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West denmark short term load forecast_for smart grids

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Machine Learning Short-term Load Forecasting in the Electrical Grid Alexandru Ceocea aceoce12@student.aau.dk Mohammed Seifu Kemal mkemal11@student.aau.dk Robin Doumerc rdoume12@student.aau.dk NDS9 Department of Electronic Systems Aalborg University Denmark
Agenda STLF A. Ceocea, M.S. Kemal, R. Doumerc Introduction Smart Grid Networks Short Term Load Forecasting Data Collection Introduction Smart Grid Networks Short Term Load Forecasting Data Collection Learning Algorithms Learning Algorithms Linear Regression Neural Networks Linear Regression Neural Networks Results Linear Regression Neural Networks Linear Regression vs Neural Networks Results Linear Regression Neural Networks Linear Regression vs Neural Networks Conclusions Conclusions Conclusions Conclusions 10 NDS9 Dept. of Electronic Systems Aalborg University Denmark
Smart Grid Networks STLF A. Ceocea, M.S. Kemal, R. Doumerc Introduction 2 What is a Smart Grid ? Smart Grid Networks Short Term Load Forecasting Modernized electrical grid that makes use of information and communication technology in order to gather and react on information such as the behavior of suppliers and consumers in an automated centralized way Data Collection Learning Algorithms Linear Regression Neural Networks Results Linear Regression Neural Networks Linear Regression vs Neural Networks Conclusions Conclusions 10 NDS9 Dept. of Electronic Systems Aalborg University Denmark
Smart Grid Networks STLF A. Ceocea, M.S. Kemal, R. Doumerc Introduction 2 What is a Smart Grid ? Smart Grid Networks Short Term Load Forecasting Modernized electrical grid that makes use of information and communication technology in order to gather and react on information such as the behavior of suppliers and consumers in an automated centralized way Data Collection Learning Algorithms Linear Regression Neural Networks Results Linear Regression Neural Networks Why Smart Grids ? Linear Regression vs Neural Networks To improve the efficiency, reliability and sustainability of the production and distribution of electricity within the Grid. Conclusions Conclusions 10 NDS9 Dept. of Electronic Systems Aalborg University Denmark
Short Term Load Forecasting STLF A. Ceocea, M.S. Kemal, R. Doumerc Load Forecasting Introduction Smart Grid Networks 3 Short Term Load Forecasting Data Collection Learning Algorithms Linear Regression Neural Networks Results Linear Regression Neural Networks Linear Regression vs Neural Networks Conclusions Conclusions 10 NDS9 Dept. of Electronic Systems Aalborg University Denmark
Short Term Load Forecasting STLF A. Ceocea, M.S. Kemal, R. Doumerc Load Forecasting Introduction Smart Grid Networks Vitally important for the electric industry 3 Short Term Load Forecasting Data Collection Balance supply and demand Learning Algorithms Infrastructure development Linear Regression Neural Networks Results Linear Regression Neural Networks Linear Regression vs Neural Networks Conclusions Conclusions 10 NDS9 Dept. of Electronic Systems Aalborg University Denmark
Short Term Load Forecasting STLF A. Ceocea, M.S. Kemal, R. Doumerc Load Forecasting Introduction Smart Grid Networks Vitally important for the electric industry 3 Short Term Load Forecasting Data Collection Balance supply and demand Learning Algorithms Infrastructure development Linear Regression Neural Networks Results Linear Regression Short term Load Forecasting Neural Networks Linear Regression vs Neural Networks Conclusions Conclusions 10 NDS9 Dept. of Electronic Systems Aalborg University Denmark
Short Term Load Forecasting STLF A. Ceocea, M.S. Kemal, R. Doumerc Load Forecasting Introduction Smart Grid Networks Vitally important for the electric industry 3 Short Term Load Forecasting Data Collection Balance supply and demand Learning Algorithms Infrastructure development Linear Regression Neural Networks Results Linear Regression Short term Load Forecasting Neural Networks Linear Regression vs Neural Networks From 1 hour to 1 week Conclusions Generation of short term scheduling functions Conclusions Assessing the security of the power system Dispatcher information 10 NDS9 Dept. of Electronic Systems Aalborg University Denmark
