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https://doi.org/10.1016/S0925-2312(98)00074-5
Title: | Evolving artificial neural networks for short term load forecasting | Authors: | Srinivasan, D. | Keywords: | Artificial neural networks Electric load forecasting Genetic algorithm Hybrid AI techniques Optimum network structure |
Issue Date: | 7-Dec-1998 | Citation: | Srinivasan, D. (1998-12-07). Evolving artificial neural networks for short term load forecasting. Neurocomputing 23 (1-3) : 265-276. ScholarBank@NUS Repository. https://doi.org/10.1016/S0925-2312(98)00074-5 | Abstract: | This paper presents artificial neural networks (ANN) evolved by a genetic algorithm for short-term load forecasting. Using real load values and forecast weather data these ANN have been tested for electric load forecasting on weekdays and weekends. For each day type, the best-evolved artificial neural network was found capable of accurately forecasting one- day ahead hourly loads. The forecasting results obtained using these best- evolved networks were observed to be consistently superior compared to a commonly used statistical method. | Source Title: | Neurocomputing | URI: | http://scholarbank.nus.edu.sg/handle/10635/68234 | ISSN: | 09252312 | DOI: | 10.1016/S0925-2312(98)00074-5 |
Appears in Collections: | Staff Publications |
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