Forecasting of electricity price and demand using auto-regressive neural networks

Daiki Yamashita, Aishah Mohd Isa, Ryuichi Yokoyama, Takahide Niimura

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper proposes a forecasting technique of electricity demand and price with volatility based on neural networks. Recent deregulation and liberalization are worldwide currents in the electric industry. The price competition was introduced in a spot market, and the price volatility is concerned because the demand side is non-elastic, and electricity differs from other general commodities. The authors firstly predict an uncertain electric power demand by using the auto-regressive model of the neural networks. The neural network is a popular feed-forward three-layer model, and the input variables of the neural networks include the historical demand, temperature, weather-related discomfort index, and the day of the week. Secondly, by using the demand forecasted and the past prices, we apply the technique for forecasting the electricity price of the next day. The utility of the proposed technique was verified by using real data of the electric power wholesale spot market.

Original languageEnglish
Title of host publicationProceedings of the 17th World Congress, International Federation of Automatic Control, IFAC
Volume17
Edition1 PART 1
DOIs
Publication statusPublished - 01 Dec 2008
Event17th World Congress, International Federation of Automatic Control, IFAC - Seoul, Korea, Republic of
Duration: 06 Jul 200811 Jul 2008

Other

Other17th World Congress, International Federation of Automatic Control, IFAC
CountryKorea, Republic of
CitySeoul
Period06/07/0811/07/08

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Electricity
Neural networks
Electric industry
Deregulation
Temperature

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering

Cite this

Yamashita, D., Mohd Isa, A., Yokoyama, R., & Niimura, T. (2008). Forecasting of electricity price and demand using auto-regressive neural networks. In Proceedings of the 17th World Congress, International Federation of Automatic Control, IFAC (1 PART 1 ed., Vol. 17) https://doi.org/10.3182/20080706-5-KR-1001.3789
Yamashita, Daiki ; Mohd Isa, Aishah ; Yokoyama, Ryuichi ; Niimura, Takahide. / Forecasting of electricity price and demand using auto-regressive neural networks. Proceedings of the 17th World Congress, International Federation of Automatic Control, IFAC. Vol. 17 1 PART 1. ed. 2008.
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Yamashita, D, Mohd Isa, A, Yokoyama, R & Niimura, T 2008, Forecasting of electricity price and demand using auto-regressive neural networks. in Proceedings of the 17th World Congress, International Federation of Automatic Control, IFAC. 1 PART 1 edn, vol. 17, 17th World Congress, International Federation of Automatic Control, IFAC, Seoul, Korea, Republic of, 06/07/08. https://doi.org/10.3182/20080706-5-KR-1001.3789

Forecasting of electricity price and demand using auto-regressive neural networks. / Yamashita, Daiki; Mohd Isa, Aishah; Yokoyama, Ryuichi; Niimura, Takahide.

Proceedings of the 17th World Congress, International Federation of Automatic Control, IFAC. Vol. 17 1 PART 1. ed. 2008.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Yamashita D, Mohd Isa A, Yokoyama R, Niimura T. Forecasting of electricity price and demand using auto-regressive neural networks. In Proceedings of the 17th World Congress, International Federation of Automatic Control, IFAC. 1 PART 1 ed. Vol. 17. 2008 https://doi.org/10.3182/20080706-5-KR-1001.3789