝑥)

𝐴𝑑𝑖𝑠 = 𝑆𝑒𝑥𝑡 ∙ 𝐴𝑚𝑎𝑥 . (3)

The zero-phase digital filtering performed, and signals filtered with low pass filter for cancelling the high frequency noise created by EMG and EMI. As well high pass filter used for equalizing magnitude. Butterworth Bandpass Filter used to distinguish IPG frequency from 50 Hz to 100 Hz. Result subtracted from the signal before filtering obtaining the noise. Signal to noise ratio (SNR) calculated

𝑆𝑁𝑅(𝑑𝐵) = 20 ∙ log10(𝐴𝑠𝑖𝑔𝑛𝑎𝑙

𝐴𝑛𝑜𝑖𝑠𝑒). (4)

Signal to noise and distortion ratio (SINAD) stated for data gathered from every electrode position

𝑆𝐼𝑁𝐴𝐷(𝑑𝐵) = 20 log𝐴𝑠𝑖𝑔𝑛𝑎𝑙+𝐴𝑛𝑜𝑖𝑠𝑒+𝐴𝑑𝑖𝑠

𝐴𝑛𝑜𝑖𝑠𝑒+𝐴𝑑𝑖𝑠. (5)

For current flow through the body, impedance perfusion index (IPI) was applied, which states the amount of current lost in the transition between current electrodes

𝐼𝑃𝐼 =𝐴𝐹𝑚𝑎𝑥−𝐴𝐹𝑚𝑖𝑛

𝐴, (6)

where 𝐴𝐹𝑚𝑎𝑥 is maximal filtered impedance amplitude value (), minimal filtered impedance amplitude value (𝐴𝐹𝑚𝑖𝑛), mean of initial data (𝐴).

According to ECG signal peaks signal were cut into waves and averaged. Adding 130 ms signal pulse time shift, IPG and ECG signals were aligned. Example of the cut and averaged IPG signal presented in Fig. 3. Signals normalized and converted to conductivity for clearer IPG representation Fig. 4.

Fig. 3. Averaged impedance signal.

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Fig. 4. Signal converted to conductivity.

Signal stability was examined by moving fixed distance voltage electrodes from ankle to knee. Stability rated by normalized systolic peak amplitude. Lower amplitude represents more unstable systolic peaks. Normalized signals average shows the ratio of P value shift in time during different heart beats.

III. RESULTS

Changing distance between voltage electrodes from 50 mm to 200 mm, increased SNR linearly almost 3 times. Harmonic distortions increased more than two times when distance changed from initial value to 100 mm, but changing distance to 150 mm decreased harmonic distortions more than 5 times. SINAD value was highest when the distance between voltage electrodes was set to 100 mm. Further increase of distance, decreased SINAD value by ~9 %/50 mm. Changing distance between current electrodes from 100 mm to 200 mm, increased SNR and SINAD 4 times and THD 2 times. Value remained stable for SNR, decreased 4 times for SINAD and slightly increased for THD, by changing the distance to 250 mm. Further increase of distance, decreased SNR and 2 times, slightly increasing SINAD.

Changing electrodes positions from shank to ankle with the stable distance between all electrodes showed that the highest SNR, SINAD and the lowest THD were in position C1 = 2, C2 = 5, V1 = 3, V2 = 4. In all other positions parameters were lower more than 2 times.

Changing electrodes possitions around the shank with the stable distance between all electrodes showed that the highest SNR, SINAD and the lowest THD were in lines 7, 2 and 1.

Changing electrodes positions around the ankle with the stable distance between all electrodes showed that the highest SNR, SINAD and the lowest THD were in lines 6, 7 and 3.

Largest relative power of the signal registered with 50 mm distance between voltage electrodes and 100 mm distance between current electrodes. In a shank region with the stable distance between all electrodes largest relative power registered in 1st measurement line, in ankle region in 4th measurement line. Changing electrodes positions from shank to ankle with the stable distance between all electrodes, the largest relative power registered in position C1 = 2, C2 = 5, V1 = 3, V2 = 4.

Lowest IPI registered with 50 mm distance between current electrodes and 200 mm distance between voltage electrodes. In a shank and an ankle region with the stable

distance between all electrodes lowest IPI registered in 5th and 6th measurement lines, while changing electrodes positions from shank to ankle IPI value remained almost stable.

The best stability monitored in measurement lines 1 and 2 with voltage electrodes V1 = 5 and V2 = 6.

IV. DISCUSSION

Impedance plethysmography is a reliable and cost-effective technique for monitoring plethysmography signal in the volume. SNR, THD and SINAD values are highly dependent on the conductive tissue. In regions with more muscle mass results obtained were better than in other areas. Muscle tissue is more conductive than most of the other tissues [11]. Furthermore, it contains many vessels. These factors increase the conductivity of the region and signal strength which overcomes myographic noise. Results of the relative power of the signal show that when the distance between current injection electrodes is smaller, it loses much less power. Completely opposite results obtained voltage electrodes. It is also confirmed by IPI results. While the distance between current electrodes is smaller, current experience less influence from the tissue through which it flows. Another hand, larger tissue can induce larger voltage changes. It is confirmed by larger relative power values obtained on more muscular regions in comparison with less muscular regions.

V. CONCLUSIONS

The position of the electrodes on the limb has a noticeable effect on the signal quality. Signal quality also depends on the distance between the electrodes. In a state of rest, the muscle tissue does not cause excessive distortion to interfere with the IPG signal. Meanwhile, tendons acting opposite. Current absorption decreases by moving the electrodes to a more massive muscle area. The best position of the electrodes for measuring IPG signal is in the massive muscle area. The best arrangement of the current electrodes is as close as possible to each other, while the voltage electrodes as far apart as possible from each other in between current electrodes.

REFERENCES

[1] E. Technologies et al., Emerging Technologies for Nutrition Research. 1997. P. 169-193.

[2] Sarkar, Swarup, AK Bhoi, ir Gyanesh Savita. 2012. „Fingertip Pulse Wave (PPG signal) Analysis and Heart Rate Detection“. International Journal of Emerging … 2(9): 5–9.

[3] Bera, Tushar Kanti. 2014. „Bioelectrical impedance methods for noninvasive health monitoring: A review“. Journal of Medical Engineering 2014.

[4] F. A. Anderson, “Impedance plethysmography in the diagnosis of arterial and venous disease”, University of Massachusetts Medical School, vol. 12, no. 13, pp. 79–102, 1984.

[5] Merletti, Author Roberto, ir Politecnico Torino. 2017. „Standards for Reporting EMG Data“. Journal of Electromyography and Kinesiology 36:

[6] Reaz, M. B.I., M. S. Hussain, ir F. Mohd-Yasin. 2006. „Techniques of EMG signal analysis: Detection, processing, classification and applications“. Biological Procedures Online 8(1): 11–35.

[7] I–II. http://linkinghub.elsevier.com/retrieve/pii/ S1050641117303292.

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[8] A. I. Cano, “Contributions to the measurement of electrical impedance for living tissue,” 2005. “3. Measurement methods,” pp. 55–74.

