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A method of Radial Basis Function(RBF)neural network algorithm based on Particle Swarm Optimization (PSO) algorithm is introduced. In the background of PJM electricity market in the USA, the short-term price is forecasted with the historical price and loads. After determining the number, the center and width of the hidden layer, code the weights of output layer to individual particles and optimize...
This paper reviews main forecasting techniques used for power system applications. Available forecasting techniques have been discussed with focus on electricity load and price forecasting as well as wind power prediction. Forecasting problems have been classified based on time frame, application specific area and forecasting techniques. Appropriate examples based on data pertaining to the Victorian...
This paper proposes the approach to reduce the prediction error at occurrence time of peak electricity price, and aims to enhance the accuracy of next day electricity price forecasting. In the proposed method, the weekly variation data is used for input factors of the NN at occurrence time of peak electricity price in order to catch the price variation. Moreover, learning data for the neural network...
This paper focuses on sensitivity analysis of neural network (NN) parameters in order to improve the performance of NN based short-term electricity price forecasting. Sensitivity analysis of NN parameters include back-propagation learning set (BP-set), learning rate (eta), momentum (alpha) and NN learning days (dNN). Presented work is an extended version of previous work done by authors to integrate...
This paper proposes an approach for next-day peak electricity price forecasting using neural networks (NN), based on rough sets. In the proposed method, input factors of the NN are selected by using correlation analysis. Moreover, learning data used for training of the NN, is selected by rough sets. The proposed method for creating learning data based on temperature fluctuation is used for generation...
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