west denmark short term load forecast_for smart grids

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 !