Smart energy management as a means towards improved energy efficiency
This work addresses energy efficiency for supermarkets, but it is incremental as it applies existing methods to a specific domain without major breakthroughs.
The study tackled the problem of high energy costs from refrigerator equipment in supermarkets by investigating methods to create performance baselines for energy consumption using three learning models (Multiple Linear Regression, Random Forests, and Artificial Neural Networks) applied to data from five supermarkets in Portugal, resulting in baselines created using off-the-shelf data mining techniques and short-term historical data.
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.