Objectives Approach Expected Results Benefits Next Steps Project Schedule Picture / Concept Project Title Input Data / Output Data Reference Name: Email: G#: ‹#› 1 FTC 2016 - Future Technologies Conference 2016 6-7 December 2016 | San Francisco, United States 701 | P a g e 978-1-5090-4171-8/16/$31.00 ©2016 IEEE Prediction of Energy Consumption in Buildings by System Identification Darrion Long Department of Computer Science, Technology and Mathematics Lincoln University Jefferson City, MO, USA [email protected] Nabil Nassif, Ph.D. PE Department of Civil and Architectural Engineering North Carolina A&T State University Greensboro, NC [email protected] Andrew Scott Ours Department of Mathematics Capital University Columbus, Ohio [email protected] Abstract—This paper presents modeling methodologies for predicting energy consumption using system identification. The models discussed will predict the systems performance using the measured input and output. To test and train the models, data was gathered from an existing building. State space, nonlinear, and polynomials models based mathematical functions and tested with different parameters such are temperature, time, and dew point. The results show that the proposed models can output similar energy results. The developed model can be used for energy assessment and diagnosis. Keywords—black box modeling; system identification; energy; energy management; buildings; data driven modeling; models I. INTRODUCTION According to U.S Energy Information Administration (EIA) [1], today’s building in the U.S. consume 72 percent of electricity produced, and use 55 percent of U.S. natural gas. Of this energy consumed the heating, ventilation, and air conditioning (HVAC) system contributes as the largest percentage of the overall energy usage in a building. If current energy use trends continue, building will become the largest consumer of global energy by 2025. In order to diminish the amount of energy consumed by building, several energy efficient strategies have been explored [2]. The black-box models are developed by measuring the data of the system input and output and fitting a mathematical function to the data. However, the development of black-box models does not require the understanding of system physics and they have high accuracy compared to the physics-based models though they suffer from not being able to form conclusion based on the data [3]. These models, propose that the incorporation of computational approaches along with real time data can help generate optimal control strategies as well as energy saving for system designers [4, 5]. Utilizing the system identification (SID) process, various model structures along with different time delays are investigated to determine the best structure yielding satisfactory accuracy in terms of mean square errors (MSE), root mean square error, and co.

1. Objectives
Approach
Expected Results
Benefits
Next Steps
Project Schedule
Picture / Concept
Project Title
Input Data / Output Data
Reference
Name: Email: G#:
‹#›

2. 1
FTC 2016 - Future Technologies Conference 2016
6-7 December 2016 | San Francisco, United States
701 | P a g e
978-1-5090-4171-8/16/$31.00 ©2016 IEEE
Prediction of Energy Consumption in Buildings by
System Identification
Darrion Long
Department of Computer Science,
Technology
and Mathematics
Lincoln University
Jefferson City, MO, USA
[email protected]
Nabil Nassif, Ph.D. PE
Department of Civil and
Architectural Engineering
North Carolina A&T State
University
Greensboro, NC
[email protected]
Andrew Scott Ours

3. Department of Mathematics
Capital University
Columbus,
Ohio
[email protected]
Abstract—This paper presents modeling methodologies for
predicting energy consumption using system identification. The
models discussed will predict the systems performance using the
measured input and output. To test and train the models, data
was gathered from an existing building. State space, nonlinear,
and polynomials models based mathematical functions and
tested
with different parameters such are temperature, time, and dew
point. The results show that the proposed models can output
similar energy results. The developed model can be used for
energy assessment and diagnosis.
Keywords—black box modeling; system identification; energy;
energy management; buildings; data driven modeling; models
I. INTRODUCTION
According to U.S Energy Information Administration
(EIA) [1], today’s building in the U.S. consume 72 percent of
electricity produced, and use 55 percent of U.S. natural gas.
Of this energy consumed the heating, ventilation, and air
conditioning (HVAC) system contributes as the largest
percentage of the overall energy usage in a building. If current
energy use trends continue, building will become the largest
consumer of global energy by 2025. In order to diminish the
amount of energy consumed by building, several energy
efficient strategies have been explored [2]. The black-box
models are developed by measuring the data of the system

