Please use this identifier to cite or link to this item:
|Title:||Evolving artificial neural networks for short term load forecasting||Authors:||Srinivasan, D.||Keywords:||Artificial neural networks
Electric load forecasting
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|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 6, 2019
WEB OF SCIENCETM
checked on Nov 28, 2019
checked on Dec 1, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.