2018/02/08 by Dylan te Lindert, Cláudio Rebelo de Sá, Lindert, Dylan te +5
Engineering · #Building Energy and Comfort Optimization #Computers and Society (cs.CY) #FOS: Computer and information sciences #Green IT and Sustainability #Machine Learning (cs.LG) #Smart Grid Energy Management
paper · pdf · doi:10.48550/arxiv.1802.04128
openalex publication_date 2018/02/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The costs associated with refrigerator equipment often represent more than half of the total energy costs in supermarkets. This presents a good motivation for running these systems efficiently. In this study, we investigate different ways to construct a reference behavior, which can serve as a baseline for judging the performance of energy consumption. We used 3 distinct learning models: Multiple Linear Regression, Random Forests, and Artificial Neural Networks. During our experiments we used a variation of the sliding window method in combination with learning curves. We applied this approach on five different supermarkets, across Portugal. We are able to create baselines using off-the-shelf data mining techniques. Moreover, we found a way to create them based on short term historical data. We believe that our research will serve as a base for future studies, for which we provide interesting directions.