Collected Data STLF A. Ceocea, M.S. Kemal, R. Doumerc Introduction Smart Grid Networks Training data is composed of energy consumption measured over the course of one year (2011), in West Denmark and is provided by Energinet. Short Term Load Forecasting 4 Data Collection Learning Algorithms Linear Regression Neural Networks Date Results Energy consumption (MWh) Linear Regression Hourly update Linear Regression vs Neural Networks Neural Networks Conclusions Time frame = 1 year Conclusions 10 NDS9 Dept. of Electronic Systems Aalborg University Denmark
Linear Regression STLF A. Ceocea, M.S. Kemal, R. Doumerc For the forecasting of electric load consumption, regression is used to model the relationship between the load and similar characteristics from a previous time frame. Introduction Smart Grid Networks Short Term Load Forecasting Data Collection Learning Algorithms 5 Linear Regression Neural Networks Results Linear Regression Neural Networks Linear Regression vs Neural Networks Conclusions Conclusions 10 NDS9 Dept. of Electronic Systems Aalborg University Denmark
Linear Regression STLF A. Ceocea, M.S. Kemal, R. Doumerc For the forecasting of electric load consumption, regression is used to model the relationship between the load and similar characteristics from a previous time frame. Introduction Smart Grid Networks Short Term Load Forecasting Data Collection Learning Algorithms Regression formula used: hθ (x) = θT x = 5 n Linear Regression Neural Networks θi xi Results i=1 Linear Regression Neural Networks Linear Regression vs Neural Networks Conclusions Conclusions 10 NDS9 Dept. of Electronic Systems Aalborg University Denmark
Linear Regression STLF A. Ceocea, M.S. Kemal, R. Doumerc For the forecasting of electric load consumption, regression is used to model the relationship between the load and similar characteristics from a previous time frame. Introduction Smart Grid Networks Short Term Load Forecasting Data Collection Learning Algorithms Regression formula used: hθ (x) = θT x = 5 n Linear Regression Neural Networks θi xi Results i=1 Linear Regression Neural Networks x1 - Day of the week Linear Regression vs Neural Networks Conclusions Conclusions 10 NDS9 Dept. of Electronic Systems Aalborg University Denmark
Linear Regression STLF A. Ceocea, M.S. Kemal, R. Doumerc For the forecasting of electric load consumption, regression is used to model the relationship between the load and similar characteristics from a previous time frame. Introduction Smart Grid Networks Short Term Load Forecasting Data Collection Learning Algorithms Regression formula used: hθ (x) = θT x = 5 n Linear Regression Neural Networks θi xi Results i=1 Linear Regression Neural Networks x1 - Day of the week Linear Regression vs Neural Networks x2 - Day of the month Conclusions Conclusions 10 NDS9 Dept. of Electronic Systems Aalborg University Denmark
Linear Regression STLF A. Ceocea, M.S. Kemal, R. Doumerc For the forecasting of electric load consumption, regression is used to model the relationship between the load and similar characteristics from a previous time frame. Introduction Smart Grid Networks Short Term Load Forecasting Data Collection Learning Algorithms Regression formula used: hθ (x) = θT x = 5 n Linear Regression Neural Networks θi xi Results i=1 Linear Regression Neural Networks x1 - Day of the week Linear Regression vs Neural Networks x2 - Day of the month Conclusions Conclusions x3 - Average previous load (24h) 10 NDS9 Dept. of Electronic Systems Aalborg University Denmark
