west denmark short term load forecast_for smart grids
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Machine LearningShort-term Load Forecasting in the Electrical Grid
Alexandru Ceoceaaceoce12@student.aau.dk
Mohammed Seifu Kemalmkemal11@student.aau.dk
Robin Doumercrdoume12@student.aau.dk
NDS9Department of Electronic Systems
Aalborg University
Denmark
10
STLF
A. Ceocea,M.S. Kemal,R. Doumerc
IntroductionSmart Grid Networks
Short Term LoadForecasting
Data Collection
Learning AlgorithmsLinear Regression
Neural Networks
ResultsLinear Regression
Neural Networks
Linear Regression vsNeural Networks
ConclusionsConclusions
NDS9Dept. of Electronic Systems
Aalborg UniversityDenmark
Agenda
IntroductionSmart Grid NetworksShort Term Load ForecastingData Collection
Learning AlgorithmsLinear RegressionNeural Networks
ResultsLinear RegressionNeural NetworksLinear Regression vs Neural Networks
ConclusionsConclusions
10
STLF
A. Ceocea,M.S. Kemal,R. Doumerc
Introduction2 Smart Grid Networks
Short Term LoadForecasting
Data Collection
Learning AlgorithmsLinear Regression
Neural Networks
ResultsLinear Regression
Neural Networks
Linear Regression vsNeural Networks
ConclusionsConclusions
NDS9Dept. of Electronic Systems
Aalborg UniversityDenmark
Smart Grid Networks
What is a Smart Grid ?Modernized electrical grid that makes use of information andcommunication technology in order to gather and react oninformation such as the behavior of suppliers and consumersin an automated centralized way
Why Smart Grids ?To improve the efficiency, reliability and sustainability of theproduction and distribution of electricity within the Grid.
10
STLF
A. Ceocea,M.S. Kemal,R. Doumerc
Introduction2 Smart Grid Networks
Short Term LoadForecasting
Data Collection
Learning AlgorithmsLinear Regression
Neural Networks
ResultsLinear Regression
Neural Networks
Linear Regression vsNeural Networks
ConclusionsConclusions
NDS9Dept. of Electronic Systems
Aalborg UniversityDenmark
Smart Grid Networks
What is a Smart Grid ?Modernized electrical grid that makes use of information andcommunication technology in order to gather and react oninformation such as the behavior of suppliers and consumersin an automated centralized way
Why Smart Grids ?To improve the efficiency, reliability and sustainability of theproduction and distribution of electricity within the Grid.
10
STLF
A. Ceocea,M.S. Kemal,R. Doumerc
IntroductionSmart Grid Networks
3 Short Term LoadForecasting
Data Collection
Learning AlgorithmsLinear Regression
Neural Networks
ResultsLinear Regression
Neural Networks
Linear Regression vsNeural Networks
ConclusionsConclusions
NDS9Dept. of Electronic Systems
Aalborg UniversityDenmark
Short Term Load Forecasting
Load Forecasting
I Vitally important for the electric industryI Balance supply and demandI Infrastructure development
Short term Load Forecasting
I From 1 hour to 1 weekI Generation of short term scheduling functionsI Assessing the security of the power systemI Dispatcher information
10
STLF
A. Ceocea,M.S. Kemal,R. Doumerc
IntroductionSmart Grid Networks
3 Short Term LoadForecasting
Data Collection
Learning AlgorithmsLinear Regression
Neural Networks
ResultsLinear Regression
Neural Networks
Linear Regression vsNeural Networks
ConclusionsConclusions
NDS9Dept. of Electronic Systems
Aalborg UniversityDenmark
Short Term Load Forecasting
Load ForecastingI Vitally important for the electric industryI Balance supply and demandI Infrastructure development
Short term Load Forecasting
I From 1 hour to 1 weekI Generation of short term scheduling functionsI Assessing the security of the power systemI Dispatcher information
10
STLF
A. Ceocea,M.S. Kemal,R. Doumerc
IntroductionSmart Grid Networks
3 Short Term LoadForecasting
Data Collection
Learning AlgorithmsLinear Regression
Neural Networks
ResultsLinear Regression
Neural Networks
Linear Regression vsNeural Networks
ConclusionsConclusions
NDS9Dept. of Electronic Systems
Aalborg UniversityDenmark
Short Term Load Forecasting
Load ForecastingI Vitally important for the electric industryI Balance supply and demandI Infrastructure development
Short term Load Forecasting
I From 1 hour to 1 weekI Generation of short term scheduling functionsI Assessing the security of the power systemI Dispatcher information
10
STLF
A. Ceocea,M.S. Kemal,R. Doumerc
IntroductionSmart Grid Networks
3 Short Term LoadForecasting
Data Collection
Learning AlgorithmsLinear Regression
Neural Networks
ResultsLinear Regression
Neural Networks
Linear Regression vsNeural Networks
ConclusionsConclusions
NDS9Dept. of Electronic Systems
Aalborg UniversityDenmark
Short Term Load Forecasting
Load ForecastingI Vitally important for the electric industryI Balance supply and demandI Infrastructure development
Short term Load ForecastingI From 1 hour to 1 weekI Generation of short term scheduling functionsI Assessing the security of the power systemI Dispatcher information
10
STLF
A. Ceocea,M.S. Kemal,R. Doumerc
IntroductionSmart Grid Networks
Short Term LoadForecasting
4 Data Collection
Learning AlgorithmsLinear Regression
Neural Networks
ResultsLinear Regression
Neural Networks
Linear Regression vsNeural Networks
ConclusionsConclusions
NDS9Dept. of Electronic Systems
Aalborg UniversityDenmark
Collected Data
Training data is composed of energy consumption measuredover the course of one year (2011), in West Denmark and isprovided by Energinet.
