Technical Indicators and Prediction for Energy Market Forecasting

Catherine McHugh, Sonya Coleman, Dermot Kerr

Research output: Contribution to conferencePaperpeer-review

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Machine learning usage for forecasting is popular in financial trading, particularly for stock price prediction and this is often combined with technical indicators to extract key predictive indicators from large time series trading datasets. Energy market trading data have similar characteristics to financial trading data, therefore deriving technical indicators specifically for electricity prices will help predict future prices and reduce trading costs. We have derived eight technical indicators for the Integrated Single Electricity Market (ISEM) energy market in Ireland using hourly electricity price data over the period February 2019 until November 2019. Technical indicator based models were obtained by using machine learning regression algorithms (Extreme Gradient [XG] Boost, Random Forest, and Gradient Boosting) trained with the proposed novel technical indicators. The results of the technical indicator models were
compared against the baseline model (raw price data only) to see if using technical indicators as inputs improves model performance. We conclude that electricity prices can be accurately predicted using the proposed technical indicators.
Original languageEnglish
Number of pages6
Publication statusPublished (in print/issue) - 14 Dec 2020
Event2020 19th IEEE International Conference on Machine Learning and Applications - Virtual
Duration: 14 Dec 202017 Dec 2020


Conference2020 19th IEEE International Conference on Machine Learning and Applications
Abbreviated titleICMLA 2020
Internet address


  • Electricity Price Forecasting
  • Short-term
  • Time Series
  • Technical Indicators
  • Machine Learning


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