Publication:
Support vector machine for day ahead electricity price forecasting

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dc.citedby2
dc.contributor.authorRazak I.A.B.W.A.en_US
dc.contributor.authorAbidin I.B.Z.en_US
dc.contributor.authorSiah Y.K.en_US
dc.contributor.authorRahman T.K.B.A.en_US
dc.contributor.authorLada M.Y.en_US
dc.contributor.authorRamani A.N.B.en_US
dc.contributor.authorNasir M.N.M.en_US
dc.contributor.authorAhmad A.B.en_US
dc.contributor.authorid56602467500en_US
dc.contributor.authorid35606640500en_US
dc.contributor.authorid24448864400en_US
dc.contributor.authorid8922419700en_US
dc.contributor.authorid36559518300en_US
dc.contributor.authorid36988193800en_US
dc.contributor.authorid55658799800en_US
dc.contributor.authorid55336187300en_US
dc.date.accessioned2023-05-29T06:00:19Z
dc.date.available2023-05-29T06:00:19Z
dc.date.issued2015
dc.description.abstractElectricity 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.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo90021
dc.identifier.doi10.1063/1.4915865
dc.identifier.scopus2-s2.0-85006192493
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85006192493&doi=10.1063%2f1.4915865&partnerID=40&md5=1e054ef9bb03868d24ece1ed896f14b0
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22340
dc.identifier.volume1660
dc.publisherAmerican Institute of Physics Inc.en_US
dc.sourceScopus
dc.sourcetitleAIP Conference Proceedings
dc.titleSupport vector machine for day ahead electricity price forecastingen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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