Heat Meter Research Articles

Estimating the as-built thermal performance of dwellings using simulated on-board data: From ideal to limited monitoring

Advancement in non-intrusive monitoring of energy usage in households presents an ideal opportunity to get insight into the as-built performance of buildings and assess performance indicators such as the Heat Loss Coefficient (HLC) of the building fabric. The availability of in-use monitored data contributes to bridging the energy performance gap while avoiding costly and exhaustive dedicated measurement campaigns. Methodologies to assess the building performance by combining on-board monitoring and statistical data-driven models are widely accepted. However, their unsupervised and automated applicability on a large scale, with limited data and no insight into the building characteristics and occupants’ behaviour still needs to be investigated. Therefore, this work aims at assessing the reliability and performance of statistical black-box AutoRegressive with eXogenous input (ARX) models on artificial measurement campaigns starting from idealistic setups and progressing towards limited ones. In this investigation, the uncertainty in the estimate inherited from inputs, such as different heat sources or weather data, and their post-processing is combined with the uncertainty originating from different monitoring packages, which results in a thorough overview of the statistical modelling limitations. Results show that disaggregation of the final heating usage poses a major challenge. In cases where gross gas usage is measured, and the system efficiency and domestic hot water consumption are not taken into account, the HLC can deviate by 49% in low-performing houses and by 106% in well-performing houses. However, by applying data pre-processing methods the discrepancy can be lowered to 8% and 23% respectively, but with high uncertainty. On the other hand, the installation of an additional heat meter could yield deviations from the target of 26% for low-performing and 11% for well-performing dwellings without data manipulation, both with low uncertainties.

In-situ validation of a sizing method for the combined production and distribution of heat for space heating and domestic hot water

To maximize the sustainable and economic benefits of collective heating systems, proper sizing is fundamental. This paper presents the validation of a novel sizing approach for collective systems producing and distributing heat for both space heating and domestic hot water, utilizing residential heat meter data. A validation methodology is developed to overcome the limitations of this type of data to identify the peak heat demand and estimate the peak heat demand under design outdoor conditions. The latter is estimated utilizing multiple linear regression coupled with an analysis of the maximum deviations. The power-storage characteristic, which shows all combinations of thermal power and thermal storage to meet the peak heat demand is determined and used to validate the novel sizing approach for six case studies. Although the results are promising, undersizing problems may arise in cases with decentralized heat storage.

Analysis of the Energy Consumption for Heating in Schools

Abstract The aim of the study is to analyse energy consumption for heating in set of the school buildings. Data analysis includes three data sets – predictions from energy performance certificates (EPC), measured data from heat meters and measured climate corrected data. Data sets were statistically checked and processed and represented graphically. The actual energy consumption differs from the one stated in the EPCs in all cases, which means that schools consume on average 15 % more energy than it is stated in the EPC. Overall, the data reveal that the actual energy consumption differs from the theoretical consumption, with all schools having higher actual consumption than the theoretical values. The differences in the data sets are discussed, and possible improvements are suggested taking into account strong relation between energy consumption and indoor air quality.

A novel intelligent modeling and prediction of heat energy consumption in smart buildings

The rising cost and demand for energy have prompted several organizations to devise innovative methods for monitoring, managing, and conserving energy. Heat energy load prediction in smart buildings is critical for saving energy as well as for meeting energy demands. As the cost of energy and demand is increasing, people must find some way to control it and save energy. To overcome these issues, an intelligent energy management system needs to be developed to help individuals by predicting heat energy demand ahead of time and analyzing energy use patterns. This paper introduces a data-driven short-term heat energy consumption prediction for smart buildings by developing a hybrid deep learning model considering the capabilities of Convolutional and Recurrent Neural Networks. The proposed model extracts hidden patterns from actual data collected through sensors and also uses hand-crafted features to predict the heat energy demand accurately. The efficiency of the proposed model has been evaluated on real smart heat metering data, a case study in Denmark, having three kinds of buildings: single-family, apartment, and terraced houses. The extensive experimental results have been reported with real-world data by considering various evaluation metrics such as MAE, RMSE, MAPE and R2. The results are promising and outperformed other state-of-the-art methods.

