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ThinkMind // DATA ANALYTICS 2020, The Ninth International Conference on Data Analytics // View article data_analytics_2020_2_100_60063


Technical Indicators for Hourly Energy Market Trading

Authors:
Catherine McHugh
Sonya Coleman
Dermot Kerr

Keywords: Hourly Forecasting; Machine Learning; Technical Indicators; Energy Market.

Abstract:
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.

Pages: 72 to 77

Copyright: Copyright (c) IARIA, 2020

Publication date: October 25, 2020

Published in: conference

ISSN: 2308-4464

ISBN: 978-1-61208-816-7

Location: Nice, France

Dates: from October 25, 2020 to October 29, 2020

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