  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.

Computers and Society;Machine Learning