Investigation On The Short-Term Load Forecasting Of Electric Power System Based On Genetic Neural Network

The short-term load forecasting of electric power system, predicting electric load for a period of hours, days, or weeks, is an important research area of electric power system's operation. It is the important foundation of the study on electric system planning problem, economical running and dispatcher automation. Furthermore, with the establishment of power market, short-term load forecasting will play a more important role in the future. With the power system becoming more and more complex, it's demonstrated that those traditional load-forecasting technologies can't satisfy the requirement of load forecasting accuracy, which becomes more and more strict. So using intelligent technologies to improve the forecasting accuracy and stability of the load forecasting of electric power system is a new character of the short-term load forecasting field of electric power system.Firstly, the principle, features, current status and development of the electric power system short-term load forecasting are generalized in this thesis. And then it makes a summary of many traditional and modern load-forecasting technologies, especially, introduces the application of ANN in electric power system short-term load forecasting. In order to improve BP algorithm's efficiency, this paper gives several improved training algorithms used in BP neural network. Considering that the number of nerve cell in hidden layer, initial weight and unit's bias value are the most important factors to the forecasting's precision of ANN, genetic algorithm is used to choose a more reasonable frame of ANN. Genetic algorithm is good for deciding the proper fabric of net, and help the ANN to conquer it's disfigurement. GA-BP algorithm makes use of the strongpoints of GA and BP algorithm, the results of the example show that GA-BP algorithm is better than BP algorithm only. At last, make weather as a input factor of BP neural network, the example of Kun Ming's load forecasting indicates that this method has better definition and is more suitable for common condition.