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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 describes an identification of the best similar days parameters for artificial neural network (ANN) based short-term price forecasting. The work presented in this paper is an extended version of our previous works where we proposed the price prediction technique by using ANN, which is based on similar days method. According to similar days method, we select similar price days corresponding...
Price forecasting has become a very valuable tool in the current upheaval of electricity market deregulation. It plays an important role in power system planning and operation, risk assessment and other decision making. This paper provides a method for predicting hourly prices in the day-ahead electricity market using recursive neural network (RNN) technique, which is based on similar days approach...
Short-term price forecasting in competitive electricity markets is critical for consumers and producers in planning their operations and managing their price risk. This paper adopts artificial neural network (ANN) model based on similar days methodology in order to forecast weekly electricity prices in the PJM market. To demonstrate the superiority of the proposed model, extensive analysis is conducted...
Forecasting hourly electricity prices and loads in daily power markets is the most essential task and basis for any decision making. An approach to predict the market behaviors is to use the historical prices, loads and other required information to forecast the future prices and loads. This paper presents an approach for short-term electricity price and load forecasting using neural network model,...
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