4. input and output and fitting a mathematical function to the
data. However, the development of black-box models does not
require the understanding of system physics and they have
high accuracy compared to the physics-based models though
they suffer from not being able to form conclusion based on
the data [3]. These models, propose that the incorporation of
computational approaches along with real time data can help
generate optimal control strategies as well as energy saving
for system designers [4, 5]. Utilizing the system identification
(SID) process, various model structures along with different
time delays are investigated to determine the best structure
yielding satisfactory accuracy in terms of mean square errors
(MSE), root mean square error, and coefficient of variances
(COV). The MSE, RMSE, and COV are employed to evaluate
the approximating capability of the identification models [6].
The necessary development of energy management tools
to prevent wasteful consumption. The use of modeling based
on mathematics and computational methods imitating the
energy consumption, can help predict and avoid mismanaged
use of energy in short-term analysis.
Using SID, a form of black-box modeling, to handle
mathematical models using dry bulb temperatures, dew point,
and number of days. The building selected is a commercial-
sized building in Greensboro, North Carolina. The results of
the SID models are compared to determine the most effective
model by using Coefficient of determination (COD), MSE,
COV.
II. METHODOLOGY
To conduct the proposed study, the following methodology
is used (1) data collection, (2) data analysis and preprocessing,
(3) model development, (4) model testing. The data is

5. collected from an existing building and used for the training
and testing of the model. The models are developed from
using data-driven modeling standards by analyzing trends
within the data, and trends found within the data. This
modeling based on data and mathematics is known as black
box modeling such as system identification where we fit a
mathematical equation to the system that is modeled after. The
objective of this overall research, as shown in Figure 1, is
having a precise mathematical model that predicts energy
consumption and how well that prediction fits to the system.
Fig. 1. A schematic of the methodology
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III. PREPROCESSING AND DATA ANAYLSIS
A. Data Collection
The data used for this research came from two sources.
First, the buildings energy consumption that is converging into
a spreadsheet by smart meter readings of kilowatts every
15minutes. Then, the weather data that is recording hourly and
is in the form of comma separated value (.csv) files, which
consist of possible conditions that affect the output of the
energy consumption such as time of weather recording,
temperature, wind-chill, heat index, dew point, humidity,
pressure, visibility, wind direction, wind speed, gust speed,
precipitation, events, and conditions from the weather records

6. of a local airport within the area of Greensboro, NC. After
collection of hourly data sets for each month, then the data
was merged into a single spreadsheet by assembling each of
the .csv files together chronologically from March 2014 to
May 2016 using command line operations. Hourly data of
each month was then inserted into the spreadsheet with the
energy consumption data.
B. Preprocessing
Now, in the preprocessing phase by duplicating the hourly
weather patterns into every 15minute segments of the hour.
This is done by insuring that weather conditions were hourly
consistent throughout the day to strengthen the creditability of
the data. This is due to trying to reduce the most amount of
noise as possible considering the possible main variable as
inputs into the black-box modeling being temperature, hour of
the day, and dew point. This is evident with data clustered
from within the range shown Figures 2, 3, and 4.
Fig. 2. A graph of how the energy consumption changes over
time
C. Data Anaylsis
The spreadsheet had trends within the data in relation to
the energy consumption which revealed that the variables
would be temperature, time of the day, and dew point
(moisture within the atmosphere).
Fig. 3. A graph of how the energy consumption corresponds to
temperatures

7. Fig. 4. A graph of how the energy consumption changes of
moisture within
the atomosphere
IV. MODELING EQUATIONS
The data-driven techniques used to process the models
were based on black-box modeling method known as system
identification. The purpose of this method, as depicted in
Figure 5, was to focus on the measured input signal and
measured output signal of the system by modeling it
mathematically with equations.
Fig. 5. A schematic of how the system identificaiton method
works
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The mathematical equations that were used for modeling
the system were polynomial, nonlinear autoregressive
(NLAR), and the state space model. This section will discuss
the three model types of model.
A. Polynomial
For a system of utilizing one output and multiple inputs,
the continuous-time ARMAX model can return a goodness-of-
it to the system based specified polynomial order to determine

8. estimated parameters and covariance. The continuous-time
ARMAX model is represented by the following equation: ( ) ( )
= ( ) ( − ) + ( ) ( )
Where A, B, and C are polynomials. y(t) is the output at
time t, u(t) is the input, e(t) us the white-noise disturbance
value ,and is the number of input samples that occur
before the input affects the output.
B. State Space(SS)
The continuous-time models are represented by the
following equation: ( ) = ( ) + ( ) + ( ) ( ) = ( ) + ( )
Where A, B, C, D, and K are state-space matrices. u(t) is
the input, y(t) is the output, e(t) is the disturbance and x(t) is a
vector based on the order of the estimated model.
C. Nonlinear Autoregressive(NLAR)
The nonlinear autoregressive model is represented by the
following equation: ( ) = ( ( − 1), ( − 2), ( − 3), … , ( ), (− 1), (
− 2), ..
Where ( − 1), ( − 2), ( − 3), … , ( ), ( −1), ( − 2), .. are delayed
input and outputs known as
regressors, ( ) is the prediction of the output as a sum of
weight regressors, and is a nonlinear function.
V. BLACK BOX MODELING
System Identification is a black box modeling method.
According to Afram [3], the black-box models are
developed by measuring the data of the system input and
output and fitting a mathematical function to the data.
This is also depicted in Figure 6 with the idea of system

