Final published version, 324 KB, PDF document
Available under license: CC BY: Creative Commons Attribution 4.0 International License
Final published version
Licence: CC BY: Creative Commons Attribution 4.0 International License
Final published version
Licence: CC BY: Creative Commons Attribution 4.0 International License
Research output: Contribution to Journal/Magazine › Book/Film/Article review › peer-review
Research output: Contribution to Journal/Magazine › Book/Film/Article review › peer-review
}
TY - JOUR
T1 - Predicting Energy Prices using Cloud forecasting Services
AU - Puecker, Dominik
AU - Weyse, Thomas
AU - Ivkic, Igor
PY - 2024/7/8
Y1 - 2024/7/8
N2 - 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.
AB - 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.
M3 - Book/Film/Article review
VL - Special Theme: Sustainable Cities
SP - 12
EP - 13
JO - ERCIM News
JF - ERCIM News
IS - 138
M1 - 138
ER -