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Application of the Improved Grey Model in the Monthly Electricity Consumption Forecasting
Science Discovery
Volume 4, Issue 1, February 2016, Pages: 1-5
Received: Apr. 7, 2016; Published: Apr. 8, 2016
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Zhang Wenzhe, State Grid Chong Qing Electric Power Company, Chongqing, China
Li Yangyang, Central China Science and Technology Development Co., Ltd., Wuhan, China
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The paper improves the prediction accuracy of the monthly electricity consumption of power system, a hybrid prediction model is put forward aiming at the problems existing in the traditional grey prediction method, which is based on the combined optimization model of particle swarm and K nearest value. Grey prediction equation is solved by particle swarm optimization algorithm, which is a good solution to the problem of the choice of parameters of gray prediction equation, with strong global optimization ability; A combinatorial optimization algorithm of the K- nearest value and particle swarm was proposed, which solves the problem of prediction error caused by large fluctuations of raw data, and improves the accuracy of prediction results. Through the prediction of monthly electricity consumption in the past years, the results show that the combination prediction method proposed in this paper can effectively predict the monthly electricity consumption, and is practical.
Monthly Prediction of Electricity Consumption, Particle Swarm, K Nearest Neighbor, Combination Optimization
To cite this article
Zhang Wenzhe, Li Yangyang, Application of the Improved Grey Model in the Monthly Electricity Consumption Forecasting, Science Discovery. Vol. 4, No. 1, 2016, pp. 1-5. doi: 10.11648/
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