Electricity price short-term forecasting using artificial neural networks

• Published in: IEEE transactions on power systems Vol. 14; no. 3; pp. 851 - 857
• Main Authors: Szkuta, B.R., Sanabria, L.A., Dillon, T.S.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01-08-1999; Institute of Electrical and Electronics Engineers
• Subjects: Applied sciences; Artificial neural networks; Computer networks; Economic forecasting; Electrical engineering. Electrical power engineering; Electrical power engineering; Electricity supply industry; Electricity supply industry deregulation; Exact sciences and technology; Forward contracts; Load forecasting; Operation. Load control. Reliability; Power generation; Power networks and lines; Power system modeling; Power systems; Short term; Waveform; Backpropagation algorithm; Deregulation; Experimental result; Electrical network; Demand forecasting; Pricing; Neural network; Modeling
• ISSN: 0885-8950; 1558-0679
• DOI: 10.1109/59.780895

This paper presents the system marginal price (SMP) short-term forecasting implementation using the artificial neural networks (ANN) computing technique. The described approach uses the three-layered ANN paradigm with backpropagation. The retrospective SMP real-world data, acquired from the deregulated Victorian power system, was used for training and testing the ANN. The results presented in this paper confirm considerable value of the ANN based approach in forecasting the SMP.