Thermal Characteristics and User Regulation of Household Heat Metering for Residential District Heating Systems in Northern China

Heat metering is an important measure of China’s heating marketization reform as well as an energy-saving policy implemented in buildings nearly 20 years ago. However, this policy has not achieved the expected results due to various reasons. It is important to note that although northern China is dominated by large apartment buildings, resulting in unique thermal characteristics and user behavior, the measured results of these characteristics are not common. In this study, data from cities in northern China were obtained using household heat meters and field testing. Based on the three levels of station, building, and household, the effects of location, heat outages, and users’ regulation behavior on energy consumption were analyzed. The results show that different locations lead to a considerable difference in heat consumption among users, and an outage leads to a significant increase in consumption by surrounding users. For typical buildings in northern China, it is challenging to realize energy savings with district heating systems based on user behavior, and household metering and charging are not fair and reliable. Adjusting the heat meter unit from the household to the building was suggested.

Disaggregation of total energy use into space heating and domestic hot water: A city-scale suited approach

This paper develops a computationally fast algorithm to disaggregate the total energy use recorded by smart heat meters into space heating (SH) and domestic hot water (DHW). The algorithm trains a regression model on SH-only hours and predicts SH for all other hours with potential DHW use. Data from smart water meters were used to identify hours with only SH. Assessing 13 regression models, an untuned random forest was identified as the best-performing regression model with an acceptable computational cost (median: 7.4s per building). Furthermore, using one year of data from over 2400 single-family homes, it was shown that the best result (median CVRMSE: 0.13) can be obtained using only smart heat meters based regressors, increasing the applicability of the developed method. Furthermore, two tests were implemented, and it was successfully demonstrated that they identify situations where the regressors are distributed differently for hours with SH only and hours with SH and DHW, to identify possible training bias; thus, buildings where the disaggregation is potentially unreliable. Validation against data from three single-family houses with known SH and DHW use confirmed the good performance of the method and that the performance can be estimated without ground truth data via nested cross-validation.

Efficiency Evaluation of Evacuated Solar Tube Collectors in Sakha Republic (Yakutia)

Calculations of solar radiation incident on an inclined surface were performed considering the installation location, local time, azimuth angle, tilt angle, atmospheric transparency coefficient and surface albedo. The calculations were based on long-term meteorological observation archives. The accuracy of the solar radiation calculations for the inclined surface was verified. A comparative analysis was conducted between the calculation results carried out for the installation location, tilt angle, and azimuth angle of the experimental setup and pyranometer measurements. Thermal energy production by evacuated solar tube collectors was also calculated. These calculations were performed on an hourly basis, taking into account the total solar radiation falling on the inclined surface and the atmospheric air temperature. The calculated values were compared with experimental data obtained from an ultrasonic heat meter. The payback period of solar heating systems for various locations was used as an efficiency indicator for the solar collectors. The article estimated the payback period for Verkhoyansk town in the Sakha Republic (Yakutia). Economic efficiency strongly depends on the installation location of the solar collector and the thermal energy tariff at the installation site.

EXPERIENCE OF A STAGE-BY-STAGE RETROFITTING OF THE GENERAL HOUSE VERTICAL HEATING SYSTEM INTO IN APARTMENT HORIZONTAL

The article considers the main mistakes in the design and construction of district heating systems, presents the problems of residential buildings in Ukraine built in the 60-90s of the last century, which unlike modern residential buildings with horizontal two-pipe regulated systems, are equipped with one-pipe vertical non-regulated heating systems with cast iron radiators or steel convectors. They have no shut-off and regulating valves, no individual regulation of heat consumption by each flat and actually turns into one heating device, where heat consumption can be regulated only by gate valves at the building inlet. As a consequence, due to the imperfect design of heating systems, 80% of the housing stock in Ukraine do not have the possibility of regulated, economical heat consumption. The article considers the practical experience of retrofitting of the general house vertical unregulated heating system into a regulated apartment horizontal heating system with its own commercial heat metering devices. The need for such a phased reconstruction of the heating system by replacing the old building-wide vertical one-pipe system with a two-pipe horizontal one is dictated by economic considerations for the consumption of fuel and energy resources, the requirements of modern standards and, most importantly, the creation of a real opportunity for all subscribers and suppliers to regulate the production of heat energy. Objective economic analysis shows that the cost of retrofitting of the heating system into a regulated apartment horizontal heating system is cheaper than autonomous heating with individual gas boilers. Reconstruction of in-house heating systems is an objective necessity, without which it is impossible for the subscriber to have a real influence on his own heat consumption.

