Diagnostic tools of energy performance for supermarkets using Artificial Neural Network algorithms

•Artificial Neural Network models are used for diagnosis purposes in supermarkets.•The models perform energy consumption evaluation and fault detection.•The focus is on both the store's and its installed systems energy consumption.

Supermarket performance monitoring is of vital importance to ensure systems perform adequately and guarantee operating costs and energy use are kept at a minimum. Furthermore, advanced monitoring techniques can allow early detection of equipment faults that could disrupt store operation. This paper details the development of a tool for performance monitoring and fault detection for supermarkets focusing on evaluating the Store's Total Electricity Consumption as well as individual systems, such as Refrigeration, HVAC, Lighting and Boiler. Artificial Neural Network (ANN) models are developed for each system to provide the energy baseline, which is modelled as a dependency between the energy consumption and suitable explanatory variables. The tool has two diagnostic levels. The first level broadly evaluates the systems performance, in terms of energy consumption, while the second level applies more rigorous criteria for fault detection of supermarket subsystems. A case study, using data from a store in Southeast England, is presented and results show remarkable accuracy for calculating hourly energy use, thus marking the ANN method as a viable tool for diagnosis purposes. Finally, the generic nature of the methodology approach allows the development and application to other stores, effectively offering a valuable analytical tool for better running of supermarkets.