Predict+Optimize Problem in Renewable Energy Scheduling

• URL: http://arxiv.org/abs/2212.10723v2
• Date: Mon, 14 Apr 2025 15:09:10 GMT
• Title: Predict+Optimize Problem in Renewable Energy Scheduling
• Authors: Christoph Bergmeir, Frits de Nijs, Evgenii Genov, Abishek Sriramulu, Mahdi Abolghasemi, Richard Bean, John Betts, Quang Bui, Nam Trong Dinh, Nils Einecke, Rasul Esmaeilbeigi, Scott Ferraro, Priya Galketiya, Robert Glasgow, Rakshitha Godahewa, Yanfei Kang, Steffen Limmer, Luis Magdalena, Pablo Montero-Manso, Daniel Peralta, Yogesh Pipada Sunil Kumar, Alejandro Rosales-Pérez, Julian Ruddick, Akylas Stratigakos, Peter Stuckey, Guido Tack, Isaac Triguero, Rui Yuan,
• Abstract summary: This paper benchmarks solutions from the IEEE-CIS Technical Challenge on Predict+ for Renewable Energy Scheduling.<n>The top-ranked method employed optimization using LightGBM ensembles achieved at least a 2% reduction in energy costs.<n>The novelty of this work lies in its comprehensive evaluation of Predict+ methodologies applied to a real-world renewable energy scheduling problem.
• Score: 31.032838966665828
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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