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
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.

SCOPUSTM   
Citations

48
checked on Oct 12, 2018

WEB OF SCIENCETM
Citations

36
checked on Oct 3, 2018

Page view(s)

43
checked on Jun 1, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.