Radiators Adjustment in Multi-Family Residential Buildings—An Analysis Based on Data from Heat Meters

Energy is consumed in buildings through the use of various types of energy systems, which are controlled by the occupants via provided interfaces. The quality of this control should be verified to improve the efficiency of the systems and for the comfort of the occupants. In the case of residential buildings, due to privacy reasons, it is problematic to directly monitor human–building interactions using sensors installed in dwellings. However, data from increasingly common smart meters are easily available. In this paper, the potential use of data from heat meters is explored for the analysis of occupant interactions with space-heating (SH) systems. A pilot study is conducted based on a one-year set of daily data from 101 dwellings. First, the identification of an indoor temperature and a strategy for thermostatic radiator valve (TRV) adjustments for all the investigated dwellings is presented. Second, the performed analysis suggests that 96% of the households did not use the automatic adjustment function of the TRVs since adjustments using the on–off mode were the most common, which could be empirical evidence for Kempton’s theory on mental models of home heating controls. The reasons for this could be the weakness of the TRV as an SH interface and the technical specificity of the analyzed SH (its supply temperature). The preliminary investigation confirms the potential of the proposed methodology, but further research is needed.

Estimating residential space heating and domestic hot water from truncated smart heat data

The EU aims to digitize the building stock across all member states to better understand energy use and achieve energy efficiency goals to address climate change. Smart heat meters are currently used for billing purposes in district heating (DH) grids. Their data is recorded as integer kWh values, which restricts usability for the modeling and analysis of DH networks. Previous research devised a methodology to estimate space heating (SH) and domestic hot water (DHW) energy from total heating data, but the data truncation process reduced accuracy. This study integrates the SPMS (Smooth–Pointwise Move–Scale) algorithm, which estimates decimal values from DH truncated measurements, to improve the accuracy of the DHW and SH disaggregation methods. The study applies these two methodologies to a dataset of 28 Danish apartments and compares the results against full-resolution and truncated data to evaluate performance. Another dataset, named “optimal dataset” is also assessed to determine overall estimation accuracy. Results show that SPMS reduces the disaggregation methodology error of SH and DHW compared to the truncated data. The optimal dataset outperforms the current methodology, indicating a potential for improving and scaling the methodology for larger datasets.

Analysing energy use clusters of single-family houses using building and socio-economic characteristics

Clustering has been shown to be a promising approach to reduce the large amount of data from smart heat meters to representative profiles. However, attempts to understand why a case (building including its occupants) is within a particular cluster have only been moderately accurate. Therefore, this work uses existing energy use clusters based on about 4500 single-family homes to investigate whether socio-economic characteristics (SECs) alone or in combination with building characteristics (BCs) can improve the insight into the energy use clusters. An established variable selection and classification approach based on random forests was used. The results show that the eight SECs used alone provide poor insight into the energy use clusters, achieving only a Matthew Correlation Coefficient (MCC) of around 0.1. Simplifying the energy use clusters based on similarities, which was successful in the past, only moderately increased the MCC (≈ 0.17). When combined with BCs, SECs were never selected by the algorithm used, showing that they do not lead to a (significant) increase in MCC for both unsimplified and simplified clusters. Thus, this work suggests that SECs do not provide additional insights into why a case is within its respective energy use cluster.

Heat pumps on exhaust air for space heating and domestic hot water, in very high energy performance multifamily building (Geneva, Switzerland): feedback in actual condition of use

This paper, which is based on a monitored case study, analyses the issues and potential of heat recovery on exhaust air in new multi-family buildings, for combined DHW and SH production by way of heat pumps, in combination with a gas boiler for peak loads. Analysis of the hydraulic diagram, as well as readings of heat and energy meters, allows to point out the causes of an actual HP share (38%) much lower than the projected value (61%), while bringing up the necessity to elaborate standard schemes and sizing rules for such systems.

Research and development of data compression methods for technical monitoring systems

Objective. The aim of the study is to develop methods for data compression in relation to monitoring systems.Method. The studies were carried out mainly experimentally, by processing data generated by the information-measuring system of the Altai State Technical University in the process of meteorological observations, temperature control and control of the consumption of such energy resources as hot and cold water, heat supply. However, when it was proposed to change their structure and presentation form to compact the stored data, the degree of compression was found theoretically.Result. The results of studies conducted with the participation of the author of the publication in the field of data processing, transmission and storage are presented. Methods for streaming data compression “on the fly” as it arrives, both lossy and lossless, as well as methods based on optimizing the structure of databases designed to store information collected during monitoring, are considered. The main attention is paid to the consideration of data compression created in the process of temperature monitoring, since it is the control and monitoring of temperature processes that is most common in various technical process control systems, heat metering systems both on the consumer side (housing and communal services) and on the side of the heat supply organization.Conclusion. In the vast majority of cases, lossless compression allows you to compress the information received from the monitoring system by more than 10 times, and lossy compression is much stronger, and without losing the pragmatic value of the stored information.

