Chinese Annual Electric Power Consumption Forecasting Based on Grey Model and Global Best Optimization Method
DOI: 10.1109/DBTA.2009.126 Conference: First International Workshop on Database Technology and Applications, DBTA 2009, Wuhan, Hubei, China, April 25-26, 2009, Proceedings
The annual electric power consumption is one of the most important factors in operation decisions of Chinese electric power generation groups. The grey model is feasible method to deal with this trend extension problem with few data. But the simple approximation in dispersing the first order differential equation affects it forecasting precise. Based on adjusting the positions of each particle, the global best optimization method could search the best proportion point. This could improve the forecasting results in the practice of annual electric power consumption.
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ABSTRACT: Monthly electric energy demand forecasting plays an important role for the running of power system. China has two tow calendars and they works at the same time. Holidays designed by the lunar calendar affect the regularity of monthly electric load recorded only by the Gregorian one. The normal fuzzy transform is advanced here to quantitatively describe the impact of the Spring Festival and further divided the influence into Jan. and Feb. After excluding the influence, the amended historical data are adopted to training RBF neural network. Experiment results show that because the regularity of raw data is improved, the generalization ability and forecasting precise of RBF neural network are improved.
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