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Short‐term electric power load forecasting based on cosine radial basis function neural networks: An experimental evaluation
Author(s) -
Karayiannis Nicolaos B.,
Balasubramanian Mahesh,
Malki Heidar A.
Publication year - 2005
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.20084
Subject(s) - artificial neural network , radial basis function , computer science , term (time) , feedforward neural network , trigonometric functions , activation function , electric power system , artificial intelligence , power (physics) , function (biology) , electric power , machine learning , mathematics , physics , geometry , quantum mechanics , evolutionary biology , biology
This article presents the results of a study aimed at the development of a system for short‐term electric power load forecasting. This was attempted by training feedforward neural networks (FFNNs) and cosine radial basis function (RBF) neural networks to predict future power demand based on past power load data and weather conditions. This study indicates that both neural network models exhibit comparable performance when tested on the training data but cosine RBF neural networks generalize better since they outperform considerably FFNNs when tested on the testing data. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 591–605, 2005.