An assessment of electrical load forecasting using artificial neural network

Shrivastava, V., Misra, R. B. and Bansal, R. C. (2012) An assessment of electrical load forecasting using artificial neural network. International Journal of Computer Aided Engineering and Technology, 4 1: 80-89. doi:10.1504/IJCAET.2012.044584

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Author Shrivastava, V.
Misra, R. B.
Bansal, R. C.
Title An assessment of electrical load forecasting using artificial neural network
Journal name International Journal of Computer Aided Engineering and Technology   Check publisher's open access policy
ISSN 1757-2657
Publication date 2012
Sub-type Article (original research)
DOI 10.1504/IJCAET.2012.044584
Volume 4
Issue 1
Start page 80
End page 89
Total pages 10
Place of publication Geneva, Switzerland
Publisher Inderscience Publishers
Collection year 2013
Language eng
Abstract The forecasting of electricity demand has become one of the major research fields in electrical engineering. The supply industry requires forecasts with lead times, which range from the short term (a few minutes, hours, or days ahead) to the long term (up to 20 years ahead). The major priority for an electrical power utility is to provide uninterrupted power supply to its customers. Long term peak load forecasting plays an important role in electrical power systems in terms of policy planning and budget allocation. This paper presents a peak load forecasting model using artificial neural networks (ANN). The approach in the paper is based on multi-layered back-propagation feed forward neural network. For annual forecasts, there should be 10 to 12 years of historical monthly data available for each electrical system or electrical buss. A case study is performed by using the proposed method of peak load data of a state electricity board of India which maintain high quality, reliable, historical data providing the best possible results. Model's quality is directly dependent upon data integrity.
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2013 Collection
School of Information Technology and Electrical Engineering Publications
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Created: Thu, 22 Mar 2012, 22:21:42 EST by Ms Deborah Brian on behalf of School of Information Technol and Elec Engineering