Abstract
| Language | English |
|---|---|
| Pages | 4060 |
| Number of pages | 4078 |
| Journal | International Journal of Electrical and Computer Engineering |
| Volume | 8 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - 1 Dec 2018 |
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Forecasting Short-term Wholesale Prices on the Irish Single Electricity Market. / Curran, Kevin.
In: International Journal of Electrical and Computer Engineering, Vol. 8, No. 6, 01.12.2018, p. 4060.Research output: Contribution to journal › Article
TY - JOUR
T1 - Forecasting Short-term Wholesale Prices on the Irish Single Electricity Market
AU - Curran, Kevin
PY - 2018/12/1
Y1 - 2018/12/1
N2 - 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.
AB - 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.
U2 - 10.11591/ijece.v8i6.pp.4060-4078
DO - 10.11591/ijece.v8i6.pp.4060-4078
M3 - Article
VL - 8
SP - 4060
JO - International Journal of Electrical and Computer Engineering
T2 - International Journal of Electrical and Computer Engineering
JF - International Journal of Electrical and Computer Engineering
SN - 2088-8708
IS - 6
ER -