Coleman, Sonya

Kerr, Dermot

McHugh, Catherine

English

Financial trading often combines machine learning and technical indicators to accurately predict future market prices. Energy data and financial data have similar features; therefore, this research derives eight electricity price technical indicators to help control spending and reduce trading costs for the Integrated Single Electricity Market in Ireland. The proposed technical indicators were derived from electricity price data, collected on an hourly basis from February until November 2019, and used to train three regression machine learning algorithms (Random Forest, Gradient Boosting, and Extreme Gradient Boosting). The results for each of the regression algorithms were first compared using one model for all trading periods. The Random Forest algorithm was then trained with the same technical indicators for each of the 24 hours periods individually to see if an hourly approach enhanced model performance. The proposed technical indicators accurately predict electricity prices and overall accuracy was greatly improved using separate hourly forecasting models

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Technical Indicators for Hourly Energy Market Trading

https://pure.ulster.ac.uk/ws/files/87492101/IARIA2020_Acceptance_Paper_60063.pdf

Technical Indicators for Hourly Energy Market Trading

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Ulster University's Research Portal