Data-driven modeling of the refrigeration load in supermarkets — A case study on three European supermarkets

When investigating supermarket refrigeration systems, it is essential to consider the refrigeration load as it directly affects the required compressor capacity and energy consumption. Therefore, it is important to have knowledge of the refrigeration load and its main influencing factors. Direct usage of data for the refrigeration load in a whole-year energy simulation can present significant challenges. This is partly due to long lead times for data collection and the challenges posed by missing data. As a result, often there is no complete data set of the refrigeration load for the whole-year. Therefore, a mathematical model can serve as a tool to interpolate and extrapolate the refrigeration load over the course of a year. The model is not intended for transfer to other locations. This paper begins with a overview of correlations found in the literature, followed by the development of an improved model that accounts for the impact of ambient temperature and time on the refrigeration load. Additionally, a novel approach using a neural network is proposed as a second model. Both models are then applied to data from three supermarkets in Europe and the model performance is evaluated. Both models performed acceptably in representing the refrigeration load of all three supermarkets. Especially including the influence of time of day into the first model significantly improves the prediction accuracy. The neural network has the highest prediction performance and can improve the prediction performance by 40% over the improved literature model.