[9] www.angelwellbeingclinic.co.ukcontent.phppge=runninginjuriesandshinsplints/httpwww.medicalexhibits.commedical_exhibits.phpexhibit=08126_01X

[10] http://humananatomychart.us/muscles-and-tendons-on-inside-of-lower-leg/

[11] C. Gabriel. Compilation of the Dielectric Properties of Body Tissues at RF and Microwave Frequencies, Report N.AL/OE-TR- 1996-0037, Occupational and environmental health directorate, Radiofrequency Radiation Division, Brooks Air Force Base, Texas (USA), 1996. ITIS Foundation database. Tinkle: https://www.itis.ethz.ch/virtual-population/tissue-properties/database/dielectric-properties/

ABSTRACT

M. Mikulenas. Influence of electrode positioning on impedance plethysmography signal quality.

Electrical bioimpedance plethysmography is a reliable and cost-effective technique for monitoring plethysmography signal in the volume. However, a human body can create a lot of distortions and drastically lower quality IPG signal. Selecting a correct place for electrodes is an important task from which depends on expected results. Analysis conducted in the article reveals how IPG signal is affected by the distance between electrodes and placement on different location of the tissue under test. Specified the best suiting position for IPG measurements.

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I. INTRODUCTION

Piezoelectric materials have been widely used for sensors or energy conversion since available of PZT and BaTiO3 in 1954. PZT and BaTiO3 have some properties such as toxicity, inflexibility and so on, which limit the development in a long term [1]. Until the year of 1969, the discovery of PVDF, a kind of piezoelectric polymer material, which has unique advantages that it is flexible and therefore can be formed easily onto the curved surfaces. Generally, there are five main procedures in making piezoelectric PVDF film, including: stretching PVDF films, depositing electrode onto PVDF surface, poling process, wiring in copper plate and laminating with plastic [2]. During these five main steps mentioned above, stretching and poling process determine the characteristic of PVDF. PVDF is a semi-crystalline polymer which exists in four different phases: α, β, γ and δ [3]. Usually β-phase is the most useful one, which can be transformed form α-phase by stretching and poling. Therefore, poling is one of the most important procedures. For poling on the stretched film, temperature and current are two main parameters. Poling at room temperature is called cold poling in general. But cold poling is difficult to overcome the occurrence of arcing or flashover under high electric field. Therefore, the temperature is usually increased from room temperature to 80°C and then slowly cooled down. Applying high electric field is desired that will induce the non-polar crystal to become polar crystal form, hence more β-phase content is gained. On the other hand, to avoid breakdown of PVDF, current needs to be controlled.

II. DESIGN AND DEVELOPMENT

Poling method is illustrated in Fig. 1. There are two metal electrodes which are applied with high voltage. In the middle of two electrodes, there is stretched PVDF film [4].

Fig. 1. Poling method.

According to the poling method, the whole poling device is designed, as shown in Fig. 2. In this device, we use a microcontroller to control the temperature, from

which can get a stable temperature. On the other hand, the metal electrodes are put on the top of thermocouple pad while applied with high voltage. While applying high voltage, we need to keep far away from the device for safety.

Fig. 2. Block diagram of poling device.

According to the design of poling device, we developed the temperature controlling part first, as illustrated in Fig. 3 and Fig. 4.

Fig. 3. Block diagram of temperature controlling.

Figure 3 shows the block diagram of temperature controlling in detail. Microcontroller gets value from upper monitor, and then controls relay to begin heat. At the same time, microcontroller gets the real temperature value from the heater with ADC, and then comparing with the value what user sets, until the real temperature is equal to the setting value.

Fig. 4. User interface of temperature controlling.

Design and development of polarization device for polyvinylidene fluoride (PVDF)

Bingzhong Peng1, (Supervisor V. Markevičius1) 1Kaunas University of Technology, Faculty of Electrical and Electronics Engineering

bingzhong.peng@ktu.edu

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Figure 4 is the user interface, which is developed with Labview. User can set the temperature needed and send it to the microcontroller.

Figure 5 shows the developed entity, including microcontroller part and thermocouple pad. While poling, the thermocouple pad temperature is controlled by the microcontroller. But usually you need to wait until the temperature is steady.

Fig. 5. Temperature controlling entity.

Fig. 6. Block diagram of current measurement and controlling.

The second part of the whole device is the current measurement and controlling part, as shown in Fig. 6 and Fig. 7.

Fig. 7. Current measurement pad.

Usually electric measurements can be performed by either applying a voltage to the sample and measuring the electric current, or by charging the sample with a constant current and measuring the voltage rise. In this case, we need to control the current, therefore, current is measured. Figure 6 is the schematic diagram of current measurement and controlling. Microcontroller gets the output voltage of resistance R and then controls the high voltage to increase or decrease the current.

III. EXPERIMENT AND RESULT

For polarization device, temperature controlling is very

important. Therefore, in order to increase the accuracy of temperature measurement, experiment is curried out. The thermocouple pad is put in constant temperature device SNOL 24/200 with temperature stability of ±1 °C, measuring output voltage. As shown in Fig. 8, temperature vary from 40 °C to 100 °C, output voltage is measured. According to the measuring result, the relationship between temperature and output voltage is calculated.

Fig. 8. Relationship between temperature and output voltage.

IV. CONCLUSIONS

Design and development of polarization device are focused on two main functions, temperature controlling and current controlling. In the experiment part, temperature measurement accuracy is insured by revising the relationship between temperature and output voltage. The device can provide a constant temperature environment for PVDF polarization.

As for experiments of how to change of current and temperature to improve the performance of PVDF during polarization procedure are not yet done. In the future work, polarization procedure needs to be researched.

REFERENCES

[1] Jain, A., Prashanth, K. J., Sharma, A. K., Jain, A., & Rashmi, P. N.(2015). Dielectric and Piezoelectric Properties of PVDF/PZT Composites: A Review.

[2] Kim, H., & Park, I. (2018). Enhanced output power from triboelectric nanogenerators based on electrospun Eu-doped polyvinylidene fl uoride nano fi bers. Journal of Physics and Chemistry of Solids, 117(August 2017), 188–193.

[3] Park, S., Kim, Y., Jung, H., Park, J., Lee, N., & Seo, Y. (2017). Energy harvesting efficiency of piezoelectric polymer film with graphene and metal electrodes. Scientific Reports, (August), 1–8.

[4] Ting, Y., Gunawan, H., Sugondo, A., & Chiu, C. (2013). A New Approach of Polyvinylidene Fluoride (PVDF) Poling Method for Higher Electric Response A New Approach of Polyvinylidene Fluoride (PVDF) Poling Method for Higher Electric Response, 0193.

ABSTRACT

B. Peng. Design and Development of Polarization Device for Polyvinylidene Fluoride (PVDF).

Polarization is an important process that affects the performance of PVDF. How to control the polarization process to achieve high sensitivity of polyvinylidene fluoride attracts a lot of research works. This research focus on the design and development of polarization device for PVDF. This device consists of two main parts: temperature control system and current control system, which can provide a stable environment, including temperature, polarization current.

