A Short-Term Power Load Forecasting Method Based on BP Neural Network

Abstract:

Short-term power load forecasting is very important for the electric power market, and the forecasting method should have high accuracy and high speed. A three-layer BP neural network has the ability to approximate any N-dimensional continuous function with arbitrary precision. In this paper, a short-term power load forecasting method based on BP neural network is proposed. This method uses the three-layer neural network with single hidden layer as forecast model. In order to improve the training speed of BP neural network and the forecasting efficiency, this method firstly reduces the factors which affect load forecasting by using rough set theory, then takes the reduced data as input variables of the BP neural network model, and gets the forecast value by using back-propagation algorithm. The forecasting results with real data show that the proposed method has high accuracy and low complexity in short-term power load forecasting.

• Periodical: Applied Mechanics and Materials (Volumes 494-495)
• Pages: 1647-1650
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.494-495.1647
• Online since: February 2014
• Authors: Ling Juan Li*, Wen Huang
• Keywords: Attribute Reduction, BP Neural Network (BPNN), Short-Term Power Load Forecasting

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