Automated fault detection and diagnosis (AFDD) can highlight system faults that otherwise go unnoticed. Supermarkets have strong potential to benefit from AFDD because substantial energy and environmental impacts can be avoided by diagnosing faults in the refrigeration, air-conditioning, and lighting systems. Many methods have been proposed in scientific literature and patents, but adoption is not widespread. This paper describes a project in which an extensive review was conducted of existing AFDD methods, and two disparate methods were selected for further study. The methods include a rule-based method and a data driven method. Each method was tested and analyzed using curated measurement data from existing supermarkets with and without faults present. The rule-based approach is most effective when the AFDD performance index is a controlled variable. The data driven method can detect changes in energy consumption, but is not as effective when input variable have significant fluctuation during unfaulted operation. (C) 2019 Elsevier B.V. All rights reserved.