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I. INTRODUCTION

Building automation and management systems have become embedded in to the modern built environment and its facilities. It extends across all sorts, sizes and functions of facilities for the needs not solely restricted to automation, but also for the free flow of information [1]. Technology has been evolving ever since the microchip revolutionized all the sectors of human life. It has rapidly swarmed into every corner of our lives and has always kept evolving into a better version of itself. For most part of the last decade, various developments were made for improving the lifestyle and comfort conditions of the people. It was not long before it was realized that these comforts were causing a rise in the energy consumption rates and prices. In addition to that, busier lives and evolving technology paved the way for smart devices and energy saving solutions. The type of equipment utilized in the to employ these energy saving solutions within a building, have different levels of impact on the environment. For instance, employing the use of an LCD monitor for the automation systems’ management has a higher environmental impact than the interface which uses infrared emitters for controlling the air conditioning units [2]. The ultimate goal is to make our daily lives easy and cost effective. Focusing on the indoor environment of the buildings, the automation systems could strike a perfect balance between maintaining a healthy atmosphere for the occupants while consuming optimal levels of energy, at the same time. This balance also depends of the behavioural trends of the occupants [3]. As the end-users of the building’s facilities, their behaviour is a prime influencer of the energy performance of the building. Hence, it is important to understand and map the occupants’ behaviour for better energy saving incentives. As stated by Fabi. V. et. al., in [3], the interaction between occupants and the automation systems would alter the indoor environment quality. Here, an interdependency relationship can clearly be noticed between all the variables involved. It would ultimately be the occupants’ behavioural habits, that would create the energy demand. Even the occupants’ perceived idea of comfort and control over the systems, would play a major role during their interaction with the systems [4]. In addition to that, various other factors such as: psychological, physiological, behavioural and personal factors also come into play. Research suggests that the climatic conditions of the buildings’ region greatly affects the decision of the occupants while engaging with the

indoor environmental systems [5]. If the data on the occupants could be fed to the building automation systems, the system could better save energy by adjusting the indoor environment quality and climate. But it cannot be stated outright that the energy use can be reduced by simply employing these automation systems. It takes a complex network of all the systems, sensors, equipment, etc. to handle each and every variable affecting the energy use by either using materials for thermal comfort and cooling [2], or setting a particular algorithm based open/close schedule for windows and doors, etc. Eventually it is the comfortable environment that the building occupants desire, and there are predefined numbers that define these conditions for such an environment. There are many standards in place which define the healthy conditions for the occupants, depending on the type of the building and the region for which the standards were set. For instance, the ASHRAE 90.1 (American Society of Heating, Refrigerating and Air-Conditioning Engineers) standards or the IEA-EBC Annex 66 (International Energy Agency Energy in Buildings and Communities Programme) or the EN 15232 Standard by the European Committee for Standardization [12], set benchmarks that need to be met for complying with the energy regulations. A network of connected sensors, automated ventilation and air-conditioning systems, etc. could make the indoor environment comfortable and healthy for the occupants, all by itself. The emphasis here is on the automation of these systems to not only control the environment within but also to safeguard the building against fire breakout, gas leaks, security, etc. This only makes the systems network more comprehensive and complex. The key lies in the integration and interoperability of the automation systems [6]. This can be achieved by employing any of the software tools available in the market, like, BACNet, LonWorks, HD-PLC, etc.

II. BACKGROUND

The Building Automation and Control Systems’ (BACS) market has been expanding exponentially. Users worldwide have been enthusiastic about employing these systems in their homes and workplaces. As given in the market research reports, in 2015 the BACS market was around $46 billion and is forecasted to reach $100 billion by 2022 [7]. Especially, with the rise in popularity of the smart home concept, users have expressed their intent to invest in the technology for their homes. But, the very immediate thought that hovers over this transaction is

A Review on cost benefit analysis of building automation and control systems

Himanshu Patel Tuniki1, (Supervisor A. Jurelionis1) 1Kaunas University of Technology, Faculty of Civil Engineering and Architecture

himanshu.tuniki@ktu.edu

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about the Return on Investment (ROI) for their investment. Many aspects of the BACS are not easy to quantify or are negligible in some cases. ROI for the investment can only be calculated if the variable can be compared with the invested amount. On these lines, the BACS’s were classified [8] on the basis of their effect on the energy performance of buildings, by the European Standard EN 15232. They are classified into: High energy performance BACS and TBM (Technical Building Management) systems (Class A), Advanced BACS and TBM systems (Class B), Standard BACS (Class C) and Non-energy efficient BACS (Class D). As explained by Ippolito, M. G., et. al., in [9], the extent of influence of BACS and TBM can be quantified using a method specified in the EN 15232. The case study research conducted in [9], considered a mid-sized house in Rome, Italy, which had a floor area of 140 m2 and had calculated the Energy Performance Index to be about 28.2 kWh/m2 per year. And, the Energy Performance Class of the building before and after the inclusion of BACS/TBM was Class D and Class A, respectively. The test house was installed with various BAC systems, temperature probes in each room, open/close detection system for doors and windows, movement and lighting sensors, shutter control system, etc. Upon comparing the energy consumption values for primary heating, for about a year, it was noticed that there was a considerable amount of reduction in the kWh/year values. It is enough evidence to prove the energy saving capacity of the BACS and TBM systems. Another similar case study was conducted by Aghemo,

C., et. al., in [10]. Here, the researchers took only the lighting control system and ten of the offices from the Building Management Department (Politecnico of Torino, Italy) into consideration. They also made both the manual and automatic controls available to the occupants. The goal of the study was to assess the energy consumption, environmental performance and check the operation of the lighting control system. After collecting the data for an activity period of one year, the ratio of automatic lighting control system to the manual lighting control system was approximately 17 %. For proper illuminance, the manual controls were used at times when the illuminance was below the reference levels prescribed by the local energy regulations and standards. The public opinion, as stated in [10], is that though the automatic lighting control system took all the variables into consideration and provided lighting when illuminance dropped below reference levels, it was necessary to have the manual system to override the automatic system when the illuminance is undetected by the sensors or when the sensors are too sensitive to illuminance. It is also concerning that the issue of overriding was seen in 60 % of the cases.

III. DISCUSSION

The prices of BACS, their sensors, software tools, monitoring devices, etc. have dropped drastically over the past decade. For instance, the price of a wall mounted CO2 sensor was roughly 140€ in 2012 [11], and now there are

multiple types of similar sensors being offered by multiple companies at rates as low as 60 € to 70 € each. Any typical user of the BACS and TBM systems, would have the basic expectation from the system which would be to have the least PBP (Pay-Back-Period) on their investment. By considering the first case study [9], PBP for the energy performance classes G to A will be in the decreasing order, i.e., Class G will have the least PBP and Class A will have the highest PBP (assuming the limitations considered in the study). The PBP rates could be altered if the BACS could increase the rate of energy savings, as the researchers have assumed the electrical energy cost as 0.19 €/kWh and for natural gas as 0.091 €/kWh. If a cost-benefit analysis were to be carried out for the thermal energy savings for the BAC passage from efficiency Class A to Class G, the analysis would reveal that for each of the energy performance classes, the benefit to cost ratio is in the increasing order. This implies that, higher the energy consumption, lower is the efficiency class. In this case, the employing of BACS is much more convenient.

