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Ammar Belatreche
Northumbria University
Computer Science & IT Electrical & Electronics Engineering

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Forecasting Short-term Wholesale Prices on the Irish Single Electricity Market Francesco Arci; Jane Reilly; Pengfei Li; Kevin Curran; Ammar Belatreche
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 6: December 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1123.558 KB) | DOI: 10.11591/ijece.v8i6.pp4060-4078

Abstract

Electricity markets are different from other markets as electricity generation cannot be easily stored in substantial amounts and to avoid blackouts, the generation of electricity must be balanced with customer demand for it on a second-by-second basis. Customers tend to rely on electricity for day-to-day living and cannot replace it easily so when electricity prices increase, customer demand generally does not reduce significantly in the short-term. As electricity generation and customer demand must be matched perfectly second-by-second, and because generation cannot be stored to a considerable extent, cost bids from generators must be balanced with demand estimates in advance of real-time. This paper outlines a a forecasting algorithm built on artificial neural networks to predict short-term wholesale prices on the Irish Single Electricity Market so that market participants can make more informed trading decisions. Research studies have demonstrated that an adaptive or self-adaptive approach to forecasting would appear more suited to the task of predicting energy demands in territory such as Ireland. We have identified the features that such a model demands and outline it here.
Co-Authors Francesco Arci Jane Reilly Kevin Curran Pengfei Li