Short-term electric power load forecasting based on cosine radial basis function neural networks: An experimental evaluation
| Autor: | Heidar A. Malki, Nicolaos B. Karayiannis, Mahesh Balasubramanian |
|---|---|
| Rok vydání: | 2005 |
| Předmět: |
Artificial neural network
Computer science business.industry Load forecasting Theoretical Computer Science Term (time) Human-Computer Interaction Artificial Intelligence Trigonometric functions Feedforward neural network Radial basis function Electric power Artificial intelligence business Software Test data |
| Zdroj: | International Journal of Intelligent Systems. 20:591-605 |
| ISSN: | 1098-111X 0884-8173 |
| DOI: | 10.1002/int.20084 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|