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

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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.

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Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sector

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Authors and Affiliations

  1. CertX, Fribourg, Switzerland

    Marco Repetto

  2. IPAG Business school, Nice, France and Ca’ Foscari University of Venice, Venice, Italy

    Cinzia Colapinto

  3. Abu Dhabi University, Abu Dhabi, United Arab Emirates

    Muhammad Usman Tariq

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  1. Marco Repetto
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  2. Cinzia Colapinto
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  3. Muhammad Usman Tariq
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Contributions

MR has contributed to Sects. 1, 3, 4, 5, and 6; CC has contributed to Sects. 1, 2, 5, and 6; MUT has contributed to Sect. 1.

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Correspondence to Cinzia Colapinto.

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Repetto, M., Colapinto, C. & Tariq, M.U. Artificial intelligence driven demand forecasting: an application to the electricity market. Ann Oper Res (2024). https://doi.org/10.1007/s10479-024-05965-y

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