A State-of-the-Art Review of Artificial Intelligence Techniques for Short-Term Electric Load Forecasting

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dc.authoridteke, ahmet/0000-0003-2610-9576
dc.contributor.authorZor, Kasim
dc.contributor.authorTimur, Oguzhan
dc.contributor.authorTeke, Ahmet
dc.date.accessioned2025-01-06T17:37:35Z
dc.date.available2025-01-06T17:37:35Z
dc.date.issued2017
dc.description6th International Youth Conference on Energy (IYCE) -- JUN 21-24, 2017 -- Budapest, HUNGARY
dc.description.abstractAccording to privatization and deregulation of power system, accurate electric load forecasting has come into prominence recently. The new energy market and the smart grid paradigm ask for both better demand side management policies and for more reliable forecasts from single end-users, up to system scale. However, it is complex to predict the electric demand owing to the influencing factors such as climate factors, social activities, and seasonal factors. The methods developed for load forecasting are broadly analyzed in two categories, namely analytical techniques and artificial intelligence techniques. In the literature, commonly used analytical methods are linear regression method, Box-Jenkins method, and nonparametric regression method. The analytical methods work well under normal daily circumstances, but they can't give contenting results while dealing with meteorological, sociological or economical changes, hence they are not updated depending on time. Therefore, artificial intelligence techniques have gained importance in reducing estimation errors. Artificial neural network, support vector machine, and adaptive neuro-fuzzy inference system are among these artificial intelligence techniques. In this paper, a state-of-the-art review of three artificial intelligence techniques for short-term electric load forecasting is comprehensively presented.
dc.description.sponsorshipScientific Research Project Unit of Cukurova University [FBA-2017-8252]
dc.description.sponsorshipThe authors would like to acknowledge the Scientific Research Project Unit of Cukurova University owing to research and financial support for the project named as Electric Demand Prediction: Data Acquisition, Implementation of ANN, and User Interface Design and numbered as FBA-2017-8252.
dc.identifier.isbn978-1-5090-6409-0
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2287
dc.identifier.wosWOS:000425930300045
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherIEEE
dc.relation.ispartof2017 6th International Youth Conference on Energy (Iyce)
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectelectric load forecasting
dc.subjectshort term
dc.subjectartificial intelligence
dc.subjectreview
dc.titleA State-of-the-Art Review of Artificial Intelligence Techniques for Short-Term Electric Load Forecasting
dc.typeConference Object

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