Research Of A Short-term Load Forecasting Model In Power System Based On Fuzzy Wavelet Network

The short-term load forecasting of electric power system is to predict electric load for aperiod of hours, days, or weeks, and especially twenty-four hours, which is the primary gistfor making the plan of power generation and the scheme of power transmission. It isimportant for economic arrangement of generating capacity, scheduling of fuel purchases andplanning for energy transaction. Its precision will greatly influence the economy and secureoperation of power system. Furthermore, with the establishment of power market, loadforecasting will play a more important role in the future. The principle, current status and development of the electric power system short-termload forecasting are generalized in this thesis. Varieties of traditional and modern predictiontechniques for load forecasting are summarized, and the differences and features of thesemethods are also emphasized. Based on the research of artificial neural network (ANN),fuzzy set theory, and wavelet analysis theory, Owning to the traits of electric power systemshort-term load forecasting, a fuzzy wavelet network (FWN) model in load forecasting isproposed, which combines the time-frequency localization ability of wavelet, fuzzy inferringand the education character of ANN together, and improves the generalization capability. Thecorresponding program of this load forecasting model is developed and applied to two kindsof practical power systems. The results show it is credible and practicable in load forecasting.Compared with the forecasting result of artificial neural network model, this model can get afaster convergence and more precise result, especially in forecasting greatly fluctuant load. Atthe end of this paper, the main problems in the research of short-term load forecasting areanalyzed and the further work is pointed out as well.