Explainable district heat load forecasting with active deep learning

District heat load forecasting is a challenging task that involves predicting future heat demand based on historical data and various influencing factors. Accurate forecasting is essential for optimizing energy production and distribution in district heating systems. However, most existing forecasting models lack transparency and interpretability and fail to capture the spatial–temporal dependencies in the data. Moreover, they often require a large amount of annotated data for training, which can be costly and time-consuming to obtain. In this paper, we present a novel approach to district heat load forecasting, which involves predicting future heat demand based on historical data and various influencing factors. The proposed approach is based on an Active Graph Recurrent Network (Ac-GRN), which leverages the strengths of active deep learning and graph neural networks to capture the complex spatial–temporal dependencies in the data. The approach also provides explainability for its predictions by using correlation-based attribution methods. The active deep learning component can effectively select the most informative and representative samples from a large pool of data, reducing the frequency and cost of data collection and human effort. The graph neural network component can model both linear and nonlinear relationships among heat meters using bidirectional recurrent connections, enhancing the accuracy and robustness of the predictions. We conduct extensive experiments and compare our approach with eleven state-of-the-art models on a real-world dataset of district heating consumption data from Danish residential buildings. Our results show that our approach outperforms other models in terms of accuracy, robustness, reliability, and computational efficiency for multi-horizon multi-step district heat load forecasting. Our approach also provides meaningful explanations for its predictions by highlighting the most influential factors and heat meters for each prediction. This paper makes a novel contribution to district heat load forecasting with explainability.

Energy justice in heat metering: Findings from a Danish experiment of metering and distribution in residential apartment buildings

According to EU regulations on heat metering, households should be metered individually and pay accordingly. However, in apartment blocks, heating is often provided for the whole building, and different ways of distributing costs are used. In Denmark, an investigation has been carried out into new ways of distributing costs, using indoor climate measurements of temperature, CO2 and humidity. Incorporating these three types of measurements in payment distribution can create an incentive to encourage residents to pursue a healthier indoor climate with lower energy consumption. This paper reports on qualitative interviews with residents as well as housing staff taking part in the experiment, relating empirical findings to questions of justice and ethics raised in the literature. Analysis reveals differing levels of understanding among residents concerning metering systems and rationales, including in relation to cost distribution and questions of justice. The experiment was carried out in social housing blocks where some residents could be categorised as vulnerable in terms of limited income and specific heating needs. Furthermore, issues of global climate and justice from an intergenerational perspective are also considered. This paper therefore draws on wider discussions about energy justice and vulnerability related to types of heat metering, and shows how these issues are interlinked with the socio-material context and the interplay with socio-technical constructions of comfort norms. The relevance of country-specific insights are thus demonstrated at a European level and beyond.

A hierarchical classifying and two-step training strategy for detection and diagnosis of anormal temperature in district heating system

Anormal temperature data caused by various reasons such as sensor faults and operation faults, which has a negative influence on heat metering and operation regulation in district heating system (DHS). However, it is difficult to detect and diagnose anormal temperature among massive unlabeled operation data. Therefore, this paper proposes a novel hierarchical classifying and two-step training strategy to facilitate the anormal temperature detection and diagnosis task. Firstly, self-defined feature change rate of operation data like water temperature, flow rate, and valve opening are constructed as additional training features to capture the characteristics of anormal temperature conditions. Then, a hierarchical classifying method is proposed to detect anormal temperature data. Finally, a two-step training strategy which combines expert knowledge with support vector machine (SVM) to fulfill anormal temperature type diagnosis. The proposed strategy is applied to a typical DHS in cold region of China. A total of 10,920 anormal data are detected. Four anormal temperature conditions are diagnosed including offline sensor, inversely connected sensor, anormal operation of heat source, and shutdown of heat station. The diagnosis accuracy for the 4 kinds of anormal temperature conditions all reached over 98%.

