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 publicationIFAC Proceedings Volumes (IFAC-PapersOnline)
Volume17
Edition1 PART 1
DOIs
Publication statusPublished - 2008
Event17th World Congress, International Federation of Automatic Control, IFAC - Seoul
Duration: 2008 Jul 62008 Jul 11

Other

Other17th World Congress, International Federation of Automatic Control, IFAC
CitySeoul
Period08/7/608/7/11

    Fingerprint

Keywords

  • Analysis and control in deregulated power systems
  • Artifical intelligence
  • Load forecast

ASJC Scopus subject areas

  • 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 IFAC Proceedings Volumes (IFAC-PapersOnline) (1 PART 1 ed., Vol. 17) https://doi.org/10.3182/20080706-5-KR-1001.3789