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Support vector machine for day ahead electricity price forecasting

Electricity price forecasting has become an important part of power system operation and planning. In a pool- based electric energy market, producers submit selling bids consisting in energy blocks and their corresponding minimum selling prices to the market operator. Meanwhile, consumers submit buy...

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Main Authors: Razak I.A.B.W.A., Abidin I.B.Z., Siah Y.K., Rahman T.K.B.A., Lada M.Y., Ramani A.N.B., Nasir M.N.M., Ahmad A.B.
Other Authors: 56602467500
Format: Conference Paper
Published: American Institute of Physics Inc. 2023
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spelling my.uniten.dspace-223402023-05-29T14:00:19Z Support vector machine for day ahead electricity price forecasting Razak I.A.B.W.A. Abidin I.B.Z. Siah Y.K. Rahman T.K.B.A. Lada M.Y. Ramani A.N.B. Nasir M.N.M. Ahmad A.B. 56602467500 35606640500 24448864400 8922419700 36559518300 36988193800 55658799800 55336187300 Electricity price forecasting has become an important part of power system operation and planning. In a pool- based electric energy market, producers submit selling bids consisting in energy blocks and their corresponding minimum selling prices to the market operator. Meanwhile, consumers submit buying bids consisting in energy blocks and their corresponding maximum buying prices to the market operator. Hence, both producers and consumers use day ahead price forecasts to derive their respective bidding strategies to the electricity market yet reduce the cost of electricity. However, forecasting electricity prices is a complex task because price series is a non-stationary and highly volatile series. Many factors cause for price spikes such as volatility in load and fuel price as well as power import to and export from outside the market through long term contract. This paper introduces an approach of machine learning algorithm for day ahead electricity price forecasting with Least Square Support Vector Machine (LS-SVM). Previous day data of Hourly Ontario Electricity Price (HOEP), generation's price and demand from Ontario power market are used as the inputs for training data. The simulation is held using LSSVMlab in Matlab with the training and testing data of 2004. SVM that widely used for classification and regression has great generalization ability with structured risk minimization principle rather than empirical risk minimization. Moreover, same parameter settings in trained SVM give same results that absolutely reduce simulation process compared to other techniques such as neural network and time series. The mean absolute percentage error (MAPE) for the proposed model shows that SVM performs well compared to neural network. � 2015 AIP Publishing LLC. Final 2023-05-29T06:00:19Z 2023-05-29T06:00:19Z 2015 Conference Paper 10.1063/1.4915865 2-s2.0-85006192493 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85006192493&doi=10.1063%2f1.4915865&partnerID=40&md5=1e054ef9bb03868d24ece1ed896f14b0 https://irepository.uniten.edu.my/handle/123456789/22340 1660 90021 American Institute of Physics Inc. Scopus
institution Universiti Tenaga Nasional
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description Electricity price forecasting has become an important part of power system operation and planning. In a pool- based electric energy market, producers submit selling bids consisting in energy blocks and their corresponding minimum selling prices to the market operator. Meanwhile, consumers submit buying bids consisting in energy blocks and their corresponding maximum buying prices to the market operator. Hence, both producers and consumers use day ahead price forecasts to derive their respective bidding strategies to the electricity market yet reduce the cost of electricity. However, forecasting electricity prices is a complex task because price series is a non-stationary and highly volatile series. Many factors cause for price spikes such as volatility in load and fuel price as well as power import to and export from outside the market through long term contract. This paper introduces an approach of machine learning algorithm for day ahead electricity price forecasting with Least Square Support Vector Machine (LS-SVM). Previous day data of Hourly Ontario Electricity Price (HOEP), generation's price and demand from Ontario power market are used as the inputs for training data. The simulation is held using LSSVMlab in Matlab with the training and testing data of 2004. SVM that widely used for classification and regression has great generalization ability with structured risk minimization principle rather than empirical risk minimization. Moreover, same parameter settings in trained SVM give same results that absolutely reduce simulation process compared to other techniques such as neural network and time series. The mean absolute percentage error (MAPE) for the proposed model shows that SVM performs well compared to neural network. � 2015 AIP Publishing LLC.
author2 56602467500
author_facet 56602467500
Razak I.A.B.W.A.
Abidin I.B.Z.
Siah Y.K.
Rahman T.K.B.A.
Lada M.Y.
Ramani A.N.B.
Nasir M.N.M.
Ahmad A.B.
format Conference Paper
author Razak I.A.B.W.A.
Abidin I.B.Z.
Siah Y.K.
Rahman T.K.B.A.
Lada M.Y.
Ramani A.N.B.
Nasir M.N.M.
Ahmad A.B.
spellingShingle Razak I.A.B.W.A.
Abidin I.B.Z.
Siah Y.K.
Rahman T.K.B.A.
Lada M.Y.
Ramani A.N.B.
Nasir M.N.M.
Ahmad A.B.
Support vector machine for day ahead electricity price forecasting
author_sort Razak I.A.B.W.A.
title Support vector machine for day ahead electricity price forecasting
title_short Support vector machine for day ahead electricity price forecasting
title_full Support vector machine for day ahead electricity price forecasting
title_fullStr Support vector machine for day ahead electricity price forecasting
title_full_unstemmed Support vector machine for day ahead electricity price forecasting
title_sort support vector machine for day ahead electricity price forecasting
publisher American Institute of Physics Inc.
publishDate 2023
_version_ 1806427343132033024
score 13.222552

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