Automated system for determining heat lossesof the educational audience. Hardware part

The article discusses the structure of the hardware and internetworking of automated room heat loss identification system on example of the Ugra State University classroom. The temperature controller has been developed taking into account the specifics of the room. A heat meter with telemetry capability has been selected.

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

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.

Numerical simulation of ultrasonic heat meter by multiphysics coupling finite-element simulation software

Objective: To increase heat calculation accuracy, the numerical simulation of the ultrasonic heat meter is explored by multiphysics coupling. Methods: The COMSOL, a multiphysics coupling finite-element simulation software, is used to build the coupling model of the sound field, structure field, and electric field. The propagation of ultrasonic waves in heat meters is simulated, and its sound field distribution in pure water is analyzed. According to the operating conditions of ultrasonic heat meters, the influence of impurities with different concentrations on ultrasonic propagation is analyzed. The end-face sound pressure levels of the incident transducer and the receiving transducer are compared to obtain the attenuation laws of ultrasonic waves in the liquid-solid two-phase flow. Results: The main lobe and multiple side lobes exist during the propagation of ultrasonic waves. The energy of the main lobe is higher than that of the side lobes. Bubbles resonate under the action of the sound field. Also, bubbles of different diameters correspond to different resonance frequencies, which have larger sound pressure than that of the incident sound field. Most of the sound waves are reflected at the liquid-solid interface, while some of them continue to propagate through the media, affecting the sound pressure distribution on the end-face of the receiving transducer, thereby affecting the measurement accuracy of the ultrasonic heat meter. Conclusion: The reliability and detection efficiency of the heat meter is improved, which is significant and theoretically valuable.

Predicting Heat Meters’ Failures With Selected Machine Learning Models

Heat metres are used to calculate the consumed energy in central heating systems. The subject of this article is to prepare a method of predicting a failure of a heat meter in the next settlement period. Predicting failures is essential to coordinate the process of exchanging the heat metres and to avoid inaccurate readings, incorrect billing and additional costs. The reliability analysis of heat metres was based on historical data collected over many years. Three independent models of machine learning were proposed, and they were applied to predict failures of metres. The efficiency of the models was confirmed and compared using the selected metrics. The optimisation of hyperparameters characteristics for each of models was successfully applied. The article shows that the diagnostics of devices does not have to rely only on newly collected information, but it is also possible to use the existing big data sets.