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Electricity price forecasting using artificial neural network

Ranjbar, M ; Sharif University of Technology | 2006

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  1. Type of Document: Article
  2. DOI: 10.1109/PEDES.2006.344294
  3. Publisher: 2006
  4. Abstract:
  5. In the restructured power markets, price of electricity has been the key of all activities in the power market. Accurately and efficiently forecasting electricity price becomes more and more important. Therefore in this paper, an Artificial Neural Network (ANN) model is designed for short term price forecasting of electricity in the environment of restructured power market. The proposed ANN model is a four-layered perceptron neural network, which consists of, input layer, two hidden layers and output layer. Instead of conventional back propagation (BP) method, Levenberg- Marquardt BP (LMBP) method has been used for the ANN training to increase the speed of convergence. Matlab is used for training the proposed ANN model, also it is performed on the Ontario electricity market to illustrate its high capability and performance. © 2006 IEEE
  6. Keywords:
  7. Backpropagation ; Computational methods ; Costs ; Electricity ; Mathematical models ; MATLAB ; Neural networks ; Electricity market ; Levenberg-marquardt BP (LMBP) method ; Price forecasting ; Electric load forecasting
  8. Source: 2006 International Conference on Power Electronics, Drives and Energy Systems, PEDES '06, New Delhi, 12 December 2006 through 15 December 2006 ; 2006 ; 078039772X (ISBN); 9780780397729 (ISBN)
  9. URL: https://ieeexplore.ieee.org/abstract/document/4148001