The results of these case studies also make some important observations about the workings of BACS in homes and workplaces. These case studies have revealed that the bottom line for any such system would be that, it needs to maintain a balance between the energy use, energy conservation and also have least environmental impact. The only factor which cannot be included in the above list is the price of the entire BACS unit for the building. Since, the type, functionality, number of occupants and requirements of each building would be different from the others, even the design and structure of each BACS unit will be different as well. But, by running the cost-benefit analysis it would establish a relation between the capital invested, ROI and the PBP. Either by utilizing the ratio of total discounted benefits to the total discounted costs over the life span of the systems. Ultimately, it is the PBP value that would be interesting to note as the whole decision of whether or not to employ the BACS would depend on it. Upon plotting a graph between the discounted costs and discounted benefits, the period (PBP) where benefits outperform the costs, could act as a comparative driver between different BACS’s. This way, the analysis can be a prime factor behind the user’s decision to opt for a particular BACS.

ACKNOWLEDGMENT

I would like to thank Dr. Valinevic ius Algimantas for his guidance and motivation which helped me in writing this research paper.

REFERENCES

[1] Iváncsy, T., and Tamus, Z. Á, “Analysis of the Energy Consumption of Building Automation Systems”. Sustainability Through Innovation in Product Life Cycle Design, EcoProduction, 871–881. Available:10.1007/978-981-10-0471-1_59

[2] Cellura, M., Ippolito, M. G., Longo, S., “Energy and environmental impacts of home automation components”, World Sustainable Building Conference, October 28-30, 2014. ISBN: 978-84-697-1815-5. Available: http://www.irbnet.de/daten/iconda/CIB_DC28022.pdf

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[3] Fabi, V., Barthelmesa, V. M., et. Al., “ Insights into the effects of occupant behaviour lifestyles and building automation on building energy use“, AiCARR 50th International Congress; Beyond NZEB Buildings, Energy Procedia 140, 10-11 May 2017, Matera, Italy.

[4] D’Oca, S., Chen, C.-F., Hong, T., & Belafi, Z., “Synthesizing building physics with social psychology: An interdisciplinary framework for context and occupant behavior in office buildings“, Energy Research & Social Science, 34, 240–251. DOI:10.1016/j.erss.2017.08.002.

[5] Tuniki, H. P., and Gultekin-Bicer, P., “A Comparative Case Study Approach: Identifying the Discrepancy between Energy Performance Results“, Architecutal Engineering Institute (AEI) conference, American Society of Civil Engineers, Oklahoma, USA, April 11–13, 2017. doi:10.1061/9780784480502.065.

[6] Ożadowicz, A., and Grela, J., “Impact of Building Automation Control Systems on Energy Efficiency – University Building Case Study”, IEEE 22nd International Conference on Emerging Technologies and Factory Automation (ETFA), 2017. Available:10.1109/etfa.2017.8247686.

[7] “Building Automation System Market Research Report - Global Forecast to 2022”, ID: MRFR/SEM/1878-HCRR, January, 2019.

[8] Ożadowicz, A., and Grela, J., “Building Automation Planning and Design Tool Implementing EN 15 232 BACS Efficiency Classes”,

IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA), 2016.

Available:10.1109/etfa.2016.7733614.

[9] Ippolito, M. G., Riva Sanseverino, E., & Zizzo, G., “Impact of building automation control systems and technical building management systems on the energy performance class of residential buildings: An Italian case study”, Energy and Buildings 69 (2014), pp 33–40. DOI: 10.1016/j.enbuild.2013.10.025.

[10] Aghemo, C., Blaso, L., & Pellegrino, A., “Building automation and control systems: A case study to evaluate the energy and environmental performances of a lighting control system in offices”, Automation in Construction, 43, 10–22, February 15, 2014. Available: http://dx.doi.org/10.1016/j.autcon.2014.02.015, Published by Elsevier B.V. DOI: 10.1016/j.autcon.2014.02.015

[11] Jermolajevas, E., “Ventilation System Control Influence for Building Energy Demand”, Final Master's Thesis, Energy Engineering and Planning Study Program, Vilnius Gedimino Technical University, Faculty of Environmental Engineering Department of Building Energy, Vilnius, 2012.

[12] Kwasnowski, P., and Fedorczak, C., M., “Methodology of Specification and Design of Public Utility Buildings to Reach the Maximum Energy Performance According to EPBD and EN 15232:2012 Standard”, Technical Transactions Architecture, Issue 14/7-A, Poland 2014.

ABSTRACT

Himanshu Patel Tuniki. A review on cost benefit analysis of building automation and control systems.

The Building Automation and Control Systems (BACS) have been in the market for quite a long time. But with the rapid expansion and constant evolution of the technology industry, the BACS have been evolving as well. This leads to a more expensive array of products in the market. Since the ultimate aim is to reduce overall costs, this paper reviews various studies and research done so far in the field, and what more could BACS offer while keeping the costs at a minimum level using the cost benefit analysis.

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I. ĮVADAS

Elektros energijos suvartojimo prognoze yra paklausi, nes sparč iai dide janč io vartotojų skaič iaus, naujų pramoninių regionų vystymosi Kinijoje ir tolimuosiuose rytuose bei dide janč io gyvenvieč ių ir įvairių moksline s paskirties ir gynybos objektų skaič iaus, Rusijos ir Kanados s iaurine se dalyse. Paklausa prognozavimo modeliams, kaip įrankiams, vis dide ja de l konkurenčijos tarp elektros energijos tieke jų [1]. Naudojant elektros suvartojimo prognozavimo modelius, uz tikrinami maz esni energijos nuostoliai, atsirandantys de l neteisingo energijos paskirstymo [1]. Atokiose vietove se esantiems naujiems objektams,

kurie yra elektros energijos vartotojai, pagrindiniai elektros energijos s altiniai naftos produkatai ir vidinio degimo generatoriai. Vis daz niau yra naudojami atsinaujinatys energijso s altiniai – ve jo je gaine s ir saule s baterijos. Kad kuo efektyviau bu tų is naudojama atsinaujinač ių energijos s altinių generuojama energija, bu tina atlikti energijos paskirstymą tam tikru momentu: ar sugeneruota elektra bus vartojama ar kaupiama baterijose. Norint atlikti tokį elektros paskirstymą bu tina naudoti suvartojamos elektros energijos prognozavimo modelius. Turint prognozavimo modelius, energijos tieke jai gali

uz tikrinti reikiamą energijos kiekį tuomet, kai jis yra reikalingas. Elektros energijos perteklius arba tru kumas, perkant arba parduodant elektros energiją realiu laiku, energijos tieke jui gali sukelti didelius finansinius nuostolius [1]. Tieke jui z inant tiksliai, kada ir kokia bus elektros energijos paklausa, jis gali uz tikrinti, kad s i paklausa bus patenkinta, bet norint z inoti s ią paklausą, tieke jas privalo naudoti suvartojamos elektros energijos prognozavimo modelį [2]. Toks is ankstinis vartojimo z inojimas tieke jui leidz ia pasiruos ti ir sukaupti reikiamą energijos kiekį is atsinaujinanč ių energijos s altinių, taip pat, jeigu yra galimybe , naudoti ir maz esnį kiekį pirminių energijos s altinių – anglies, naftos, dujos ir kt.

II. STATISTINIŲ PROGNOZAVIMO MODELIŲ APZ VALGA

Statistiniai prognozavimo modeliai – tai prognozavimo modeliai, kur suvartojama elektros energija yra apras oma kaip matematine priklausomybe nuo tam tikrų parametrų [1]. S ie modeliai yra skirstomi į du pogrupius – sude ties modeliai (angl. additive models) (1), ir daugybos modeliai (angl. multipličative models) (2).

