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Daily peak electric load forecasting using an artificial neural network and an improvement method for reducing the forecasting errors
Author(s) -
Makino Kyoko,
Shimada Tsuyoshi,
Ichikawa Ryoichi,
Ono Masaya,
Endo Tsunekazu
Publication year - 1996
Publication title -
electrical engineering in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.136
H-Index - 28
eISSN - 1520-6416
pISSN - 0424-7760
DOI - 10.1002/eej.4391160503
Subject(s) - artificial neural network , backpropagation , computer science , electrical load , artificial intelligence , probabilistic neural network , feedforward neural network , bounded function , domain (mathematical analysis) , approximation error , machine learning , time delay neural network , algorithm , engineering , voltage , mathematics , mathematical analysis , electrical engineering
This paper proposes a forecasting method for shortterm peak electric loads using a 3‐layer neural network of locally active units. Each unit in the hidden layer of the neural network is activated only by input vectors in a bounded domain of vector space. This characteristic enables additional learning. Furthermore, it is supposed to provide the network structure with information that helps to improve forecasting accuracy. The neural network is applied to daily peak load forecasting simulations in summer. The results show that the proposed method is superior to a conventional neural network with the backpropagation algorithm. To make the best use of the neural network, an error‐oriented method of parameter modification is also examined.