Arci, Francesco, Reilly, Jane, Li, Pengfei, Curran, Kevin and Belatreche, Ammar (2018) Forecasting Short-term Wholesale Prices on the Irish Single Electricity Market. International Journal of Electrical and Computer Engineering (IJECE), 8 (6). pp. 4060-4078. ISSN 2088-8708
|
Text
11130-30778-1-PB.pdf - Published Version Available under License Creative Commons Attribution Non-commercial 4.0. Download (1MB) | Preview |
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.
Item Type: | Article |
---|---|
Uncontrolled Keywords: | artificial neural networks, electricity markets, machine learning, market predictions, neural networks |
Subjects: | G400 Computer Science |
Department: | Faculties > Engineering and Environment > Computer and Information Sciences |
Depositing User: | Elena Carlaw |
Date Deposited: | 06 Jun 2019 13:03 |
Last Modified: | 01 Aug 2021 11:34 |
URI: | http://nrl.northumbria.ac.uk/id/eprint/39539 |
Downloads
Downloads per month over past year