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《Power System Technology》 2009-17
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Short-Term Electricity Price Forecasting Based on Cloud Model

LI Ran1,CUI Tian-bao1,XIAO Jin-yong2 (1.School of Electrical & Electronic Engineering,North China Electric Power University,Baoding 071003,Hebei Province,China;2.Huaneng Shang'an Power Plant,Shijiazhuang 050310,Hebei Province,China)  
At present,there are various electricity price forecasting methods such as time-series,neural network,wavelet transform and so on,however these methods can merely give out the forecasted results at required point of time. In this paper,at first the concept and features of cloud model are presented,the process of the discretization of electricity price and load data and the concept zooming based on cloud model are given then a conceptual models of price and load are achieved. By means of great determination law the elastic classification of the data set are conducted and the Boolean database of electricity price and load is established; then according to given support degree and confidence level of soft domain values and by use of cloud based mining algorithm for associative knowledge,the association rules among time,load and electricity price are obtained. Finally,taking the conjunction of time and load as the premise of the rules and the price as the consequent of the rules,a rule generator is built and forecasting is proceeded by the mined rules. The forecasting results of the proposed method is a set of a series of uncertain discrete points and every point of the set can be offered to the user as forecasting result,and the user can select these results properly according to their experience and other information,and it is also possible to offer the expectation values of all points to the user as the results.
【CateGory Index】: F407.61
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