• Energy Research

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.

The shift towards renewable energy and decreasing battery prices have led to numerous installations of PV and battery systems in industrial and public buildings. Furthermore, the fluctuation of energy costs is increasing since energy sources based on solar and wind power depend on the weather situation. In order to reduce energy costs, it is necessary to plan energy-hungry activities while taking into account private PV production, battery capacity, and energy market prices. This problem was posed in the 2021 “IEEE-CIS Technical Challenge on Predict + Optimize for Renewable Energy Scheduling”. The target was to solve the two subtasks of forecasting the base load and of computing an optimal schedule of a list of energy intensive activities with inter-dependencies. We describe our approach to this challenge, which resulted in the third place of the leaderboard. For the prediction of the base load, we use a combination of a statistical and a machine learning approach. For the optimization of schedules, we employ a tuned mixed integer linear programming approach. We present a detailed experimental evaluation of the proposed approach on the use case and data provided in the challenge.