The Research Of Medium And Long-Term Load Forecasting Based On Artificial Neural Network

Load forecasting is the basic issue to ensure the stable and economical operation of power system. For a power system, both the improvement of electricity quality and the increase of stability and economy depend on the precise load forecasting. The significance of medium and long-term load forecasting lies in the help it offers with the installation of generators and the planning, capacity increase and rebuilding of the electric network, which are among the most important work of the power planning utilities.Based on the basic theory research of ANN, the dissertation expounds the theory and methods of medium and long-term load forecasting with BP neural network, employing the approach ability of Multi-layer Feedforward Neural Network. However, BP algorithm can enter into local optimal point and slow down the convergence rate because of the improper selection of initial weight and threshold. To overcome these weaknesses, the dissertation elaborates how to introduce the genetic algorithm into the optimization of ANN initial weight and threshold. Besides, the dissertation advances the load forecasting model on the basis of artificial neural network, after it analyzes the structure of electricity load and the factors that influence the load forecasting. The model makes a fuller consideration of the effect of every social economic index on the demand of electricity quantity and achieves a higher precision of forecasting, with social economic indexes such as population, gross output value of main national economy trades, etc. as the independent variables and quantity of electricity as the variable.