NDLI: An improved neural network prediction model for load demand in day-ahead electricity market

Content Provider	IEEE Xplore Digital Library
Author	Bo Yang Yuanzhang Sun
Copyright Year	2008
Description	Author affiliation: Central China Grid Co. Ltd., Wuhan (Bo Yang)
Abstract	Load demand prediction is vital for maintaining stability and controlling risks of electricity market. An improved model which combines neural network with genetic algorithm is proposed to accurately predict load demand at equilibrium situation of day-ahead electricity market. In the proposed model, load demand prediction problem is converted into optimization problem of error minimization between the actual output and the desired output. Optimal topology and initial weights of neural network are obtained by using hybrid genetic operation of selection, crossover and mutation. Next, gradient learning algorithm with momentum rate is used to train neural network and optimal connection weights are obtained. The proposed model is tested on load demand prediction in California electricity market. The test results show that the proposed model can effectively approximate input/output mapping of training samples and can obtain more accurate load demand prediction values than BP neural network.
Starting Page	4425
Ending Page	4429
File Size	129546
Page Count	5
File Format	PDF
ISBN	9781424421138
DOI	10.1109/WCICA.2008.4593635
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Predictive models Load modeling Power markets Training Topology Network topology Electricity market Neural network Market clearing price Genetic algorithm Electric power system
Content Type	Text
Resource Type	Article

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