A Novel Approach to Forecast Electricity Price for PJM Using Neural Network and Similar Days Method

Price forecasting in competitive electricity markets is critical for consumers and producers in planning their operations and managing their price risk, and it also plays a key role in the economic optimization of the electric energy industry. This paper explores a technique of artificial neural net...

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Bibliographic Details
Published in:IEEE transactions on power systems 2007-11, Vol.22 (4), p.2058-2065
Main Authors: Mandal, P., Senjyu, T., Urasaki, N., Funabashi, T., Srivastava, A.K.
Format: Article
Language:English
Subjects:
Artificial neural networks
Computer simulation
Day-ahead electricity market
Economic forecasting
Electric utilities
Electricity
Electricity supply industry
Energy management
Forecasting
Industrial economics
Learning theory
Load forecasting
Markets
Mathematical models
neural network
Neural networks
Permissible error
Power generation economics
Predictive models
price forecasting
Risk management
similarity technique
Studies
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Summary:Price forecasting in competitive electricity markets is critical for consumers and producers in planning their operations and managing their price risk, and it also plays a key role in the economic optimization of the electric energy industry. This paper explores a technique of artificial neural network (ANN) model based on similar days (SD) method in order to forecast day-ahead electricity price in the PJM market. To demonstrate the superiority of the proposed model, publicly available data acquired from the PJM Interconnection were used for training and testing the ANN. The factors impacting the electricity price forecasting, including time factors, load factors, and historical price factors, are discussed. Comparison of forecasting performance of the proposed ANN model with that of forecasts obtained from similar days method is presented. Daily and weekly mean absolute percentage error (MAPE) of reasonably small value and forecast mean square error (FMSE) of less than 7/MWh were obtained for the PJM data, which has correlation coefficient of determination of 0.6744 between load and electricity price. Simulation results show that the proposed ANN model based on similar days method is capable of forecasting locational marginal price (LMP) in the PJM market efficiently and accurately.
ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2007.907386