Day-ahead price forecasting in restructured power systems using artificial neural networks

Over the past 15 years most electricity supply companies around the world have been restructured from monopoly utilities to deregulated competitive electricity markets. Market participants in the restructured electricity markets find short-term electricity price forecasting (STPF) crucial in formula...

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Bibliographic Details
Published in:Electric power systems research Vol. 78; no. 8; pp. 1332 - 1342
Main Authors: Vahidinasab, V., Jadid, S., Kazemi, A.
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 01.08.2008
Elsevier
Subjects:
Applied sciences
Artificial neural networks
Electrical engineering. Electrical power engineering
Electrical power engineering
Electricity price forecasting
Exact sciences and technology
Fuzzy clustering method
Levenberg–Marquardt algorithm
Miscellaneous
Operation. Load control. Reliability
Power networks and lines
Sensitivity analysis
Electricity price forecasting
Sensitivity analysis
Levenberg–Marquardt algorithm
Fuzzy clustering method
Artificial neural networks
Short term
Automatic classification
Deregulation
Profitability
Power system economics
Restructuration
Daily variation
Neural network
Risk analysis
Implementation
Electrical network
Levenberg Marquardt algorithm
Pricing
Power markets
Economic aspect
Open market
Risk management
Learning algorithm
Comparative study
Levenberg-Marquardt algorithm
ISSN:0378-7796, 1873-2046
Online Access:Get full text
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Summary:Over the past 15 years most electricity supply companies around the world have been restructured from monopoly utilities to deregulated competitive electricity markets. Market participants in the restructured electricity markets find short-term electricity price forecasting (STPF) crucial in formulating their risk management strategies. They need to know future electricity prices as their profitability depends on them. This research project classifies and compares different techniques of electricity price forecasting in the literature and selects artificial neural networks (ANN) as a suitable method for price forecasting. To perform this task, market knowledge should be used to optimize the selection of input data for an electricity price forecasting tool. Then sensitivity analysis is used in this research to aid in the selection of the optimum inputs of the ANN and fuzzy c-mean (FCM) algorithm is used for daily load pattern clustering. Finally, ANN with a modified Levenberg–Marquardt (LM) learning algorithm are implemented for forecasting prices in Pennsylvania–New Jersey–Maryland (PJM) market. The forecasting results were compared with the previous works and showed that the results are reasonable and accurate.
ISSN:0378-7796
1873-2046
DOI:10.1016/j.epsr.2007.12.001