9. identification towards this research.
Fig. 6. Example of a black-box model
In regards to this research, the data set of the system
mainly focused on the year of 2014 as there were difficulties
of fitting a mathematical model to the year of 2015 and 2016
as the building received upgrades in the Heating, Ventilation,
and Air Conditioning (HVAC) system. The year of 2014, prior
to the upgrades allow for the data to be fit with a mathematical
function without the unknown factors of the upgrades. The
different mathematical functions that were available to fit the
data were transfer functions, state space models, process
models, polynomials models, nonlinear models, spectral
models, and correlation models. As stated previously, for the
sake of this research the mathematical functions used for
fitting the energy consumption data were state space models,
polynomials models, and nonlinear models. The data used is
divided into two time series sets (1) training set with inputs
and outputs, from March 17th to October 17th in the year 2014
and the (2) testing set with inputs and outputs from October
18th to December 31st in the year of 2014. State space,
nonlinear, and polynomials models were based on the training
set and were validated with the testing set with five
polynomial models, five state space models and six nonlinear
models with different parameters regarding the inputs and
outputs.
The inputs for the black-box models that will be discussed
are temperature, hour of the day, and dew point for having the
most impact on the output, energy consumption. First,
temperature due to the constant change of throughout years
that will affect the energy consumption of a building by the
use of Heating, Ventilation, and Air Conditioning (HVAC)

10. system for comfort. Second, the hour of the day due the
different temperatures throughout the day that affect the use of
the HVAC, lighting, and electric appliances. Third, dew point
being moisture within the atmosphere would affect the
temperature.
VI. RESULTS
The 16 models used a function based on probability using
the history of the time series data, training and testing set, to
make a prediction of the outputs referred to as the K-step
ahead prediction. The K-step ahead prediction method was
done by focusing on the measured outputs of the model to see
similarities of the system measured output, so a prediction
horizon was set to 3 which became a multiple of the data
sample-time. This allows for a K step prediction to become a 3
step prediction to give a prediction of the measured output,
mean square error (MSE) defined as with the following
equation MSE = ∑ (y − y ) , coefficient of variance (C )
defined as C = , and coefficient of
determination ( ) defined as = ∑ . The fitting of the
mathematical functions brought about the different goodness-
of-it measures based on statistical approach using the COV,
MSE, and COD. The difference between the training and
testing sets shows improvement among the COV, MSE, and
COD that is reflected in Figures 7, 8, and 9.
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11. Fig. 7. Comparsion of the training and testing data COV results
Fig. 8. Comparsion of the training and testing data MSE results
Fig. 9. R-squared values that determines how well the model
fits the system
VII. CONCLUSION
The few mathematical models offer excellent feedback
showing that the coefficient of determination had high
percentages around 70% such as ss2, ss3, and nlarx3. While
model ss2 has the best result there is still room for
improvement to obtain a more accurate model with reduced
MSE and higher COV and R-squared value while using K-
step prediction method to predict a model’s performance with
further predictions to determine if the models can perform
long-term analysis rather than short-term analysis.
The findings in the results came to the most efficient
model that meets the output of the system while maintaining
the least amount of error. The model that shows the most
precession is the State Space models, but specifically ss2 with
results of an R-squared value of 74.77% with the training data
being 0.10075 for COV and 55.454 for MSE. Then, the
results of the testing data being 0.1273 for COV and 89.347
for MSE. This is represented within Figure 10 and 11 with a
fitting of the model “ss2” against the system with measured
inputs (temperature, hours of the through the days, and dew
point) and measured output (energy consumption).
Fig. 10. The fitting of state space model “ss2” to the testing

12. data
VIII. FUTURE WORK
Additional approaches based on results and conclusion
will be implemented in upcoming revisions of this research
that will consist of optimization and further analysis of inputs
that affect energy consumption for more defined models. The
optimization approach is a method that will be an application
of machine learning that will be a autonomous task that to
determine the most effective model out of the developed
models, and construct a model of best fit that can best match
the buildings energy consumption based on analysis of trends
in data using polynomials, state space, and nonlinear auto
regression. In relation to the previous method mentioned the
approach of further analysis of models with fewer inputs that
can determine if less inputs such as temperature, dew point,
and other weather conditions to allow for accurate results of
energy consumption within a building. In addition, the
analysis of inputs mentioned will allow for forecasting into the
unknown periods of time based on the most effective models
predictions based on the known data.
FTC 2016 - Future Technologies Conference 2016
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Fig. 11. The fitting of state space model “ss2” to the training
data
REFERENCES

