Please use this identifier to cite or link to this item: 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
Source: 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
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