𝐿 = 𝐴 + 𝐵 + 𝐶 + 𝜀, (1) 𝐿 = 𝐴 ∗ 𝐵 ∗ 𝐶 ∗ 𝜀, (2)

č ia: L – suvartojamos elektros energijos prognoze . Suvartojama elektros energija priklauso nuo A, B, C ir ε dedamųjų, kur A dedamoji atitinka bazinę suvartojamos elektros energijos dalį, kitaip vadinama tendenčija (angl. trend). B dedamoji, tai čikline dalis, priklausanti nuo is orinių reis kinių, kurių negali kontroliuoti vartotojas. C dedamoji – tai sezonine dalis, kuri is reis kia nestandartinę sistemos suvartojamos elektros energijos dalį, kuri remiasi į pasitaikanč ias s ventines dienas arba atostogas. ε dedamoji atitinka triuks mą arba trikdz ius pač ioje sistemoje arba objekte (1 pav.).

1 pav. Sudėties prognozavimo modelio komponenčių įtaka bendrai modelio prognozei.

Prie statistinių progozavimo modelių yra priskiriami [1]:

• Eksponentinio is lyginimo modelis (angl. Exponential Smoothing);

• Autoregresiniai modeliai (angl. Autoregressive Model);

• Autoregresinis slenkanč io vidurkio modelis (angl. Autoregressive Moving Average Model);

• Autoregresinis integralinio slenkanč io vidurkio modelis (angl. Autoregressive Integrated Moving Average Model);

• Autoregresiniai slenkanč io vidurkio modeliai su is oriniais kintamaisiais (angl. Autoregressive Moving Average Model with Exogenous Variables).

A. Autoregresiniai modeliai

Autoregresiniai modeliai – statistiniai prognozavimo modeliai, naudojami apibu dinti atsitiktinį pročesą, kuris remiasi prielaida, kad ateities prognoze tiesis kai priklauso nuo buvusių faktinių verč ių (3) [3].

Pastato suvartojamos elektros energijos statistinis prognozavimo modelis

Stanislav Ignatavičius1, (Vadovas G. Dervinis1) 1Kauno technologijos universitetas, Elektros ir elektronikos fakultetas

stanislav.ignatavicius@gmail.com

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𝑋𝑡 = 𝑐 + ∑ 𝜑𝑖 ∗ 𝑋𝑡−𝑖 + 𝜀𝑡𝑁𝑖=1 , (3)

č ia φ1, φ2,..., φn – autoregresinio modelio koefičientus, Xt –elektros energijos prognoze t-uoju laiko momentu. εt –suvartojamos elektros energijos trikdz iai Autoregresinio modelio eile p parodo, kiek diskretinių

laiko z ingsnių atgal sistemos suvartojamos energijos kiekis daro įtaką bu simai sistemos suvartojamos elektros energijos prognozei. Autoregresiniai metodai, kaip ir kiti statistiniai

duomenų analize s metodai, vis daz niau yra naudojami kaip duomenų apdorojimo priemone , siekiant sudaryti tikslesnį elektros apkrovos prognozavimo modelį [3].

B. Autoregresinis prognozavimo modelis su judančiu vidurkiu

ARMA modelis yra jungtinis, sudarytas is autoregresines dalies (AR), ir judanč io vidurkio dalies (MA). MA modelis, tai funkčija, kuri apibu dina trikdz ių ε autoregresinę priklausomybę. Modelio q eile nurodo, kiek diskretinų laiko z ingsnių atgal buvę trikdz iai daro busimam trikdz iui (4).

𝑋𝑡 = 𝜀𝑡 + ∑ 𝜃𝑖 ∗ 𝜀𝑡−𝑖𝑞𝑖=1 , (4)

č ia θi tai MA dedamosios parametrai, εt – modelį t laiko momentu veikiantys trikdz iai. εt-i – pave linti sistemos trikdz iai. Autoregresinis judanč io vidurkio prognozavimo

modelis ARMA turi dualias savybes, kurios yra apjungtos viename modelyje. S io modelio prognoze priklauso ir nuo pries tai buvusio energijos suvartojimo ir nuo buvusių trikdz ių (5) [4], [5].

𝑋𝑡 = 𝑐 + ∑ 𝜑𝑖 ∗ 𝑋𝑡−𝑖 + 𝜀𝑡 +𝑁𝑖=1 ∑ 𝜃𝑖 ∗ 𝜀𝑡−𝑖

𝑞𝑖=1 , (5)

C. Autoregresinis integralinis prognozavimo modelis su slenkančiu vidurkiu

ARIMA prognozavimo mdoelis, tai ARMA prognozavimo modelis, turintis integruojanč ią dalį „I“, kuri yra is reis kiama per skirtumo operatorių (angl. differenče operator) (10) [4]. Skirtumo operatorius naudojamas pas alinti sistemos nestačionarumą. ARMA modelio matematine is rais ka yra pateikta 6-oje formule je. Laiko momento t autoregresinio modelio pirmos eile s skirtumas yra pateiktas 7-oje formule je [4].

𝑋𝑡 = 𝑐 + ∑ 𝜑𝑖 ∗ 𝑋𝑡−𝑖 + 𝜀𝑡 +𝑁𝑖=1 ∑ 𝜃𝑖 ∗ 𝜀𝑡−𝑖

𝑞𝑖=1 , (6)

𝑋𝑡′ = 𝑋𝑡 − 𝑋𝑡−1. (7)

ARIMA modelio matematine is rais ka yra jungtinis modelis, sudarytas is (6) ir (5) formulių (9).

(1 − ∑ 𝜙𝑖 ∗ 𝐿𝑖

𝑝

𝑖=1

) ∗ (1 − 𝐿)𝑑 ∗ 𝑋𝑡 =

= (1 + ∑ 𝜃𝑖 ∗ 𝐿𝑖𝑞

𝑖=1 ) ∗ 𝜀𝑡, (9)

č ia ARIMA modelyje ϕ yra autoregresine s dalies parametras, θ – tai judanč io vidurkio (MA) modelio

parametrai. p – tai autoregresine s dedamosios eile , q – tai judanč io vidurkio dedamosios eile , o d atitinka integruojanč ios dedamosios eilę. Pagrindinis ARIMA prognozavimo modelio tru kumas

yra jo reiklumas duomenims. Norint realizuoti s į modelį, reikia nemaz o kiekio istorinių duomenų. ARIMA modelis yra taikomas trumpalaike ms prognoze ms sudaryti.

D. Autoregresniai prognozavimo modeliai su išoriniais kintamaisiais

ARMAX prognozavimo modelis – tai ARMA modelis su is oriniais kintamaisiais. Modelis yra sudarytas is trijų dalių – AR, MA ir papildomų is orinių kintamųjų. Autoregresiniai modeliai AR, ARMA, ARIMA turi savo atitinkantį modelį su is oriniais kintamaisiais: ARX, ARMAX, ARIMAX. Kitaip sakant, ARMAX modelis pateiktas 10 – oje formule je [6].

