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Short term load forecasting (STLF) plays a significant role in the management of power system of countries and regions on the grounds of insufficient electric energy for increased need. This paper presents an approach of back propagation neural network based genetic algorithm (GA) optimizing to develop the accuracy of predictions. With GA's optimizing and BP neural network's dynamic feature, the weight...
A prediction scheme of electric load using a Bilinear Recurrent Neural Network (BRNN) is proposed in this paper. Since the BRNN is based on the bilinear polynomial, BRNN has been successfully used in modeling highly nonlinear systems with time-series characteristics. Dynamic BRNN further improves the convergence of BRNN and the Dynamic BRNN can be a natural candidate in predicting electric load. The...
In order to improve the load-forecast precision and availability of power system, a method based on Elman neural network and MATLAB is presented to create a load forecast model, which according to the Elman neural network model having the characteristics of approach to arbitrary nonlinear functions and having the ability of reflecting the dynamic behavior of the system and for the practicability and...
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