Linear Regression STLF A. Ceocea, M.S. Kemal, R. Doumerc For the forecasting of electric load consumption, regression is used to model the relationship between the load and similar characteristics from a previous time frame. Introduction Smart Grid Networks Short Term Load Forecasting Data Collection Learning Algorithms Regression formula used: hθ (x) = θT x = 5 n Linear Regression Neural Networks θi xi Results i=1 Linear Regression Neural Networks x1 - Day of the week Linear Regression vs Neural Networks x2 - Day of the month Conclusions Conclusions x3 - Average previous load (24h) x4 - Load of same time frame (1h) on previous day 10 NDS9 Dept. of Electronic Systems Aalborg University Denmark
Linear Regression STLF A. Ceocea, M.S. Kemal, R. Doumerc For the forecasting of electric load consumption, regression is used to model the relationship between the load and similar characteristics from a previous time frame. Introduction Smart Grid Networks Short Term Load Forecasting Data Collection Learning Algorithms Regression formula used: hθ (x) = θT x = 5 n Linear Regression Neural Networks θi xi Results i=1 Linear Regression Neural Networks x1 - Day of the week Linear Regression vs Neural Networks x2 - Day of the month Conclusions Conclusions x3 - Average previous load (24h) x4 - Load of same time frame (1h) on previous day x5 - Load at same time, same day, previous week x6 - Month 10 NDS9 Dept. of Electronic Systems Aalborg University Denmark
Neural Networks STLF A. Ceocea, M.S. Kemal, R. Doumerc Introduction Smart Grid Networks Short Term Load Forecasting Data Collection Learning Algorithms Linear Regression 6 Neural Networks Results Linear Regression Neural Networks Linear Regression vs Neural Networks Conclusions Figure: Artificial Neural network Conclusions Same features as before Comparison purposes Better data fitting 10 NDS9 Dept. of Electronic Systems Aalborg University Denmark
Linear Regression - 4 features vs 6 features STLF A. Ceocea, M.S. Kemal, R. Doumerc Introduction Smart Grid Networks Short Term Load Forecasting Data Collection Learning Algorithms Linear Regression Neural Networks Results 7 Linear Regression Neural Networks Linear Regression vs Neural Networks Conclusions Conclusions Figure: 24 Hour prediction using Linear Regression MAPE4ft = 8.060 MAPE6ft = 8.473 10 NDS9 Dept. of Electronic Systems Aalborg University Denmark
Results Neural Networks STLF A. Ceocea, M.S. Kemal, R. Doumerc Introduction Smart Grid Networks Short Term Load Forecasting Data Collection Learning Algorithms Linear Regression Neural Networks Results Linear Regression 8 Neural Networks Linear Regression vs Neural Networks Conclusions Conclusions Figure: 24 Hour prediction using Neural Networks - 6 features MAPE = 5.060 10 NDS9 Dept. of Electronic Systems Aalborg University Denmark
LR vs NN - 6 features STLF A. Ceocea, M.S. Kemal, R. Doumerc Introduction Smart Grid Networks Short Term Load Forecasting Data Collection Learning Algorithms Linear Regression Neural Networks Results Linear Regression Neural Networks 9 Linear Regression vs Neural Networks Conclusions Conclusions Figure: Linear Regression vs Neural Networks MAPELR = 8.473 MAPENN = 5.060 10 NDS9 Dept. of Electronic Systems Aalborg University Denmark
Conclusions STLF A. Ceocea, M.S. Kemal, R. Doumerc Introduction Linear Regression Smart Grid Networks Short Term Load Forecasting Data Collection Learning Algorithms Linear Regression Neural Networks Results Linear Regression Neural Networks Neural Networks Linear Regression vs Neural Networks Conclusions 10 10 Conclusions NDS9 Dept. of Electronic Systems Aalborg University Denmark
Conclusions STLF A. Ceocea, M.S. Kemal, R. Doumerc Introduction Linear Regression Smart Grid Networks Short Term Load Forecasting More features = better training data fitting Data Collection Learning Algorithms Validation data fitting might not be optimal because of the non linearity of the system Linear Regression Neural Networks Results Linear Regression Neural Networks Neural Networks Better adapted to non-linear systems Linear Regression vs Neural Networks Conclusions 10 Conclusions Better overall results based on our implementation 10 NDS9 Dept. of Electronic Systems Aalborg University Denmark
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West denmark short term load forecast_for smart grids

