Title: An assessment of electrical load forecasting using artificial neural network

Authors: V. Shrivastava; R.B. Misra; R.C. Bansal

Addresses: Reliability Engineering Centre, Indian Institute of Technology, Kharagpur-721302, India. ' Reliability Engineering Centre, Indian Institute of Technology, Kharagpur-721302, India. ' School of Information Technology and Electrical Engineering, The University of Queensland, St. Lucia Campus, Qld 4072, Australia

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.

Keywords: load forecasting; artificial neural networks; ANNs; multi-layered neural models; back propagation algorithm; electricity demand.

DOI: 10.1504/IJCAET.2012.044584

International Journal of Computer Aided Engineering and Technology, 2012 Vol.4 No.1, pp.80 - 89

Published online: 16 Aug 2014 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article