Study of the influence of the angle of inclination on the thermal resistance of glass light structures of agricultural buildings

The paper presents the results of studies of the influence of the angle of inclination on the thermal resistance of glass light structures of agricultural buildings. For experimental studies, a special refrigerating chamber was used, in which one of the walls is made in the form of a double-glazed window. The camera tilted towards the light structure. With the help of heat meters installed on the surface of the glass, the value of the heat flux was measured. Both single-chamber and double-glazed windows were tested. Based on the experimental data obtained, a calculated dependence is proposed for determining heat losses through external inclined light fences, including double-glazed windows.

Exploratory Data Analysis for Building Energy Meters Using Machine Learning

The purpose of this research was to apply exploratory data analysis techniques to building energy meters, such as electricity, cold, heat, and steam meters. A thorough understanding of energy usage patterns becomes increasingly vital in an era of growing awareness of energy management and sustainability. Trends, patterns, and anomalies can be identified in building energy meter data using meticulous data exploration approaches, which can give significant insights for increasing energy efficiency. Exploratory data analysis combined with machine learning approaches may be was used to reveal hidden patterns of energy usage and examine the links between relevant factors. The findings of this exploratory data analysis gave vital insights into building energy use trends. Some significant and hidden information that was crucial for understanding energy usage within a certain time frame in each building was discovered via the investigation of the data used in this study. Steam had the highest use, whereas electricity had the lowest. Utilities were more popular before 5 a.m., followed by healthcare, with daytime use hours beginning around 10 a.m., depending on the area. During the working day, the industry needs more energy. Places of worships use more energy on weekends. There was a significant relation between the number of floors and spaces per level of a building and the height meter reading between May and October. There is a significant association between the kind of buildings used for schools, workplaces and high energy use. This study significantly contributed to the management of the energy and sustainability domains. Using exploratory data analysis and machine learning approaches to building energy meters could optimize energy usage, minimize running costs, and enhance overall energy efficiency. This research is still very open to be continued using other methods, to obtain other hidden information.

Evaluating Reduction in Thermal Energy Consumption across Renovated Buildings in Latvia and Lithuania

Currently, the optimization of thermal energy consumption in buildings is considered a suitable alternative in the construction of new buildings, as a result of which the overall energy efficiency of the building increases. Thus, this study examined the efficiency and efficacy of different building renovation packages conducted across several buildings in Latvia and in Lithuania (across a larger building stock). In the first section of this study, 13 multi-apartment residential houses with 3 building renovation packages have been investigated in the city of Daugavpils, Latvia, in order to determine the actual reduction in heat energy consumption across each of the renovation implementation packages. The study findings indicate that changes in Latvian building regulations regarding insulation thickness did not significantly impact thermal energy consumption in fully renovated buildings. However, the combination of facade renovations, upgraded heating systems, and improved ventilation systems resulted in substantial energy savings, with an average reduction of 50.59% in thermal energy consumption for space heating across the reviewed multi-apartment residential building stock. In the following section of this study, the impact of the Energy Performance of Buildings Directive (EPBD) on building energy efficiency in Lithuania has been examined. The results show that over a 10-year period in the 2000s, Lithuanian building stock experienced a 20% increase in energy efficiency, followed by an additional 6.3% increase between 2010 and 2016. The mandatory requirement for renovated buildings to achieve a minimum energy efficiency class has resulted in significant reductions in energy consumption for heating purposes. The findings underscore the effectiveness of building renovation packages and the EPBD regulations in enhancing energy efficiency and promoting sustainable building practices. The importance of heat metering, consideration of indoor air temperature, and the need to address indoor air quality during renovations were also highlighted.

Comparison of calculation and consumption methods for determining Energy Performance Certificates (EPC) in the case of multi-family residential buildings in Poland (Central-Eastern Europe)

The calculation method and the consumption method are used to award Energy Performance Certificates (EPC), in accordance with the Energy Performance of Buildings Directive (EPBD) 2010/31/EU with revisions. The calculation method can be used for any type of building, regardless of the type of heat source. A good knowledge of the building is required (architectural and construction documentation, inventory of the building). The consumption method can be used only in buildings with heat and gas meters. It is not necessary to have detailed information about the building. However, this method is not applicable to offices, hotels, or production buildings with periodic breaks in heating and hot water consumption. Here, we apply the calculation method and the consumption method to analyse 15 multi-family residential buildings located in Poland (Central-Eastern Europe). Both methods gave similar values. Using the consumption method the energy indicators were slightly lower than using calculation method (on average, the results differed by approx. 14–17%). The consumption method provides a useful alternative to the calculation method, and can be tested by the scientific and expert community in other countries where it has not yet been implemented.