NDLI: A Novel Approach of Input Variable Selection for ANN Based Load Forecasting

Content Provider	IEEE Xplore Digital Library
Author	Shrivastava, V. Misra, R.B.
Copyright Year	2008
Description	Author affiliation: Reliability Eng. Centre, Indian Inst. of Technol. Kharagpur, Kharagpur (Shrivastava, V.; Misra, R.B.)
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. A case study is performed using the proposed method of peak load data of the Grid Corporation of Orrissa (GRIDCO), India which maintain high quality, reliable, historical data.
Starting Page	1
Ending Page	5
File Size	1773837
Page Count	5
File Format	PDF
ISBN	9781424417636
DOI	10.1109/ICPST.2008.4745348
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-12
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Input variables Load forecasting Artificial neural networks Power system planning Power system modeling Electrical engineering Electricity supply industry Power supplies Load modeling Predictive models Neural Network Back Propagation Algorithm Load Forecasting Multi-layered Neural Model
Content Type	Text
Resource Type	Article

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