  • 1. Machine Learning Short-term Load Forecasting in the Electrical Grid Alexandru Ceocea aceoce12@student.aau.dk Mohammed Seifu Kemal mkemal11@student.aau.dk Robin Doumerc rdoume12@student.aau.dk NDS9 Department of Electronic Systems Aalborg University Denmark
  • 2. Agenda STLF A. Ceocea, M.S. Kemal, R. Doumerc Introduction Smart Grid Networks Short Term Load Forecasting Data Collection Introduction Smart Grid Networks Short Term Load Forecasting Data Collection Learning Algorithms Learning Algorithms Linear Regression Neural Networks Linear Regression Neural Networks Results Linear Regression Neural Networks Linear Regression vs Neural Networks Results Linear Regression Neural Networks Linear Regression vs Neural Networks Conclusions Conclusions Conclusions Conclusions 10 NDS9 Dept. of Electronic Systems Aalborg University Denmark
  • 3. Smart Grid Networks STLF A. Ceocea, M.S. Kemal, R. Doumerc Introduction 2 What is a Smart Grid ? Smart Grid Networks Short Term Load Forecasting Modernized electrical grid that makes use of information and communication technology in order to gather and react on information such as the behavior of suppliers and consumers in an automated centralized way Data Collection Learning Algorithms Linear Regression Neural Networks Results Linear Regression Neural Networks Linear Regression vs Neural Networks Conclusions Conclusions 10 NDS9 Dept. of Electronic Systems Aalborg University Denmark
  • 4. Smart Grid Networks STLF A. Ceocea, M.S. Kemal, R. Doumerc Introduction 2 What is a Smart Grid ? Smart Grid Networks Short Term Load Forecasting Modernized electrical grid that makes use of information and communication technology in order to gather and react on information such as the behavior of suppliers and consumers in an automated centralized way Data Collection Learning Algorithms Linear Regression Neural Networks Results Linear Regression Neural Networks Why Smart Grids ? Linear Regression vs Neural Networks To improve the efficiency, reliability and sustainability of the production and distribution of electricity within the Grid. Conclusions Conclusions 10 NDS9 Dept. of Electronic Systems Aalborg University Denmark
  • 5. Short Term Load Forecasting STLF A. Ceocea, M.S. Kemal, R. Doumerc Load Forecasting Introduction Smart Grid Networks 3 Short Term Load Forecasting Data Collection Learning Algorithms Linear Regression Neural Networks Results Linear Regression Neural Networks Linear Regression vs Neural Networks Conclusions Conclusions 10 NDS9 Dept. of Electronic Systems Aalborg University Denmark
  • 6. Short Term Load Forecasting STLF A. Ceocea, M.S. Kemal, R. Doumerc Load Forecasting Introduction Smart Grid Networks Vitally important for the electric industry 3 Short Term Load Forecasting Data Collection Balance supply and demand Learning Algorithms Infrastructure development Linear Regression Neural Networks Results Linear Regression Neural Networks Linear Regression vs Neural Networks Conclusions Conclusions 10 NDS9 Dept. of Electronic Systems Aalborg University Denmark
  • 7. Short Term Load Forecasting STLF A. Ceocea, M.S. Kemal, R. Doumerc Load Forecasting Introduction Smart Grid Networks Vitally important for the electric industry 3 Short Term Load Forecasting Data Collection Balance supply and demand Learning Algorithms Infrastructure development Linear Regression Neural Networks Results Linear Regression Short term Load Forecasting Neural Networks Linear Regression vs Neural Networks Conclusions Conclusions 10 NDS9 Dept. of Electronic Systems Aalborg University Denmark
  • 8. Short Term Load Forecasting STLF A. Ceocea, M.S. Kemal, R. Doumerc Load Forecasting Introduction Smart Grid Networks Vitally important for the electric industry 3 Short Term Load Forecasting Data Collection Balance supply and demand Learning Algorithms Infrastructure development Linear Regression Neural Networks Results Linear Regression Short term Load Forecasting Neural Networks Linear Regression vs Neural Networks From 1 hour to 1 week Conclusions Generation of short term scheduling functions Conclusions Assessing the security of the power system Dispatcher information 10 NDS9 Dept. of Electronic Systems Aalborg University Denmark
