Predicting Energy Prices using Cloud forecasting Services

Puecker, Dominik and Weyse, Thomas and Ivkic, Igor (2024) Predicting Energy Prices using Cloud forecasting Services. ERCIM News, Specia (138): 138. pp. 12-13.

Text (EN138-article) - Published Version
Available under License Creative Commons Attribution.
Download (0B)

Text (EN138-article) - Published Version
Available under License Creative Commons Attribution.
Download (0B)

Text (EN138-article) - Published Version
Available under License Creative Commons Attribution.
Download (0B)

Text (EN138-article) - Published Version
Available under License Creative Commons Attribution.
Download (0B)

Text (EN138-article) - Published Version
Available under License Creative Commons Attribution.
Download (0B)

Text (EN138-article)
EN138-article.pdf - Published Version
Available under License Creative Commons Attribution.
Download (331kB)

Abstract

The energy sector is essential for economic development, and the liberalisation of the electricity market has made energy pricing dynamic, influenced by supply and demand. Accurate energy price forecasting is crucial for supply planning and investment decisions, offering security and risk minimization for producers and traders. We propose a cloud-based AI prototype that predicts energy prices using historical data. It details our method for assessing model accuracy by comparing actual to predicted prices, demonstrating how cloud technology can streamline data-intensive tasks in energy forecasting.