𝑋𝑡 = 𝜀𝑡 + ∑𝜑𝑖 ∗ 𝑋𝑡−1 +

+ ∑ 𝜃𝑖 ∗ 𝜀𝑡−𝑖𝑞𝑖=1 + ∑ 𝜂𝑖 ∗ 𝑑𝑡−𝑖

𝑏𝑖=1

.𝑝𝑖=1 (10)

č ia dt-i – tai is orinis kintamasis, ηi – tai is orinio kintamojo parametrai. b atitinka is orinio kintamojo eilę. ARMAX prognozavimo modelis naudojamas, kai yra

papildomi is oriniai kintamieji, kurie gali padidinti įprastinio ARMA prognozavimo modelio tikslumą. Pagrindinis s io modelio realizačijos sude tingumas yra parametrų apskaič iavimas [6].

E. Eksponentinis išlyginimas

Eksponentinio is lyginimo prognozavimo modelyje bu simas elektros energijos suvartojimas yra sudaromas, remiantis istorinių duomenų eksponentis kai apdorotu (angl. weighted) vidurkiu. Modelio verte s is lyginamos, naudojant is lyginimo koefičientą α. Paprasto eksponentinio is lyginimo (angl. Simple exponential smoothing) verte Si laiko momentu i yra gaunama pagal z emiau pateiktą formulę (11) [7].

𝑆𝑖 = 𝛼 ∗ 𝑥𝑖 + (1 − 𝛼) ∗ 𝑆𝑖−1, (11)

č ia Si – is lyginta modelio verte i–uoju laiko momentu. α – is lyginimo koefičientas. xi – sistemos suvartojama elektros energija i –uoju laiko momentu. Si-1 – suglotninta sistemos verte . Eksponentinio is lyginimo modelyje prognozuojama

verte i+1 – uoju laiko momentu priklauso nuo pač io nagrine jamo pročeso formos ir nuo is lygintų pročeso verč ių. Eksponentinio is lyginimo prognozavimo modelis yra naudojamas trumpalaikių prognozių sudaryme, taip pat gali bu ti naudojamas prognozei sudaryti vidutinio bei ilgo laiko intervaluose [7]. S is prognozavimo modelis gali bu ti tikslesnis nei ARIMA, regresinis ir neuroninio tinklo modeliai [7]–[9].

III. EKSPERIMENTO EIGA IR NAUDOJAMI DUOMENYS

Nagrine jamas objektas – Lietuvos tyrimo instituto pastatas esantis Breslaujos g. 3, Kaunas. Gauti pastato suvartojamos elektros energijos duomenys uz laikotarpį nuo 2017-05-18 iki 2018-04-23, duomenys buvo registruojami kas valandą (2 pav.) (1 lentele ).

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1 lentelė. Nagrinėjamo individalaus pastato suvartojamos elektros energijos kiekis.

2017-05-18 2017-05-19 ... 2018-04-23

01:00 34,5 37 ... 40,2

02:00 37 38,4 ... 40,8

03:00 37,2 36,4 ... 40

... ... ... ... ...

17:00 46,6 43,4 ... 47,6

18:00 43,4 37,4 ... 48

... ... ... ... ...

23:00 36,6 38,6 ... 40,6

00:00 36 38,8 ... 40,8

2 pav. LEI pastato suvartotos elektros energijos kiekio grafikas per pirmasias 12 darbo dienų nuo 2017 05 18 dienos.

Prognozavimo modeliai buvo sudaryti tik darbo dienoms. Sudarant prognozavimo modelius, buvo naudojami tik

istoriniai suvartojamos elektros energijos duomenys. Siekiant is gauti didesnį prognozavimo modelio tikslumą, buvo atliekami pastarųjų duomenų apdorojimai [10].

Sudarant statistinius prognozavimo modelius buvo naudojama 40 d.d. arba 960 informačinų tas kų, pagal kuriuos buvo nustatomi modelių parametrai. Modelių testavimui buvo naudojami sekantys 10 d.d. arba 240 tas kų. Modeliai tarpusavyje yra lyginami pagal vidutinę pročentinę paklaidą (angl. mean perčentage error MPE) (12).

𝑀𝑃𝐸 = √∑ (

|𝑦𝑖−𝑦𝑖𝑡|

𝑦𝑖)∗100%𝑁𝑖=1

𝑁, (12)

A. Optimalaus statistinio prognozavimo modelio nustatymo ekperimento eiga

Siekiant nustatyti optimalų autoregresinį prognozavimo modelį, buvo atlikti sekantys veiksmai [11]:

• Nagrine jamo objekto duomenų vizulaine analize ;

• Analitine nagrine jamo objekto duomenų analize , nustatant objekto suvartojamos energijos stačionarumą;

• Autoregresinių prognozavimo modelių struktu ros nustatymas;

• Pasiu lytų autoregresinių modelių struktu ros patykimumo skaič iavimas;

• Autoregresinių prognozavimo modelių parametrų apskaič iaivmas;

• Prognoze s sudarymas.

B. Optimalaus eksponentinio išlyginimo prognozavimo modelio nustatymo eksperimento eiga

Pastarojo modelio is lyginimo koefičiento α optimaliai vertei nustatyti buvo naudojamas gradiento nusileidimo, patobulintas gradiento nusileidimo, dalinimo pusiau, penkių tas kų, auksinio pju vio ir Landveberio paies kos algoritmais. Pasirinkti algoritmai tarpusavyje buvo vertinami pagal optimalaus parametro nustatymo laiką, iteračijų skaič ių bei prognozavimo paklaidą MPE (18).

IV. EKSPERIMENTO REZULTATAI

S iame straipsnyje apras ytas sudarytas trumpojo laikotarpio prognozavimo modelis STLF, kuris prognozuoja energijos suvartojimą valanda į priekį, ne ilgesniam kaip 24 – urių valandų laikotarpiui [12], [13].

A. Optimalaus autoregresinio prognozavimo modelio nustatymas

Atliekant vizualinę duomenų analize nustatyta, kad nagrine jamo objekto duomenyse pasireis kia sezonis kumas. Energijos suvartojimo pasikartojimo intervalas yra 24 val. (3 pav.).

3 pav. LEI analizuojamo pastato suvartota elektros energija kas valandą darbo dienomis 48 val. bėgyje.

Atliekant analitinę duomenų analizę, nustatomas naudojamų duome nų stačionarumas. Stačionarumui nustatyti naudojamas praple stasis Dičkey-Fuller testas (angl. Augmented Dičkey–Fuller test, ADF test). ADF testas buvo atliekamas neapdorotiems duomenims, duomenims su pas alintų duomenų vidurkiu, taip pat duomenų pirmos eile s skirtumui (2 lentele ).

2 lentelė. Duomenų analitinis tyrimas, naudojant ADF testą

Eilės Nr. Duomenys ADF testo

rezultatas Stacianarumas

1 Neapdoroti

duomenys 0 Nestacionarus

2 Duomenys be

vidurkio 1 Stacionarus

3 Duomenų pirmos

eilės skirtumas 1 Stacionarus

Duomenys be vidurkio ir duomenų pirmos eile s

skirtumas yra stačionaru s, tode l postarosioms duomenų imtims autoregresiniai prognozavimo modeliai bus sudaromi. Neapdorotai duomenų imč iai, de l savo nestačionarumo, autoregresiniai prognozavimo modeliai nebus sudaromi. Duomenims be vidurkio ir pirmos eile s skirtumo

duomenų imč ių pronozavimo modelių struktu roms

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nustatyti naudojamos autokoreliačijos (ACF) ir daline s autokoreliačijos (PACF) funkčijos (13), (15).

