Reasearch On Power Demand Compound Forecasting Basing Knowledge Discovery

Power demand forecasting is an important foundation work for power system planning and operation. It provides foundation for power companies to set out purchase of electricity and power production plans and also displays an important guarantee for grid secure and economic operation. Power demand indicators will be subject to all kinds of factors. Theories and methods of knowledge discovery can be used to mine the intrinsic law of indicators varying and mutual relation with influence factors. The primary work done in power demand forecasting in this paper is as follows.Firstly, a compound forecasting model based on three index quantities is researched and put forward. Three index quantities are total index quantity, increasing index quantity and index growth rate. Converting prediction indicator sequence into three index quantities sequences and proceeding analysis and forecasting respectively for them, after that do synthesis to get final forecasting result, which is called compound forecasting. In this paper, power demand is divided into quantity of electricity forecasting and electric load prediction, and grey association analysis is applied to analyze the mutual relation between quantity of electricity indicator and influence factors. An achievement model of compound forecasting is presented on, which is comprehensive model of quantity of electricity compound forecasting. It benefits from combination forecasting idea. At first, an analytic hierarchy process model is constructed to analyze and estimate the three index quantities respectively, then selecting out optimal forecasting model for each index quantity, in which evaluation criterion involve model forecasting error, fitting degree of model, expert trust degree of the model and confidence level of the trend of forecasting results. And then, two synthesis methods are researched, which are synthesis method basing forecasting efficiency degree and radial basic function neural network synthesize model, and compare relative merits of them. Finally, through instance analysis to contrast dominant of the comprehensive model to traditional models. Compound forecasting methodology in a position to via to analyze forecasted indices multiangular in depth, gain much relevant data variational inherent law, thereby work out preferable forecast.Secondly, research on comprehensive utilization multiple data mining method proceed short term load forecasting. At first, reduction influencing factor set is done with rough set, and the reduced set is regarded as day characteristic set. After that using fuzzy C-means clustering algorithm to cluster daily load curve, via which most similar curves are clustered, following substitute day characteristic set for daily load curve, and computer every category center. Calculating space of characteristic set to every class center for forecasted day before forecasting, and the class of proximate space is the attributive class for forecasted day. Training specimen of BP neural network prediction model may choose data from the attributive category for training. Through case analysis, should method in a position prominence increase precision of prediction, and might adapt some technical dates'change in load.Thirdly, make a study of application methods for collaborative knowledge discovery in power demand forecasting. Collaborative knowledge discovery could fusion user drive knowledge and data drive knowledge, which is workable for user to participate in the course of knowledge discovery, and through knowledge focusing to realize more knowledge excavation, in turn, knowledge base dynamic state renewal and knowledge all-around evaluation. In paper, put forward electricity demand forecasting collaborative knowledge discovery model, which consider fully power demand prediction characteristic and mainly realize to synergistic evaluation for forecasting result, and hereon implement forecasting result trimming.Fourthly, a power demand forecasting and analysis system is developed. It applies the compound forecasting idea and adopts modular design. The system has characteristic of friendly interface, plentiful function, all-side analysis and strong expandability. It has been used in Hebei electric power corporation development and mastermind department over two years and received an award of progress prize in science and technology honorable mention Hebei electric power corporation.