| Based on the discussions of the conventional and recent methods of short term load forecasting such as time series,multiple regression approaches and artificial intelligence technologies,this paper presents a hybrid short term forecasting model which combines the artificial neural network (ANN) and Genetic algorithm (GA) .In order to improve the convergence speed and precision of the Back-propagation (BP),a new improved algorithm-the adapted learning algorithm based on quasi-Newton method is given. In order to improve the shortcoming of the BP-local convergence,which affects the efficiency,and precision of BP,we present an improved Genetic Algorithms. Then,a hybrid short-term load forecasting model is built by combining the above two algorithms. At last,based on the analysis of electric load,we build 24-hour forecasting models according the type of the date and the weather. With all above the discussions,we build the software. At the end of this paper,we applied it to a certain electric network and obtained a satisfied result. |