13. [1] U.S. Energy Information Adminstration EIA,www.eia.gov
[2] ASHARE. 2011. ASHARE Handbook-Applications. Chapter
41.
Atlanta: American Society of Heating Refrigeration and Air
Conditioning Engineers.
[3] Afram, A. and Janabi-Sharifi,F. 2015 Black-box Modeling
of
Residential HVAC System and Comparison of Gray-box and
Black-
box Modeling Methods. Energy and Buildings 94(1):121-49
[4] Nassif,N. 2014. Modeling and Optimization of HVAC
systems using
Artifical Neural Network and Genetic Algorithm. International
Journal of Building Simulation 7 (3):237.245.
[5] Nassif,N. 2008. Self-Turning Dynamic Models of HVAC
System
Components. Energy and Buildings 40:1709-1720.
[6] Buford J. N. Nassif. 2016. The Dynamic Modeling of
Chilled Water
HVAC Systems Using System Identification Methods. 2016
ASHRAE Annual Conference, St Louis, Missouri.
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/CMTT10
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/CMU10
/Cmvtt10
/CMVTT10
/ColonnaMT
/Colossalis-Bold
/ComicSansMS
/ComicSansMS-Bold

26. /Consolas
/Consolas-Bold
/Consolas-BoldItalic
/Consolas-Italic
/Constantia
/Constantia-Bold
/Constantia-BoldItalic
/Constantia-Italic
/CooperBlack
/CopperplateGothic-Bold
/CopperplateGothic-Light
/Copperplate-ThirtyThreeBC
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/Corbel-Bold
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/CordiaNew-Bold
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/CordiaNew-Italic
/CordiaUPC
/CordiaUPC-Bold
/CordiaUPC-BoldItalic
/CordiaUPC-Italic
/Courier
/Courier-Bold
/Courier-BoldOblique
/CourierNewPS-BoldItalicMT
/CourierNewPS-BoldMT
/CourierNewPS-ItalicMT
/CourierNewPSMT
/Courier-Oblique
/CourierStd
/CourierStd-Bold
/CourierStd-BoldOblique
/CourierStd-Oblique

27. /CourierX-Bold
/CourierX-BoldOblique
/CourierX-Oblique
/CourierX-Regular
/CreepyRegular
/CurlzMT
/David-Bold
/David-Reg
/DavidTransparent
/Dcb10
/Dcbx10
/Dcbxsl10
/Dcbxti10
/Dccsc10
/Dcitt10
/Dcr10
/Desdemona
/DilleniaUPC
/DilleniaUPCBold
/DilleniaUPCBoldItalic
/DilleniaUPCItalic
/Dingbats
/DomCasual
/Dotum
/DotumChe
/DoulosSIL
/EdwardianScriptITC
/Elephant-Italic
/Elephant-Regular
/EngraversGothicBT-Regular
/EngraversMT
/EraserDust
/ErasITC-Bold
/ErasITC-Demi
/ErasITC-Light
/ErasITC-Medium

28. /ErieBlackPSMT
/ErieLightPSMT
/EriePSMT
/EstrangeloEdessa
/Euclid
/Euclid-Bold
/Euclid-BoldItalic
/EuclidExtra
/EuclidExtra-Bold
/EuclidFraktur
/EuclidFraktur-Bold
/Euclid-Italic
/EuclidMathOne
/EuclidMathOne-Bold
/EuclidMathTwo
/EuclidMathTwo-Bold
/EuclidSymbol
/EuclidSymbol-Bold
/EuclidSymbol-BoldItalic
/EuclidSymbol-Italic
/EucrosiaUPC
/EucrosiaUPCBold
/EucrosiaUPCBoldItalic
/EucrosiaUPCItalic
/EUEX10
/EUEX7
/EUEX8
/EUEX9
/EUFB10
/EUFB5
/EUFB7
/EUFM10
/EUFM5
/EUFM7
/EURB10
/EURB5