I DateI Energy consumption (MWh)I Hourly updateI Time frame = 1 year
10
STLF
A. Ceocea,M.S. Kemal,R. Doumerc
IntroductionSmart Grid Networks
Short Term LoadForecasting
Data Collection
Learning Algorithms5 Linear Regression
Neural Networks
ResultsLinear Regression
Neural Networks
Linear Regression vsNeural Networks
ConclusionsConclusions
NDS9Dept. of Electronic Systems
Aalborg UniversityDenmark
Linear Regression
For the forecasting of electric load consumption, regression isused to model the relationship between the load and similarcharacteristics from a previous time frame.
Regression formula used: hθ(x) = θT x =n∑
i=1θixi
I x1 - Day of the weekI x2 - Day of the monthI x3 - Average previous load (24h)I x4 - Load of same time frame (1h) on previous dayI x5 - Load at same time, same day, previous weekI x6 - Month
10
STLF
A. Ceocea,M.S. Kemal,R. Doumerc
IntroductionSmart Grid Networks
Short Term LoadForecasting
Data Collection
Learning Algorithms5 Linear Regression
Neural Networks
ResultsLinear Regression
Neural Networks
Linear Regression vsNeural Networks
ConclusionsConclusions
NDS9Dept. of Electronic Systems
Aalborg UniversityDenmark
Linear Regression
For the forecasting of electric load consumption, regression isused to model the relationship between the load and similarcharacteristics from a previous time frame.
Regression formula used: hθ(x) = θT x =n∑
i=1θixi
I x1 - Day of the weekI x2 - Day of the monthI x3 - Average previous load (24h)I x4 - Load of same time frame (1h) on previous dayI x5 - Load at same time, same day, previous weekI x6 - Month
10
STLF
A. Ceocea,M.S. Kemal,R. Doumerc
IntroductionSmart Grid Networks
Short Term LoadForecasting
Data Collection
Learning Algorithms5 Linear Regression
Neural Networks
ResultsLinear Regression
Neural Networks
Linear Regression vsNeural Networks
ConclusionsConclusions
NDS9Dept. of Electronic Systems
Aalborg UniversityDenmark
Linear Regression
For the forecasting of electric load consumption, regression isused to model the relationship between the load and similarcharacteristics from a previous time frame.
Regression formula used: hθ(x) = θT x =n∑
i=1θixi
I x1 - Day of the week
I x2 - Day of the monthI x3 - Average previous load (24h)I x4 - Load of same time frame (1h) on previous dayI x5 - Load at same time, same day, previous weekI x6 - Month
10
STLF
A. Ceocea,M.S. Kemal,R. Doumerc
IntroductionSmart Grid Networks
Short Term LoadForecasting
Data Collection
Learning Algorithms5 Linear Regression
Neural Networks
ResultsLinear Regression
Neural Networks
Linear Regression vsNeural Networks
ConclusionsConclusions
NDS9Dept. of Electronic Systems
Aalborg UniversityDenmark
Linear Regression
For the forecasting of electric load consumption, regression isused to model the relationship between the load and similarcharacteristics from a previous time frame.
Regression formula used: hθ(x) = θT x =n∑
i=1θixi
I x1 - Day of the weekI x2 - Day of the month
I x3 - Average previous load (24h)I x4 - Load of same time frame (1h) on previous dayI x5 - Load at same time, same day, previous weekI x6 - Month
10
STLF
A. Ceocea,M.S. Kemal,R. Doumerc
IntroductionSmart Grid Networks
Short Term LoadForecasting
Data Collection
Learning Algorithms5 Linear Regression
Neural Networks
ResultsLinear Regression
Neural Networks
Linear Regression vsNeural Networks
ConclusionsConclusions
NDS9Dept. of Electronic Systems
Aalborg UniversityDenmark
Linear Regression
For the forecasting of electric load consumption, regression isused to model the relationship between the load and similarcharacteristics from a previous time frame.
