Research Of Electric Power Load Forecasting Based On Rough Set Theory

With the development of our national electric industry, the management of electrified wire netting is more and more modernized. People pay more attention to the study on electric system load forecasting than ever. It has become one of the important tasks in the study on modern electric system. It is the important foundation of the study on electric system planning problem, economical running and dispatcher automation. It's accuracy directly influence power system's security, profit and quality.Firstly, the paper studies the characters and sorts of load, it shows that load not only has three kinds of periodicity which are yearly, weekly and daily periodicity but also takes on different characteristics because of the influence of various outer factors. The influence of temperature, rainfall and holiday is analyzed in detail.Secondly, the background and development of ANN to short-term load forecasting are introduced and then some introduction of basic theory and research work have been done about how to apply ANN to prediction technology, during which BP network is introduced importantly.Choosing input variable and networks architecture is key processes for modeling short-term load forecasting by artificial neural networks, the paper deal with it based on rough set theory. Based on the research of rough set theory, the paper discussed the arithmetic of attribute reducts, morever, apply it to mine more correlative attributes in the pending forecasting componentsm, insures the rationality of input parameters of forecasting model.Lastly, construct the short-term load forecasting based on data mining, the author aims at each stage of STLF and has done deep research on the pre-process of historical load data, classification of load samples, process of weather condition, establishment of forecasting model and its input parameters mining. All these work had laid a solid foundation for hi-accuracy STLF. The forecasting results show that the proposed method possesses better forecasting accuracy and the forecasting is satisfactory.