𝐴𝐶𝐹(ℎ) = �̂� =�̂�(ℎ)

�̂�(0), (13)

𝐴𝐶𝑉𝐹(ℎ) = 𝛾(ℎ) =1

𝑛∗ ∑ (𝑥𝑡+ℎ − �̅�) ∗ (𝑥𝑡 − �̅�)

𝑛−ℎ𝑡=1 , (14)

čia h=0, 1, ..., n-1 – vėlinimo žingsnių skaičius, n - duomenų kiekis. ACFV -autokovariacijos funkcija (angl. sample autocovariance function). �̅� - naudojamų duomenų vidurkis.

𝑃𝐴𝐶𝐹(ℎ) = {1

𝜙ℎℎ̂

𝑘𝑎𝑖 ℎ = 0𝑘𝑎𝑖 ℎ ≥ 1

, (15)

č ia 𝜙ℎℎ̂ – tai koefičientas, kuris apibu dina dalinę autokoreliačiją. S is koefičientas yra lygus paskutiniam koefičientui tiesine je h – osios eile s regresijoje.

4 pav. LEI duomenų su pašalintu vidurkiu ACF funkcijos grafikas.

5 pav. LEI duomenų su pašalintu vidurkiu PACF funkcijos grafikas.

6 pav. LEI duomenų pirmos eilės skirtumo ACF funkčijos grafikas.

7 pav. LEI duomenų pirmos eilės skirtumo PACF funkčijos grafikas.

Pagal gautus ACF ir PACF grafikus daromos is vados kiekvienai duomenų imč iai:

• Duomenų imč iai su pas alintu vidurkiu rekomenduojamas AR(1) modelis, su koefičiento verte ϕ>0 (4 ir 5 pav.).

• Pirmos eile s skirtumo duomenų imč iai rekomenduojamas MA(1) modelis, su koefičiento verte θ

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5 lentelė. Realizuotų modelių patikimumo kriterijų AIC ir BIC vertės.

Modelis AIC BIC

ARMA(1,0,0)xARMA(1,0,0)24 5,0426 5,0578

ARMA(1,0,0)xARMA(1,0,0)25 5,1203 5,1355

ARMA(2,0,0) 5,1847 5,1999

Pagal gautas AIC ir BIC vertes, patikimiausias modelis

yra AR(1) su sezonine dedamąja AR24(1), kai AIC verte yra 5,0426, o BIC kriterijaus verte yra 5,0578. Optimalaus parametrų apskaič iavimo algoritmo

nustatymo rezultatai yra pateikiami lentele je z emiau (6 lentelę). Parametrai apskaič iuojami pagal Maksimalios tikimybe s įvertinimo metodą (angl. Maximum Likelyhood Estimation) (18).

𝐿𝐿𝐹(𝝋, 𝜽, 𝜎𝑎) = −𝑁

2ln(𝜎𝑎

2) −𝑺(𝝋,𝜽)

2𝜎𝑎2 , (18)

𝑆(𝝋, 𝜽) = ∑ 𝑎𝑖2(𝝋, 𝜽)𝑁𝑖=1 , (19)

č ia 𝝋, 𝜽, 𝜎𝑎 – ies komi modelio parametrai, N – duomenų imtis, ai suvartojamos energijos pokytis nuo i-1 z ingsnio.

S(φ,θ) formule s nez inomi autoregresinių modelių parametrai nustatomi pagal: Levenberg – Marquardt, gradiento nusileidimo ir patobulintą gradiento nusileidimo algoritmą. Patobulintas gradiento nusileidimo algoritmas, tai standartinis gradiento nusileidimo algoritmas, kuriame kieviename z ingsnyje yra apskaič iojamos tarpine s parametrų verte s, nuo kurių apskaič iuojama gradiento verte . Palyginamos iteračijos metu gautos paklaidos verte s ir naujas gradiento tas kas tampa tuo tas ku, nuo kurio paklaida yra maz iausia. Optimalus autoregresinių modelių paies kos algoritmas

nustatomas pagal parametrų apskaič iavimo iteračijų skaič ių, nustatymo laiką ir sąlygos tenkinimo, kad pasirinkus skirtingą ies komų parametrų pradine s verte s tas ką, parametrai bu tų rasti tie patys. Bandymai buvo kartojami po tris kartus. Testavimui buvo naudojamas ARMA(1,0,1) struktu ros modelis.

6 lentelė. ARMA(1,0,1) modeliui parametrų paieškos algoritmų rezultatai.

Parametras Algoritmas Parametro

vertė

Įteracijų

skaičius

Skaičiavimo

laikas, s

ϕ Gradiento nusileidimas

0,8371 1791 0,3451

θ -0,2220

ϕ Patobulintas

Graidinto

nusileidimas

0.8371 918 0,6358

θ -0.2220

ϕ Levenberg - Marquardt

0,8324 100 0,7305

θ -0,3855

Įvertinant gautus rezultatus, nuspręsta tolimesniuose

bandymuose naudoti patobulintą gradiento nusileidimo algoritmą. AR, MA ir ARMA geriausių 3-jų prognozavimo modelių

su maz iausią prognozavimo paklaida struktu ra, parametrų verte ir prognozavimo paklaida yra pateikiama z emiau (7 lentele ). Remiantis gautais rezultatais, optimalus autoregresinis

prognozavimo modelis yra AR(1) x AR24(1), nes s io modelio yra maz iausia prognozavimo paklaida 18,5833 %. S io modelio prognozavimo grafikas yra pateikiamas z emiau (8 pav.).

7 letelė. Penkių optimalių AR, MA ir ARMA prognozavimo modelių prognozės paklaida.

Modelis Parametras Parametro

vertė

Prognozavimo

paklaida MSE, %

AR(1)xAR24(1) ϕs 0,8039

18,5833 ΦNS 0,4560

ARMA(2,0,1)

ϕ1 0,8337

19,8389 ϕ2 0,1623 θ1 0,2906

ARMA(2,0,0) ϕ1 1,0833

20,3394 ϕ2 -0,2257

8 pav. AR(1) x AR24(1) modelio energijos suvatojimo prognozės ir tikrojo suvartojimo grafikas.

B. Optimalaus autoregresinio prognozavimo modelio su išoriniais kintamaisiais nustatymas

Pries atliekant optimalaus ARMAX modelio nustatymą, atliekamas nagrine jamo objekto energijos suvartojimo koreliačijos nustatymas su is oriniais kintamaisiais – oro temperatu ra ir santykine dre gme. Koreliačijos verte tarp suvartojamos energijos ir oro

temperatu ros yra 0,193551, kai koreliačija tarp suvartojamos energijos ir santykine s dregme s yra -0,0440654 (9 pav.).

9 pav. LEI pastato suvartojamos elektros energijos, aplinkos oro temperatūros ir santykinės drėgmės grafikas.

Kadangi gautas rys ys tarp suvartojamos energijos ir oro prognoze s yra maz as, prognozavimo modelis su is oriniais kintamaisiais nebuvo sudaromas.