29. /EURB7
/EURM10
/EURM5
/EURM7
/EuroMono-Bold
/EuroMono-BoldItalic
/EuroMono-Italic
/EuroMono-Regular
/EuroSans-Bold
/EuroSans-BoldItalic
/EuroSans-Italic
/EuroSans-Regular
/EuroSerif-Bold
/EuroSerif-BoldItalic
/EuroSerif-Italic
/EuroSerif-Regular
/EUSB10
/EUSB5
/EUSB7
/EUSM10
/EUSM5
/EUSM7
/FelixTitlingMT
/Fences
/FencesPlain
/FigaroMT
/FixedMiriamTransparent
/FootlightMTLight
/Formata-Italic
/Formata-Medium
/Formata-MediumItalic
/Formata-Regular
/ForteMT
/FranklinGothic-Book
/FranklinGothic-BookItalic
/FranklinGothic-Demi

30. /FranklinGothic-DemiCond
/FranklinGothic-DemiItalic
/FranklinGothic-Heavy
/FranklinGothic-HeavyItalic
/FranklinGothicITCbyBT-Book
/FranklinGothicITCbyBT-BookItal
/FranklinGothicITCbyBT-Demi
/FranklinGothicITCbyBT-DemiItal
/FranklinGothic-Medium
/FranklinGothic-MediumCond
/FranklinGothic-MediumItalic
/FrankRuehl
/FreesiaUPC
/FreesiaUPCBold
/FreesiaUPCBoldItalic
/FreesiaUPCItalic
/FreestyleScript-Regular
/FrenchScriptMT
/Frutiger-Black
/Frutiger-BlackCn
/Frutiger-BlackItalic
/Frutiger-Bold
/Frutiger-BoldCn
/Frutiger-BoldItalic
/Frutiger-Cn
/Frutiger-ExtraBlackCn
/Frutiger-Italic
/Frutiger-Light
/Frutiger-LightCn
/Frutiger-LightItalic
/Frutiger-Roman
/Frutiger-UltraBlack
/Futura-Bold
/Futura-BoldOblique
/Futura-Book
/Futura-BookOblique

31. /FuturaBT-Bold
/FuturaBT-BoldItalic
/FuturaBT-Book
/FuturaBT-BookItalic
/FuturaBT-Medium
/FuturaBT-MediumItalic
/Futura-Light
/Futura-LightOblique
/GalliardITCbyBT-Bold
/GalliardITCbyBT-BoldItalic
/GalliardITCbyBT-Italic
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/Garamond
/Garamond-Bold
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/Garamond-LightCondensedItalic
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/GeometricSlab703BT-Light
/GeometricSlab703BT-LightItalic
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/Georgia-Bold
/Georgia-BoldItalic
/Georgia-Italic
/GeorgiaRef
/Giddyup
/Giddyup-Thangs
/Gigi-Regular
/GillSans
/GillSans-Bold
/GillSans-BoldItalic

32. /GillSans-Condensed
/GillSans-CondensedBold
/GillSans-Italic
/GillSans-Light
/GillSans-LightItalic
/GillSansMT
/GillSansMT-Bold
/GillSansMT-BoldItalic
/GillSansMT-Condensed
/GillSansMT-ExtraCondensedBold
/GillSansMT-Italic
/GillSans-UltraBold
/GillSans-UltraBoldCondensed
/GloucesterMT-ExtraCondensed
/Gothic-Thirteen
/GoudyOldStyleBT-Bold
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/GoudyOldStyleBT-Roman
/GoudyOldStyleT-Bold
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/GSIDefaultSymbols
/Gulim
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/Harrington
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33. /Helvetica-BoldOblique
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/Helvetica-Condensed-LightObl
/Helvetica-Condensed-Oblique
/Helvetica-Fraction
/Helvetica-Narrow
…
1
Name:
Class:
Final Project
PROJECT TOPIC:
Object Tracking using Image Registration and Kalman Filter
PAPER REFERENCE:
Paper: Naidu, VPS. and Raol, J.R. “Object Tracking using
Image Registration and Kalman
Filter.” Multi Sensor Data Fusion Lab, Flight Mechanics and
Control Division, National
Aerospace Laboratories, Bangalore-17, India

34. OBJECTIVE:
1. Generate simulated data following steps listed below:
a. Design a registration algorithm called “Sum Absolute
Difference” for detecting
target location (M x N) from frame to frame.
b. Using the target location, compute the center of the target
mass. Center of the
target mass serves as a point for the synthetic data.
c. Repeat steps ‘a’ and ‘b’ to calculate center of the target mass
on each frame.
2. Employing the created data in step one, apply Kalman filter
to the data and make
predictions for the location of the mass in the next frame.
3. Use the real-world human subject data to test and analyze
Kalman filter results
OVERALL APPROACH:
1. Human operator select a target of interest to be tracked from
the reference frame
2. Image registration algorithm locates the selected target
location in the current frame with
respect to the reference frame
a. Sum Absolute Difference (SAD)
b. Normalized Cross Correlation (NCC)
3. Target locations from the image registration algorithm are
passed to a target state
estimator (Kalman Filer)

