Abstract
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.
Highlights
Buildings consume 40% of the total energy used globally and are responsible of 30% of the total CO2 emissions [1]
Research shows that building stock can greatly reduce their energy demand by implementing energy conservation measures (ECMs) [3], [4] and the Intergovernmental Panel on Climate Change (IPCC) report suggests that buildings in the public and commercial sector could achieve an 18% reduction in carbon emissions through no or low cost ECMs [5]
In order to identify and implement ECMs, operational data from the studied building is required for detailed analysis, fault detection and diagnostics (FDD) and the creation of building energy models (BEM) that be used for evaluating cost-effective ECMs
Summary
Buildings consume 40% of the total energy used globally and are responsible of 30% of the total CO2 emissions [1]. It is important that highlight that most of the building stock in the UK is equipped with non-energy efficient building management systems (or has no BMS at all) [6] meaning that a large proportion of operational data is not available for detailed analysis, FDD and BEMs, which limits the implementation of cost-effective ECMs. time-series energy information is not always accessible in near real time due to the lack of smart meters for energy sources (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, limitations in the installation of newer meters due to lack of ownership in leased spaces, restrictions due to specific regulations of a country and the fact that meters must comply with safety regulations.
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