NDLI: Short-Term Electric Load Forecasting Based on SAPSO-ANN Algorithm

Content Provider	IEEE Xplore Digital Library
Author	Xiang Li Shang-Dong Yang Jian-Xun Qi Shu-Xia Yang
Copyright Year	2006
Description	Author affiliation: Sch. of Bus. Adm., North China Electr. Power Univ., Beijing (Xiang Li; Shang-Dong Yang; Jian-Xun Qi; Shu-Xia Yang)
Abstract	For the economical, secure and stable operation of the electric power system, the short-term load forecasting plays a vital role. The paper applies the SAPSO-ANN model to forecast the short-term electric load. In order to enhance the generality of the model, as well as the capabilities of training and learning in the forecasting, the capability of searching the optimum in the overall situations in the PSO algorithm has been strengthened by using the SA algorithm which has the characteristic of global optimization, and the learning algorithm of a typical three-layer feed-forward neural network BP has been replaced by the mixed PSO algorithm. Taking actual load data of a power grid company in South China as a sample, the PSO-ANN model has been compared to the traditional model. The results show that this model has better capability of forecasting and network learning
Sponsorship	IEEE Syst., Man and Cybernetics Hebei Univ.
Starting Page	2882
Ending Page	2885
File Size	239256
Page Count	4
File Format	PDF
ISBN	1424400619
DOI	10.1109/ICMLC.2006.259074
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-13
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Load forecasting Economic forecasting Predictive models Power system modeling Power system economics Power generation economics Feedforward systems Neural networks Feedforward neural networks Power grids Simulation annealing algorithm Artificial neural networks Particle Swarm Optimization (PSO) algorithm Short-term electric load forecasting
Content Type	Text
Resource Type	Article

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