Research On Mid-Long Term Power Load Forecasting Model And Realization Of Software

Mid-long term power load forecasting is fundamental to power system planning and operation. The purpose is to discuss how we can forecast electrical demand accurately. The united data mining technology for historical data preprocessing is proposed in the thesis. The data sequence which can boost up rules is composed through the method. The optimum combined model is built based on that. For the monthly load with double trends of increasing and fluctuating, the integrated optimum gray neural network model of monthly load forecasting is proposed in the thesis for the first time. In the model, we regard vertical historical data as the primitive array to forecast increasing trend by the gray model, and regard horizontal historical data as the primitive array to forecast fluctuating trend by the ANN . Based on that, the concept of the optimum credibility is introduced, and the integrated optimum model is build in the thesis. In this thesis, multi-degree recursive regression analysis is applied to forecast the mid-long term load for the first time. The method can perfectly reflect the import function of the related factors as well as have strong adaptability to the power system with sequential variance. The advantage and applicability of the three methods mentioned in the front have been verified by instances. Lastly, mid-long term load forecasting software is developed for LiaoNing power grid in this thesis.