An Efficient Approach for Peak-Load-Aware Scheduling of Energy-Intensive Tasks in the Context of a Public IEEE Challenge

Steffen Limmer and Nils Einecke
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Steffen Limmer: Honda Research Institute Europe GmbH, 63073 Offenbach, Germany
Nils Einecke: Honda Research Institute Europe GmbH, 63073 Offenbach, Germany

Energies, 2022, vol. 15, issue 10, 1-23

Abstract: 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.

Keywords: mathematical optimization; load prediction; scheduling; peak shaving (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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