Computer Vision-based Reader for analogue Energy/Water Meters in low-cost embedded System: a Case Study in an Office Building in Scotland

Proceedings of the 12th Nordic Symposium on Building Physics (NSB 2020), 7-9 September 2020

by Adalberto Guerra-Cabrera; Giulia Barbano (Integrated Environmental Solutions, Research & Development, Glasgow, UK) Giovanni Tardioli; Girish Mallya Upudi (Integrated Environmental Solutions, Research & Development, Dublin, Ireland)


Implementation of cost-effective energy conservation measures (ECMs) is expected to generate up to 18% of carbon emissions reductions in office buildings. In order to determine adequate ECMs for a specific building, operational data is required. However, buildings generally lack operational data in the form of time series that can limit a breath of analysis required for determining adequate ECMs. Energy time-series data is commonly lacking in the UK due to uneven availability of smart meters (heat, gas, water), security restrictions in Energy Information Systems (EIS) and building management systems (BMS), restrictions and costs associated for automated reporting from utility companies, etc. This work presents a non-intrusive computer vision-based reader to generate energy readings at 10-minute resolution using a Raspberry-Pi, a traditional webcam and an LED light. OpenCV, an open source computer vision library, is used to detect and interpret numeric values from a heat meter, which are in turn uploaded to a cloud-based energy platform to create a complete operational data set enabling detailed analytics, fault detection and diagnostics (FDD) and model calibration. A case study of an office building in Scotland is presented. The building has a heat meter with no remote access capabilities. The accuracy of the method, i.e. the ability of the script to accurately derive the rate of change between readings, resulted on a 92% percent during a test done for 100 samples. Recommendations for accuracy improvements are included in the conclusions.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 847053.

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