Artificial intelligence driven demand forecasting: an application to the electricity market

Marco Repetto (), Cinzia Colapinto () and Muhammad Usman Tariq ()
Additional contact information
Marco Repetto: CertX
Cinzia Colapinto: IPAG Business school, Nice, France and Ca’ Foscari University of Venice
Muhammad Usman Tariq: Abu Dhabi University

Annals of Operations Research, 2025, vol. 346, issue 2, No 34, 1637-1651

Abstract: Abstract Demand forecasting with maximum accuracy is critical to business management in various fields, from finance to marketing. In today’s world, many firms have access to a lot of data that they can use to implement sophisticated models. This was not possible in the past, but it has become a reality with the advent of large-scale data analysis. However, this also requires a distributed thinking approach due to the resource-intensive nature of Deep Learning models. Forecasting power demand is of utmost importance in the energy industry, and various methods and approaches have been employed by electrical companies for predicting electricity demand. This paper proposes a novel multicriteria approach for distributed learning in energy forecasting. We use a Quadratic Goal Programming approach to construct a robust decision rule ensemble that optimizes a pre-defined loss function. Our approach is independent of the loss function’s differentiability and is also model agnostic. This formulation offers interpretability for the decision-maker and demonstrates less proclivity of regression against the mean that affects autoregressive models. Our findings contribute to the field of energy forecasting and highlight the potential of our approach for enhancing decision-making in the energy industry.

Keywords: Demand forecasting; Federated learning; Deep learning; Multiple criteria decision making; Goal programming; Electricity forecasting (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10479-024-05965-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:annopr:v:346:y:2025:i:2:d:10.1007_s10479-024-05965-y

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10479

DOI: 10.1007/s10479-024-05965-y

Access Statistics for this article

Annals of Operations Research is currently edited by Endre Boros

More articles in Annals of Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().