35. 4. The estimator continuously estimates the position of a
moving target in a sequence of
video frames.
2
DATA:
Human subject is moving at about constant speed, but the center
of the mass of the subject
changes as subject moves from frame to frame. Therefore, the
motion is not linear. Three types
of data sets will be analyzed for target tracking with the subject
moving: 1) along the image
plane, 2) orthogonal to the image plane, and 3) diagonal to the
image plane.
For target moving along the plan:
Real-world human subject data collected @ 30 Hz
3

36. For target moving diagonal to the plan:
For target moving orthogonal to the plan:
Modeling of photovoltaic grid connected generation system
based on parameter
identification method
Ren Jiayu1, Li Chunlai2,Teng Yun 1, Yang Xia1,Yao
Shengpeng1
1 Shenyang University of Technology;No.111, Shenliao West
Road, Economic & Technological Development Zone,
Shenyang, 110870, P.R.China.
2 Qinghai Electric Power Research Institute, Xining 810008,
China.
[email protected]
Abstract—In order to ensure the accuracy of the output of the
photovoltaic power generation system, the identification of the
photovoltaic parameters is carried out. In photovoltaic power
generation system, the analysis is made from two aspects:
photovoltaic array and grid connected inverter. The key
parameters affecting PV model output are found. Using the
recursive least squares method to develop grid-connected PV
system parameter identification, and use of photovoltaic
measured data parameter identification photovoltaic cells and
photovoltaic inverter. Then, a more accurate PV system

37. parameters could be obtained. Finally get a set of more accurate
parameters of photovoltaic power generation system. By
comparing the analytical calculation, system identification
simulation output and photovoltaic measured output, to verify
the feasibility of photovoltaic power generation system
parameters method and results, lay the foundation for further
study on distribution network distributed PV connected to the
distribution network problems.
Key words: PV Power Generation System; Simulation
modeling ;parameters identification; least square method
INTRODUCTION
According to the forecast of solar photovoltaic power
generation in twenty-first Century will occupy an important
place in the world's energy consumption, and will become
the main body of the world's energy supply. With the
relaxation of electric power policy, the photovoltaic power
generation access distribution network has become an
inevitable trend. In order to study the influence of large scale
access to the distribution network, the digital simulation of
power system is an important method in the research
institutions at home and abroad[1].
The key of digital simulation technology of photovoltaic
power generation is to ensure the output characteristic of the
photovoltaic power generation model could in line with the
measured characteristics of the system. For this purpose it
needs more accurate and reasonable parameters of the
photovoltaic power generation system. In control parameters
setting of photovoltaic power generation system, commonly
used methods are theoretical analysis and system
identification method. The theoretical analysis method can
maximize the reproduction of the internal process of
photovoltaic power generation system. So it is widely used

38. by researchers. But because of the complexity of
photovoltaic system, the theoretical analysis method in
solving control parameters encountered nonlinear differential
equations, and some unmeasured variables will affect the
calculation results of the parameters, the parameters can not
be obtained directly for photovoltaic control, often require
multiple manual adjustment; system identification method is
used to model the measured input and output for unknown
parameters. A high fitting degree so the output of the model
and the measured system, the economic system and in the
aerospace, motor control and other fields have been
successfully applied "but in the field of photovoltaic power
generation, photovoltaic array is currently only for
themselves to carry out the identification work, there is no
Research on the overall results on photovoltaic power
generation system[2-3].
In this paper, the photovoltaic array and grid inverter has
carried on the systematic parameter identification. Firstly,
the model of photovoltaic generation system is analyzed, and
get the key parameters that influence the output of the model
of grid connected PV system. Then the recursive least
squares method is used to determine the photovoltaic grid
connected power system parameter identification method,
and the use of photovoltaic measurement data and grid
connected photovoltaic power generation system model for
multiple identification, a specific set of control parameters
are obtained. Finally, the system identification and
theoretical analysis parameters of the photovoltaic operation
characteristics are compared to verify the feasibility of
photovoltaic power control parameters identification, and
enhance the credibility of the photovoltaic power generation
system simulation.
STRUCTURE OF PHOTOVOLTAIC POWER GENERATION

39. SYSTEM
Photovoltaic power generation system is composed of
photovoltaic array can convert light energy into direct
current, and through the inverter dc can transform into ac
power, connected to the grid. The typical photovoltaic
structure as shown in Figure.1.
PV Array DC/DC
Converter
Voltage Source
Converter
Line Filter Grid
+
-
C1 C2
PgQg
Rs
Xs
VDC Vgrid
IDCIPV PPV
VPV
Fig
ure.1 Photovoltaic (PV) power grid structure

