Research On Medium-Term Electrical Load Forecasting Model Based On Elman Neural Network

Load forecasting belongs to stratagem forecasting, which is an important research content of power system planning and running and a premise for reliable supplying and economic running. With the development of the state power system, the electric network management modernizes day by day and load forecasting is one of the most important tasks of the modern power system operation research, which arouses increasing more and more interests from researchers and is a major foundation for the research of power system planning and power system economic operation and automatic dispatch. Therefore, finding an appropriate load forecasting method to improve the accuracy of precision has important application value.The core problem of load forecasting is technical matters or the mathematical model. Conventional model is described by clear mathematic representation, which has the advantages of little calculation and high speed, but which has lots of limitations such as non-self learning, and non-self adapting at the same time. Especially with the developing of economic of our country, the structure of power system is more and more complicated, then the features of nonlinear, time-varying and uncertainty of load are more visible, so a suitable mathematical model which can clearly express the relationship between load and the variable of affecting load is difficult to built. But non-mathematical model based on neural network provide a new way for solving the problem of mathematical model.In this paper, medium-term load forecasting based on Elman neural network is presented, and a modified BP algorithm or a back-propagation algorithm with adaptive learning speed and momentum gradient-falling is used. The contribution of this model is to offer a nonlinear and dynamic behavior of electric load, which also can overcome the problems of slow rate of convergence and local minimum of standard BP algorithm. Historical load of data Gansu grid which is used to forecast each month of next year are investigated in details to demonstrate the availability and extensibility of this approach.The algorithm based on mathematical statistics and three-point flat principle is presented to identify and correct anomalous data, which realizes accurate allocation and correction of anomalous data and provides a preparation for accurate forecasting. At last, an application is given to prove that this method has the advantage of simpleness, practicality, small workload and little man-made interference and shows that the method is effective and practical.This thesis researches Elman neural network forecasting model with the ability of adapting time-varying, including one hidden layer Elman neural network model, two hidden layer Elman neural network model and three hidden layer Elman neural network model. A specialcorrelation layer is appended to hidden layer of BP network to form an Elman neural network with memorial ability, with which the nonlinearity and the dynamic behavior of the system can be mapped. At the same time, the problem of by using BP neural network to identify dynamic system with a bad result is solved.At last, by using MATLAB language to program for simulation and comparing forecasting precision of three models, we obtained that the forecasting precision of two-hidden-layer Elman neural network is the best one, which can meet the product needs of enterprise, then load forecasting software based on windows is exploited and has a good prospect of exploiting and application.