  • 9. Collected Data STLF A. Ceocea, M.S. Kemal, R. Doumerc Introduction Smart Grid Networks Training data is composed of energy consumption measured over the course of one year (2011), in West Denmark and is provided by Energinet. Short Term Load Forecasting 4 Data Collection Learning Algorithms Linear Regression Neural Networks Date Results Energy consumption (MWh) Linear Regression Hourly update Linear Regression vs Neural Networks Neural Networks Conclusions Time frame = 1 year Conclusions 10 NDS9 Dept. of Electronic Systems Aalborg University Denmark
  • 10. Linear Regression STLF A. Ceocea, M.S. Kemal, R. Doumerc For the forecasting of electric load consumption, regression is used to model the relationship between the load and similar characteristics from a previous time frame. Introduction Smart Grid Networks Short Term Load Forecasting Data Collection Learning Algorithms 5 Linear Regression Neural Networks Results Linear Regression Neural Networks Linear Regression vs Neural Networks Conclusions Conclusions 10 NDS9 Dept. of Electronic Systems Aalborg University Denmark
  • 11. Linear Regression STLF A. Ceocea, M.S. Kemal, R. Doumerc For the forecasting of electric load consumption, regression is used to model the relationship between the load and similar characteristics from a previous time frame. Introduction Smart Grid Networks Short Term Load Forecasting Data Collection Learning Algorithms Regression formula used: hθ (x) = θT x = 5 n Linear Regression Neural Networks θi xi Results i=1 Linear Regression Neural Networks Linear Regression vs Neural Networks Conclusions Conclusions 10 NDS9 Dept. of Electronic Systems Aalborg University Denmark
  • 12. Linear Regression STLF A. Ceocea, M.S. Kemal, R. Doumerc For the forecasting of electric load consumption, regression is used to model the relationship between the load and similar characteristics from a previous time frame. Introduction Smart Grid Networks Short Term Load Forecasting Data Collection Learning Algorithms Regression formula used: hθ (x) = θT x = 5 n Linear Regression Neural Networks θi xi Results i=1 Linear Regression Neural Networks x1 - Day of the week Linear Regression vs Neural Networks Conclusions Conclusions 10 NDS9 Dept. of Electronic Systems Aalborg University Denmark
  • 13. Linear Regression STLF A. Ceocea, M.S. Kemal, R. Doumerc For the forecasting of electric load consumption, regression is used to model the relationship between the load and similar characteristics from a previous time frame. Introduction Smart Grid Networks Short Term Load Forecasting Data Collection Learning Algorithms Regression formula used: hθ (x) = θT x = 5 n Linear Regression Neural Networks θi xi Results i=1 Linear Regression Neural Networks x1 - Day of the week Linear Regression vs Neural Networks x2 - Day of the month Conclusions Conclusions 10 NDS9 Dept. of Electronic Systems Aalborg University Denmark
  • 14. Linear Regression STLF A. Ceocea, M.S. Kemal, R. Doumerc For the forecasting of electric load consumption, regression is used to model the relationship between the load and similar characteristics from a previous time frame. Introduction Smart Grid Networks Short Term Load Forecasting Data Collection Learning Algorithms Regression formula used: hθ (x) = θT x = 5 n Linear Regression Neural Networks θi xi Results i=1 Linear Regression Neural Networks x1 - Day of the week Linear Regression vs Neural Networks x2 - Day of the month Conclusions Conclusions x3 - Average previous load (24h) 10 NDS9 Dept. of Electronic Systems Aalborg University Denmark