C. Optimalaus eksponentinio išlyginimo prognozavimo modelio nustatymas

Eksponentinio is lyginimo prognozavimo modelio parametro α verte s, gautos naudojant skirtingus paies kos

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algoritmus, pateikiamos z emiau (8 lentele ).

8 lentelė. Eksponentinio išlyginimo parametrų paieškos algoritmų palyginimas.

Algoritmai Parametro

vertė Iteracijos

Skaičiavimo

laikas, s

Gradiento nusileidimo 1,1374 69 0,0361

Pjūvio pusiau 1,1378 2000 0,2313

Auksinio pjūvio 1,1409 17 0,2265

Penkių taškų 1,1377 11 1,4015

Landveberio

optimizacijos 1,1385 88 0,0760

Patobulintas gradiento nusileidimo

1,1385 139 0,0289

Remiantis gautais rezultatais, eksponentinio is lyginimo

modelio geriausiais parametro paies kos algoritmas – patobulintas gradiento nusileidimo algoritmas. Prognozei atlikti bus naudojama patobulinto gradiento nusileidimo algoritmo surasta parametro verte . Prognoze sudaryta, esant α parametro vertei 1,1385.

Gauta pastorojo modelio prognozavimo paklaida yra 24,0032 %. Prognoze s ir tikrojo suvartojimo grafikas yra pateikiami z emiau (10 pav.).

10 pav. Eksponentinio išlyginimo modelio energijos suvatojimo prognozės ir tikrojo suvartojimo grafikas.

V. IS VADOS

Optimalus statistinis prognozavimo modelis, pasirinktam objektui, yra AR(1)xAR24(1). S io modelio prognozavimo vidutine santykine prognozavimo paklaida yra 18,5833 %. Kai modelis sudarytas pagal objekto suvartojamos energijos duomenis su pas alintu vidurkiu, o parametrams apskaič iuoti naudojamas patobulintas gradiento nusileidimo algoritmas. Taip pat pastarasis modelis yra patykimiausias su maz iausiomis AIC ir BIC kriterijų verte mis. Autoregresinis prognozavimo modelis su is oriniais

kintamaisias nebuvo sudaromas de l suvartojamos elektros energijos koreliačijos su is oriniais kintamaisiais. Korelaičija tarp suvartojamos elektros energijos ir oro temperatu ros – 0,193551, o tarp suvartojamos elektros energijos oro santykine s dre gme s – 0,0440654. Eksponentinio is lyginimo prognozavimo modelio

prognoze yra 24,0032 %. Pastarojo modelio prognoze s tiklumas nusileidz ia AR(1)xAR24(1) modelio prognoze s tiklsumui.

PADE KA

Autorius de koja Gitanai Skinzgailaitei uz patarimus.

LITERATU RA

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[11] Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. „Time series analysis : forečasting and čontrol“. 2015, pp. 179 – 209.

[12] Elias Kyriakides, „Short term electric load forecasting: A tutorial“, 2007, Trends in Neural Computation, pp. 391-418. [Online]. Available: https://link.springer.com/chapter/10.1007%2F978 -3-540-36122-0_16

[13] K. Y. Lee, Y. T. Cha, J. H. Park, „Short-term load forecasting using an artificial neural network“. 1992, IEEE Transactions on Power Systems (7). [Online]. Available: https://ieeexplore.ieee.org/abstract/document/141695/

ABSTRACT

S. Ignatavicius. Building electricity consumption statistical forecasting model.

This article presents an optimal statistical electricity consumption forecasting model for individual building. An experimentally determined optimal statistical electricity consumption forecasting model, it's structure, parameters calculating methods and parameters values which coresponds to the minimal forecasting error.

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I. INTRODUCTION

Biotechnological processes are complex and difficult to manage since there are many biochemical and biotechnological factors to take into account. These factors have direct and significant impact on the quality, amount and cost of the end product [1]–[2] and therefore, high degree of diligence with combination of modern high accuracy equipment is required [3]. The equipment must also be able to monitor micro processes. Such monitoring can’t be omitted and it requires high sensitivity, speed and accuracy properties [4]–[5]. During cell cultivations in Kaunas University of Technology bioprocess laboratory and Centre for Innovative Medicine (CIM, Vilnius), one of the important devices used is a mass airflow measuring device (MAF). MAF devices are necessary for oxygen uptake rate (OUR) calculations and control of airflow to bioreactor.

II. MAF MEASUREMENT IMPACT ON OUR

OUR bioprocess parameter is essential for further calculations of other unmeasurable parameters. Hence, accurate data should be used for OUR calculations (1) [6]–[7]

𝑂𝑈𝑅(𝑡) = α ∙ Qγ ∙ 𝑁𝛽 ∙ (CSAT − 𝐶(𝑡)) − 𝐶′(𝑡), (1)

where C '(t) is the rate of dissolved oxygen change in bioreactor, Q - airflow rate to bioreactor, N - rotational speed of the propeller, α, β, γ - empirical constants, parameters depending on bioreactor dimensions and type, CSAT - media for maximum oxygen saturation.

Since the airflow parameter Q is raised to power of γ which is a non-negative number severe inaccuracies in OUR calculations due to Q parameter measurement errors can be expected. The smoothing of the collected data is frequently not an option because of the risk of information loss [8].

Figure 1 shows data collected from “Applikon” MAC controller [9] which represents airflow data during E.coli bacteria cultivation in CIM. In comparison Figure 2 shows data collected during the same cultivation but from MAF sensor system with NTC thermistor. By comparing the two figures one can see a more smoothed and processed data in Figure 1. However this data misses clarity what processes are taking place in the bioreactor thus, Figure 2 is used to determine whether bacterial growth or any other process can be identified.

1 pav. Fig. 1. Data collected from Applikon ez-control mass airflow controller.

Fig. 2. Data collected from NTC thermistor MAF device.

III. MATHEMATICAL MODEL OF MAF AIRFLOW SENSOR

MAF measuring devices normally don’t directly measure actual mass flow. This statement can be easily tested by connecting at least two sensors in series close to each other and when a mass flow is introduced to the system readings from sensors will not be equal and will be effected by how sensors are located in the system and external boundary conditions [10]. Measurements taken

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Development of mass airflow system for bioreactor oxygen uptake rate estimation

Andrius Simonavičius1, Vygintas Vytartas1, (Supervisor R. Urniežius1) 1Kaunas University of Technology, Faculty of Electrical and Electronics Engineering

andrius.simo@gmail.com

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closer to a flow source will be in higher pressure system than measurements near the end of the system where pressure can be near atmospheric. But flow mass does not change through the whole system till the end, i.e. balance equation is preserved. To research this phenomenon and to help quantify it, a mass airflow sensor is conceived. Frequently airflow sensors are based on “hot wire” construction principle [11] which is sensitive to small changes in airflow. While a quick response is one of the desirable attributes, it would be more acceptable to add some inertia to the system. This is required to negate the effects of extra dust built up over time on the sensor resistors’ surface and to soften the blows of momentary spikes in flow from valve switches or any other disturbances that are miniscule in duration but are strong in effect. For this reason, a negative temperature coefficient thermistor (NTC) is chosen as a “hot wire” replacement. A Vishay Ntcle305e4sb [12] thermistor was tested experimentally in variable airflow. The results suggest that thermistors resistance can be measured as a stable reading in about 200 milliseconds which is enough for biotechnological processes which (u