Short-TermPower Demand Forecasting for Diverse Consumer Types Using Customized Machine Learning Approaches

Asier Diaz-Iglesias (), Xabier Belaunzaran and Ane M. Florez-Tapia
Additional contact information
Asier Diaz-Iglesias: Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), 20009 Donostia-San Sebastián, Spain
Xabier Belaunzaran: Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), 20009 Donostia-San Sebastián, Spain
Ane M. Florez-Tapia: Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), 20009 Donostia-San Sebastián, Spain

Energies, 2025, vol. 18, issue 20, 1-26

Abstract: Ensuring grid stability in the transition to renewable energy sources requires accurate power demand forecasting. This study addresses the need for precise forecasting by differentiating among industrial, commercial, and residential consumers through customer clusterisation, tailoring the forecasting models to capture the unique consumption patterns of each group. Feature selection incorporated temporal, socio-economic, and weather-related data obtained from the Copernicus Earth Observation (EO) program. A variety of AI and machine learning algorithms for short-term load forecasting (STLF) and very-short-term load forecasting (VSTLF) are explored and compared, determining the most effective approaches. With all that, the main contribution of this work are the new forecasting approaches proposed, which have demonstrated superior performance compared to simpler models, both for STLF and VSTLF, highlighting the importance of customized forecasting strategies for different consumer groups and demonstrating the impact of incorporating detailed weather data on forecasting accuracy. These advancements contribute to more reliable power demand predictions, with our novel forecasting approaches reducing the Mean Absolute Percentage Error (MAPE) by up to 1–3% for industrial and 1–10% for commercial consumers compared to baseline models, thereby supporting grid stability.

Keywords: load forecasting; machine learning; smart grid; clustering (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/20/5332/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/20/5332/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:20:p:5332-:d:1767935

Access Statistics for this article

Energies is currently edited by Ms. Cassie Shen

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().