40. 2016 International Conference on Smart City and Systems
Engineering
978-1-5090-5530-2/16 $31.00 © 2016 IEEE
DOI 10.1109/ICSCSE.2016.90
378
2016 International Conference on Smart City and Systems
Engineering
978-1-5090-5530-2/16 $31.00 © 2016 IEEE
DOI 10.1109/ICSCSE.2016.90
378
In Figure.1, the grid connected photovoltaic systems
mainly include two parts: photovoltaic cells and photovoltaic
inverters. The ideal equivalent circuit of the photovoltaic cell
is shown in Figure.2, which is obtained by a light source and
a diode in parallel.
VD
+
-
RP
RS
Iph

41. Ipv
IshId
vpv
Figure.2 Equivalent circuit model of PV cells
Its volt-ampere relation is:
sh
sAkT
IRVq
sph R
IRVeIII
s �
�
�
�
�
�
�
�
�
��
�

42. 1
)(
1
Type: V:the output voltage of the photovoltaic cell; I:the
output current of the photovoltaic battery; phI : photo
generated current source; sI : the diode saturation current; Q:
electronic power constant, the value is 1.602×10 19- C;
K:the Boltzmann constant, 1.381 ×10J/K. For this
equivalent circuit, when its open circuit, the output voltage
V corresponding to the open circuit voltage is refocV , ; its
short circuit, the output current I corresponding to the short
circuit current is refscI , .
In order to maximize the utilization of solar energy,
photovoltaic cells need to use maximum power point
tracking control (MTTP), every time increase or reduce the
output voltage of the photovoltaic array, and the observation
of the changes of PV array output power, in order to
determine the adjustment direction of the next step. At this
time equivalent circuit of output voltage V and output
current I corresponding to the maximum power voltage
refmpV , and maximum power point current refmpI , . In
summary, for the photovoltaic power generation system,
we need to identify and control of four
parameters refocV , , refscI , and refmpV , , refmpI , [4-6] .
Photovoltaic system inverter using voltage power outer
loop control and current inner loop control. The power
voltage ring photovoltaic array by MTTP control of DC
voltage and inverter output reactive power respectively with
the reference signal were compared, and the error of PI
control, so as to obtain the reference signals of the inner

43. loop controller drefI and qrefI .
The inner current loop controller adopt dq rotating
coordinate system control, namely the use of orthogonal
Park transformation, the three-phase voltage and current to
the rotating frequency of W transform to dq0 coordinate
system, three-phase symmetrical balance at this time will be
the direct current component. Park coordinate
transformation formula:
abcdq xTx �
0 (2)
Where:
�
�
�
�
�
�
�
�
�
�
�
�
�
�
�

44. ���
��
�
2
1
2
1
2
1
)
3
2sin()
3
2sin(sin
)
3
2cos()
3
2cos(cos
3
2 �
�

45. �
�
ttt
ttt
T
In the inverter control of key parameters of the system
outer loop PI parameters active proportional coefficient PK ,
active integral coefficient iPK , reactive power proportional
coefficient QK , reactive integral coefficient iQK and inner
loop PI parameters direct axis proportional coefficient dK
and direct axis time constant dT , axis ratio coefficient qK ,
quadrature axis time constant qT .
PARAMETER IDENTIFICATION OF
PHOTOVOLTAIC GRID CONNECTED SIMULATION
MODEL
Parameter identification is based on the input and output
information of the system, in a certain criterion, estimated
the unknown parameters of the model, the basic principle is
shown in Figure 3.H(k) and Z(k) is the system input and
output variables, � for unknown model parameters.
Figure 3. Schematic diagram of parameter identification
Commonly used parameter identification method is least
square method, using system input and output data with

46. minimum variance as the goal to parameter estimation
values are constantly revised, in order to obtain more
accurate parameter estimates. For from 1t to nt time input
and output observation ix y and parameter identification
number i� value to construct m linear equations
)()()()( 2211 ixixixiy nn��� ���
�
),,2,1( mi �
(3)
According to the error of the system �� Xy �
, the
corresponding variance J is:
)()(
1
2 ����� XyXyJ TT
m
i
i ��
�
(4)
At this time there is a set of parameters
�
� to meet

