Abstract
Load forecasting is an important work in the electric power department, and the medium and long term load forecasting is mainly aimed at the electric power generation planning and development planning. Accurate forecasting is the basis of rational planning, and the planning will greatly affect the investment. Therefore, it has a great significance to improve the accuracy of load forecasting. Accuracy of the medium and long term load forecasting is affected by various stochastic factors, such as economy, policy, and climate. So, the accurate forecast is a very complex work. In order to improve the accuracy of load forecasting, this paper introduces a combined forecasting model, which can obtain more accurate results by combining the advantages of each model. In this paper, we use the gray prediction model, neural network prediction model, and regression analysis model for the whole society annual electricity consumption in long-term load combination forecast. The method of this paper can make full use of the advantages of the grey prediction which requires less data, simple operation, and easy to test. It can make full use of the advantages of the neural network method which has the function of self adaptation, and has a strong ability of learning and mapping. It can also make full use of the advantages of the regression analysis method that does not need to take into account the distribution of the data and the trend of change. Finally, we conduct an example verification. We choose the proposed method, the traditional GM (1,1) model, regression analysis prediction model and BP neural network prediction model for comparison. The results show that the proposed method is feasible and effective.