  • 15. Linear Regression STLF A. Ceocea, M.S. Kemal, R. Doumerc For the forecasting of electric load consumption, regression is used to model the relationship between the load and similar characteristics from a previous time frame. Introduction Smart Grid Networks Short Term Load Forecasting Data Collection Learning Algorithms Regression formula used: hθ (x) = θT x = 5 n Linear Regression Neural Networks θi xi Results i=1 Linear Regression Neural Networks x1 - Day of the week Linear Regression vs Neural Networks x2 - Day of the month Conclusions Conclusions x3 - Average previous load (24h) x4 - Load of same time frame (1h) on previous day 10 NDS9 Dept. of Electronic Systems Aalborg University Denmark
  • 16. Linear Regression STLF A. Ceocea, M.S. Kemal, R. Doumerc For the forecasting of electric load consumption, regression is used to model the relationship between the load and similar characteristics from a previous time frame. Introduction Smart Grid Networks Short Term Load Forecasting Data Collection Learning Algorithms Regression formula used: hθ (x) = θT x = 5 n Linear Regression Neural Networks θi xi Results i=1 Linear Regression Neural Networks x1 - Day of the week Linear Regression vs Neural Networks x2 - Day of the month Conclusions Conclusions x3 - Average previous load (24h) x4 - Load of same time frame (1h) on previous day x5 - Load at same time, same day, previous week x6 - Month 10 NDS9 Dept. of Electronic Systems Aalborg University Denmark
  • 17. Neural Networks STLF A. Ceocea, M.S. Kemal, R. Doumerc Introduction Smart Grid Networks Short Term Load Forecasting Data Collection Learning Algorithms Linear Regression 6 Neural Networks Results Linear Regression Neural Networks Linear Regression vs Neural Networks Conclusions Figure: Artificial Neural network Conclusions Same features as before Comparison purposes Better data fitting 10 NDS9 Dept. of Electronic Systems Aalborg University Denmark
  • 18. Linear Regression - 4 features vs 6 features STLF A. Ceocea, M.S. Kemal, R. Doumerc Introduction Smart Grid Networks Short Term Load Forecasting Data Collection Learning Algorithms Linear Regression Neural Networks Results 7 Linear Regression Neural Networks Linear Regression vs Neural Networks Conclusions Conclusions Figure: 24 Hour prediction using Linear Regression MAPE4ft = 8.060 MAPE6ft = 8.473 10 NDS9 Dept. of Electronic Systems Aalborg University Denmark
  • 19. Results Neural Networks STLF A. Ceocea, M.S. Kemal, R. Doumerc Introduction Smart Grid Networks Short Term Load Forecasting Data Collection Learning Algorithms Linear Regression Neural Networks Results Linear Regression 8 Neural Networks Linear Regression vs Neural Networks Conclusions Conclusions Figure: 24 Hour prediction using Neural Networks - 6 features MAPE = 5.060 10 NDS9 Dept. of Electronic Systems Aalborg University Denmark
  • 20. LR vs NN - 6 features STLF A. Ceocea, M.S. Kemal, R. Doumerc Introduction Smart Grid Networks Short Term Load Forecasting Data Collection Learning Algorithms Linear Regression Neural Networks Results Linear Regression Neural Networks 9 Linear Regression vs Neural Networks Conclusions Conclusions Figure: Linear Regression vs Neural Networks MAPELR = 8.473 MAPENN = 5.060 10 NDS9 Dept. of Electronic Systems Aalborg University Denmark
  • 21. Conclusions STLF A. Ceocea, M.S. Kemal, R. Doumerc Introduction Linear Regression Smart Grid Networks Short Term Load Forecasting Data Collection Learning Algorithms Linear Regression Neural Networks Results Linear Regression Neural Networks Neural Networks Linear Regression vs Neural Networks Conclusions 10 10 Conclusions NDS9 Dept. of Electronic Systems Aalborg University Denmark
  • 22. Conclusions STLF A. Ceocea, M.S. Kemal, R. Doumerc Introduction Linear Regression Smart Grid Networks Short Term Load Forecasting More features = better training data fitting Data Collection Learning Algorithms Validation data fitting might not be optimal because of the non linearity of the system Linear Regression Neural Networks Results Linear Regression Neural Networks Neural Networks Better adapted to non-linear systems Linear Regression vs Neural Networks Conclusions 10 Conclusions Better overall results based on our implementation 10 NDS9 Dept. of Electronic Systems Aalborg University Denmark