Regression formula used: hθ(x) = θT x =n∑
i=1θixi
I x1 - Day of the weekI x2 - Day of the monthI x3 - Average previous load (24h)
I x4 - Load of same time frame (1h) on previous dayI x5 - Load at same time, same day, previous weekI x6 - Month
10
STLF
A. Ceocea,M.S. Kemal,R. Doumerc
IntroductionSmart Grid Networks
Short Term LoadForecasting
Data Collection
Learning Algorithms5 Linear Regression
Neural Networks
ResultsLinear Regression
Neural Networks
Linear Regression vsNeural Networks
ConclusionsConclusions
NDS9Dept. of Electronic Systems
Aalborg UniversityDenmark
Linear Regression
For the forecasting of electric load consumption, regression isused to model the relationship between the load and similarcharacteristics from a previous time frame.
Regression formula used: hθ(x) = θT x =n∑
i=1θixi
I x1 - Day of the weekI x2 - Day of the monthI x3 - Average previous load (24h)I x4 - Load of same time frame (1h) on previous day
I x5 - Load at same time, same day, previous weekI x6 - Month
10
STLF
A. Ceocea,M.S. Kemal,R. Doumerc
IntroductionSmart Grid Networks
Short Term LoadForecasting
Data Collection
Learning Algorithms5 Linear Regression
Neural Networks
ResultsLinear Regression
Neural Networks
Linear Regression vsNeural Networks
ConclusionsConclusions
NDS9Dept. of Electronic Systems
Aalborg UniversityDenmark
Linear Regression
For the forecasting of electric load consumption, regression isused to model the relationship between the load and similarcharacteristics from a previous time frame.
Regression formula used: hθ(x) = θT x =n∑
i=1θixi
I x1 - Day of the weekI x2 - Day of the monthI x3 - Average previous load (24h)I x4 - Load of same time frame (1h) on previous dayI x5 - Load at same time, same day, previous weekI x6 - Month
10
STLF
A. Ceocea,M.S. Kemal,R. Doumerc
IntroductionSmart Grid Networks
Short Term LoadForecasting
Data Collection
Learning AlgorithmsLinear Regression
6 Neural Networks
ResultsLinear Regression
Neural Networks
Linear Regression vsNeural Networks
ConclusionsConclusions
NDS9Dept. of Electronic Systems
Aalborg UniversityDenmark
Neural Networks
Figure: Artificial Neural network
I Same features as beforeI Comparison purposesI Better data fitting
10
STLF
A. Ceocea,M.S. Kemal,R. Doumerc
IntroductionSmart Grid Networks
Short Term LoadForecasting
Data Collection
Learning AlgorithmsLinear Regression
Neural Networks
Results7 Linear Regression
Neural Networks
Linear Regression vsNeural Networks
ConclusionsConclusions
NDS9Dept. of Electronic Systems
Aalborg UniversityDenmark
Linear Regression - 4 features vs 6 features
Figure: 24 Hour prediction using Linear RegressionMAPE4ft = 8.060MAPE6ft = 8.473
10
STLF
A. Ceocea,M.S. Kemal,R. Doumerc
IntroductionSmart Grid Networks
Short Term LoadForecasting
Data Collection
Learning AlgorithmsLinear Regression
Neural Networks
ResultsLinear Regression
8 Neural Networks
Linear Regression vsNeural Networks
ConclusionsConclusions
NDS9Dept. of Electronic Systems
Aalborg UniversityDenmark
Results Neural Networks
Figure: 24 Hour prediction using Neural Networks - 6 featuresMAPE = 5.060
10
STLF
A. Ceocea,M.S. Kemal,R. Doumerc
IntroductionSmart Grid Networks
Short Term LoadForecasting
Data Collection
Learning AlgorithmsLinear Regression
Neural Networks
ResultsLinear Regression
Neural Networks
9 Linear Regression vsNeural Networks
ConclusionsConclusions
NDS9Dept. of Electronic Systems
Aalborg UniversityDenmark
LR vs NN - 6 features
Figure: Linear Regression vs Neural NetworksMAPELR = 8.473MAPENN = 5.060
10
STLF
A. Ceocea,M.S. Kemal,R. Doumerc
IntroductionSmart Grid Networks
Short Term LoadForecasting
Data Collection
Learning AlgorithmsLinear Regression
Neural Networks
ResultsLinear Regression
Neural Networks
Linear Regression vsNeural Networks
Conclusions10 Conclusions
NDS9Dept. of Electronic Systems
Aalborg UniversityDenmark
Conclusions
Linear Regression
I More features = better training data fittingI Validation data fitting might not be optimal because of the
non linearity of the system
Neural Networks
I Better adapted to non-linear systemsI Better overall results based on our implementation
10
STLF
A. Ceocea,M.S. Kemal,R. Doumerc
IntroductionSmart Grid Networks
Short Term LoadForecasting
Data Collection
Learning AlgorithmsLinear Regression
Neural Networks
ResultsLinear Regression
Neural Networks
Linear Regression vsNeural Networks
Conclusions10 Conclusions
NDS9Dept. of Electronic Systems
Aalborg UniversityDenmark
Conclusions
Linear RegressionI More features = better training data fittingI Validation data fitting might not be optimal because of the
non linearity of the system
Neural NetworksI Better adapted to non-linear systemsI Better overall results based on our implementation
Thank you !
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