47. 379379
yXXX TT 1-
�
� (5)
�
� is called the least squares estimate of � , that is, the
best parameters of the model identification.
In order to reduce the complexity of the least square
method in the case of large matrix dimension, it is needed to
be improved and improved. The basic idea is: new estimate
value = old estimate value and correction, that means the
estimated value is using observational data of the predicted
value is corrected, with the introduction of new
observational data of successive, step by step to estimate the
parameters until the estimated value reached satisfactory
accuracy so far[9-11].
According to the relevant regulations, the single point
access capacity of the distribution network is not more than
6MW, this paper studies the selection of 1MW photovoltaic
power generation system, and the use of digital simulation
software Dig SILENT before and after parameter
identification. PV inverter and the output of the transformer
capacity to take 1MW, inverter AC side voltage to take
0.328kV, inverter control using 0dcU control, the control
power factor of 0.995, through the 10kV to access
distribution network.
The flow chart of control parameter identification of

48. photovoltaic power generation is shown in Figure 4:
Figure 4. Flow chart of PV parameter identification
According to the key control parameters of photovoltaic
power generation system refoc,V refscI , refmp,V
refmp,I , as well as the outer loop control parameter
iQQiPP TKTK ,,, and the inner loop control parameter
qqd TKTK ,,,d , the objective function is the difference
between the measured data and the simulation results.
�
�
�
�
��
�
�
�
�
�
�
�

49. n
i
i
n
i
i
ii
ii
SMG
SMF
1
2
22
1
11
])[(
])[(
�

50. (6)
Type:
ii
IV 11 , photovoltaic battery in the measurement
and Simulation of voltage and current, respectively; i� is
the need to photovoltaic array requires identification of the
parameter T,,,, I,VIV refocrefmprefscrefoc ; ii IV 22 ,
respectively for the measured and simulated output of the
inverter output voltage and current; i
for the PV inverter
need to identify the loop control parameters and inner loop
control parameters T),,,,,,,( qqddiQQiPP TKTKTKTK .
PV SYSTEM IDENTIFICATION RESULTS AND
VERIFICATION
First by analytical method of the photovoltaic system all
the control parameters were calculated, and then set the
photovoltaic system inverter control parameters remain
unchanged, and the control parameters of photovoltaic cell
identification, the parameter analysis and identification of
system parameters as shown in Table 1 below.
Table 1. Parameter identification of PV array
According to the existing model of photovoltaic cells
were set the control parameters of the parameters obtained
from analytical parameters and identification method, and
set the illumination variations in the 1000-600W/m2 within
the scope of mutation, at this time, PV output voltage and
output current waveforms recorded, and photovoltaic

51. measured data of ratio, and the simulation results are as
shown in Figure 5 and Figure 6. Can be seen, when the PV
system illumination disturbance, photovoltaic and network
voltage and current will fluctuate, then resume the steady
state, which after the application of system identification
parameters of the PV model output and the real situation in
steady state operation and fluctuation trend fitting degree
were higher than analytical method. The simulation results.
Figure 5. Results of PV voltage with different parameters
380380
Figure 6. Results of PV current with different parameters
After the identification of the photovoltaic array control
parameters, parameters of the two loop control parameters of
the grid connected inverter are identified, and the parameters
of the system are identified and the parameters of the system
are shown in Table 2.
Table 2. Results of parameters identification of PV inverter
Respectively set control parameters for the parameters
obtained from analytical parameters and identification
method, and set the voltage on DC side of the inverter in 0.5
seconds occurred disturbance reference value of 0.95, power
output waveform of the record of photovoltaic power
generation system, also with photovoltaic measured data
were compared. Simulation results as shown in Figure 7. It
can be seen that the parameter identification characteristics
of photovoltaic inverter power compared with the power
characteristics of the analytical method to recover faster
from the steady state, and actual measurement system

52. variation trend of closer, can better reflect the actual
situation of photovoltaic power generation system.
Figure 7. Output power waveforms of PV inverter with
different
parameters
CONCLUSIONS
(1) The structure and principle of photovoltaic power
generation system are analyzed, and the key parameters of
the array and inverter which affect the output of PV system
model are obtained.
(2) Using the recursive least square method to study the
parameter identification method of distributed photovoltaic
system, and the control parameters of the PV model are
obtained.
(3) Compared with the measured data, the simulation
data of the PV system in the analytical parameters,
identification parameters, and the accuracy of the
identification method and the simulation results are verified.
It is worth pointing out that the initial value of the
parameter identification before setting and the results of the
success of the identification of certain constraints, to be
further improved. But the results of this study will be helpful
to grasp the operation characteristics of photovoltaic power
generation system, and provide the necessary technical
support for the study of the impact of photovoltaic power
distribution network.
ACKNOWLEDGMENTS
Project Supported by QingHai Province Key

53. Laboratory of Photovoltaic grid connected power generation
technology( NO. 2014-Z-Y34A).
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