Publication:
Electricity Price Prediction with Support Vector Machine and Bacterial Foraging Optimization Algorithm for Day-Ahead model

Simple item page
dc.citedby1
dc.contributor.authorIntan Azmira W.A.R.en_US
dc.contributor.authorAhmad A.en_US
dc.contributor.authorAbidin I.Z.en_US
dc.contributor.authorYap K.S.en_US
dc.contributor.authorNasir M.N.M.en_US
dc.contributor.authorUpkli W.R.en_US
dc.contributor.authorid56602467500en_US
dc.contributor.authorid55336187300en_US
dc.contributor.authorid35606640500en_US
dc.contributor.authorid24448864400en_US
dc.contributor.authorid55658799800en_US
dc.contributor.authorid57217213893en_US
dc.date.accessioned2023-05-29T08:07:28Z
dc.date.available2023-05-29T08:07:28Z
dc.date.issued2020
dc.descriptionElectric machine theory; Forecasting; Optimization; Support vector machines; Bacterial Foraging Optimization Algorithm (BFOA); Bacterial foraging optimization algorithms; Day-ahead price forecasts; Forecasting accuracy; Least square support vector machines; Multi-stage optimization; Optimization levels; Uncertain condition; Power marketsen_US
dc.description.abstractPredicting the price of electricity is an important aspect in the operation and planning of power systems. However, predicting the price of electricity is a relatively challenging task as it faces very uncertain conditions. Hence, this study proposes a hybrid Least Square Support Vector Machine (LSSVM) and Bacterial Foraging optimization Algorithm (BFOA) for day-ahead electricity price forecast. The main contribution of this work is the multistage optimization approach of LSSVM-BFOA that can improve the forecasting accuracy and efficiency. This is achieved by optimizing the input features and parameters of LSSVM at the same time. The input features have been reduced by six optimization levels in order to avoid losing any significant input. At the same time, the average MAPE is observed and the second stage of optimization is carried out. These processes are performed until there is no improvement in MAPE is observed. This model is examined in the Ontario power market. The LSSVM-BFOA model developed showed higher prediction accuracy with less complex model structure than most existing models. The day ahead price forecast is beneficial for both power generators and consumers in bidding for electricity prices. � 2020 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo9383184
dc.identifier.doi10.1109/SCOReD50371.2020.9383184
dc.identifier.epage164
dc.identifier.scopus2-s2.0-85103496018
dc.identifier.spage159
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85103496018&doi=10.1109%2fSCOReD50371.2020.9383184&partnerID=40&md5=483e6dbcd72ea9be03f3a606a5b60898
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25230
dc.identifier.volume2020-January
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2020 IEEE Student Conference on Research and Development, SCOReD 2020
dc.titleElectricity Price Prediction with Support Vector Machine and Bacterial Foraging Optimization Algorithm for Day-Ahead modelen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
Files
Collections
SCOPUS