Predict+Optimize Problem in Renewable Energy Scheduling

Predict+Optimize frameworks integrate forecasting and optimization to address real-world challenges such as renewable energy scheduling, where variability and uncertainty are critical factors. This paper benchmarks solutions from the IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling, focusing on forecasting renewable production and demand and optimizing energy cost. The competition attracted 49 participants in total. The top-ranked method employed stochastic optimization using LightGBM ensembles, and achieved at least a 2% reduction in energy costs compared to deterministic approaches, demonstrating that the most accurate point forecast does not necessarily guarantee the best performance in downstream optimization. The published data and problem setting establish a benchmark for further research into integrated forecasting-optimization methods for energy systems, highlighting the importance of considering forecast uncertainty in optimization models to achieve cost-effective and reliable energy management. The novelty of this work lies in its comprehensive evaluation of Predict+Optimize methodologies applied to a real-world renewable energy scheduling problem, providing insights into the scalability, generalizability, and effectiveness of the proposed solutions. Potential applications extend beyond energy systems to any domain requiring integrated forecasting and optimization, such as supply chain management, transportation planning, and financial portfolio optimization.

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Publication Details

Journal
IEEE Access
Published
2025-01-01
DOI
https://doi.org/10.1109/access.2025.3555393
Citations
10
Primary Topic
Smart Grid Energy Management
Type
article
Field-Weighted Citation Impact
7.30

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Predict+Optimize Problem in Renewable Energy Scheduling

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IEEE Access
Smart Grid Energy Management
7.30
article

Predict+Optimize Problem in Renewable Energy Scheduling

Pablo Montero‐Manso, Robert E. Glasgow, Alejandro Rosales-Pérez, Mahdi Abolghasemi, Christoph Bergmeir, Rasul Esmaeilbeigi, Quang Neo Bui, Evgenii Genov, Richard Bean, Guido Tack, Steffen Limmer, Scott Ferraro, Nils Einecke, Akylas Stratigakos, Rui Yuan, Nam Trong Dinh, Rakshitha Godahewa, Abishek Sriramulu, Peter J. Stuckey, Luis Magdalena, Isaac Triguero, Frits de Nijs, Julian Ruddick, Priya Galketiya, Daniel Peralta, Yogesh Pipada Sunil Kumar, Yanfei Kang, John T. Betts
article en
10 citations

Abstract

Predict+Optimize frameworks integrate forecasting and optimization to address real-world challenges such as renewable energy scheduling, where variability and uncertainty are critical factors. This paper benchmarks solutions from the IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling, focusing on forecasting renewable production and demand and optimizing energy cost. The competition attracted 49 participants in total. The top-ranked method employed stochastic optimization using LightGBM ensembles, and achieved at least a 2% reduction in energy costs compared to deterministic approaches, demonstrating that the most accurate point forecast does not necessarily guarantee the best performance in downstream optimization. The published data and problem setting establish a benchmark for further research into integrated forecasting-optimization methods for energy systems, highlighting the importance of considering forecast uncertainty in optimization models to achieve cost-effective and reliable energy management. The novelty of this work lies in its comprehensive evaluation of Predict+Optimize methodologies applied to a real-world renewable energy scheduling problem, providing insights into the scalability, generalizability, and effectiveness of the proposed solutions. Potential applications extend beyond energy systems to any domain requiring integrated forecasting and optimization, such as supply chain management, transportation planning, and financial portfolio optimization.

IEEE AccessVol. 13
The University of Sydney (AU), Vrije Universiteit Brussel (BE), Deakin University (AU), Queensland University of Technology (AU), The University of Queensland (AU), Université Paris Sciences et Lettres (FR), Instituto Andaluz de Ciencias de la Tierra (ES), Honda (Germany) (DE), Centre Procédés, Energies Renouvelables et Systèmes Energétiques (FR), Monash University (AU), The University of Adelaide (AU), Beihang University (CN), Tecnológico de Monterrey (MX)
Queensland Health, European Commission, Deakin University, Monash University, University of Adelaide, Hong Kong Polytechnic University, European Regional Development Fund, Agencia Estatal de Investigación
Affordable and clean energy
Openalex Percentile: Top 3%
Smart Grid Energy Management
7.30
DOIOpen Access PDFOpenAlex
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