Building Heating Systems Research Articles

RECONSTRUCTION OF HEATING SYSTEMS IN RESIDENTIAL BUILDINGS WHILE MAINTAINING EXISTING HEAT SOURCES

The purpose of the article is to substantiate the need for real reconstruction of single-pipe vertical centralized heating systems, which are operated in most multi-story residential buildings in Ukraine, into apartment horizontal systems. The paper presents the main design shortcomings of heating systems in residential buildings built in the second half of the 20th century. The feasibility of using horizontal apartment systems is substantiated. For the first time, the heating system was reconstructed during the heating season without displacing residents and without stopping the existing heating system. A technical solution is proposed for supplying heat to apartments from an existing individual heating station with an elevator without its reconstruction. The article presents the results of a number of studies aimed at improving the heating system and determining the indicators of its economic and energy efficiency. Installation work can be carried out selectively in any number of apartments, and "old" and "new" heating systems can operate in parallel. The methodology for the reconstruction of heating systems presented in this paper can be used in the process of post-war restoration of the destroyed infrastructure of the housing stock of Ukraine. Modernizing in-building systems while maintaining the existing heat source is much cheaper than decentralizing heat supply by installing independent heating systems with a gas boiler in each apartment. The main conclusions of the article include that the reconstruction of existing heating systems is an objective necessity, without which the subscriber's influence on his heat consumption is impossible. The modernization described in this article is dictated by all the regulatory deadlines for major repairs of heating systems, which in most cases are more than twice as long.

Impact of practical challenges on the implementation of data-driven services for building operation: Insights from a real-life case study

Data-driven applications in buildings using AI and machine learning have generated a lot of interest, but scaling these applications is challenging due to the uniqueness of each building. During the process of implementing a data-driven predictive heating control in a full-scale real-life office building in Norway, 24 practical challenges were encountered. In this work, those practical challenges are presented, discussed and attributed to four main categories: i) physical limitations, ii) data acquisition and communication, iii) data and model definition and iv) building occupants. Detailed examples for the challenges are provided and more than 15 lessons-learned with regards to developing and implementing data-driven services for building operation are presented. Furthermore, this work discusses how the practical challenges impact the choice of a data-driven approach to control the operation of an office building heating system in a predictive manner and how the practical challenges influence the creation of variation in the measurement data needed to identify a model during normal building operation. Finally, it is shown that a substantial number of practical challenges that were encountered during the operational phase are rooted in the design and construction phase of a building project or from rehabilitation during the operational phase. This highlights the fact that the possible use of data-driven services for building operation should be considered during the tendering and design phase to minimize the number of challenges regarding the widespread implementation of data-driven services for building operation, especially regarding predictive control.

Solution-Processed Metallic Nanowire Network for Wearable Transparent Thermal Radiation Shield.

Heating requirements for residential and commercial dwellings result in significant energy consumption and deleterious environmental effects. Personal radiative thermal management textiles regulate the wearer's body temperature by controlling the material's intrinsic optical properties. Passive heating textiles suppress radiative heat losses and therefore significantly reduce the energy consumption required for building heating systems. Guided by an optical theoretical approach, a transparent radiation shield (TRS) is designed based on silver nanowires (AgNWs) that can suppress human body heat with simultaneous visible light transmittance anticipated for practical fabrics. We experimentally demonstrated a TRS with large infrared light reflectance (low emissivity of 35%) and a visible (VIS) transparency value of 75% (400-800 nm). The results are well corroborated by the Mie scattering theory and the wire-mesh equivalent sheet impedance model, which provide fundamental mechanism understanding and guidance toward higher performance. The TRS is fabricated by a simple, solution-processing method with thermoplastic elastomer protective layers, granting notable stretching capabilities, mechanical robustness, and conformability to any body shape or object. The rigorous theoretical strategy enables the scalable synthesis of low-emissivity and visibly transparent textiles for personal thermal comfort.

Enhancing operational optimization of district heating substations through refined estimations of network campus buildings heat demands to achieve a low return from the network

Network temperatures in district heating systems are important operational factors for obtaining efficient performance. A low network return temperature allows for the recovery of low-grade heat from assets such as condensing boilers, waste incineration, geothermal sources and industrial waste heat. Fluctuations in heating and cooling demands affect the return temperatures of the building substations and in the network. This variability impacts the economic viability and environmental sustainability of the entire system. This paper presents a nonlinear optimization strategy to maintain sufficient energy flows in the network's primary and secondary circuits to achieve low return temperatures from all substations in the network. The defined optimization strategy incorporates the thermodynamic model of the substation and building heating system as opposed to traditional weather-based supply temperature adjustments. The estimated heat demands and tariffs, ▪ penalties are inputs used by the optimizer to find the optimal solution. The total operational expenditure for electricity and gas consumption shows an 18% reduction with 8% reduction in emissions and 6% efficiency improvement when compared with the measured weather-based approach. The developed strategy will aid the network operators in the economic dispatch of heat generation while ensuring the user's thermal comfort.

Environmental footprint analysis of domestic air source heat pumps

Air source heat pumps (ASHP) are increasingly being recognised as a low carbon alternative to traditional fossil fuel type heating systems for residential buildings, with many countries targeting their mass deployment to meet their emissions reduction goals. However, the environmental impacts of ASHPs throughout their entire life cycle to include manufacture and end-of-life, as well as operation, have not received comprehensive attention to date. This study addresses this gap by conducting a review of research quantifying the life cycle impact of domestic ASHPs, coupled with an examination of technology uptake and deployment. By analysing the entire life cycle, from production to end-of-life, the global warming potential of a 6 kW ASHP is estimated to be 35.8 t CO2 equivalent over the lifetime of 17 years identified by the study. Additionally, the study conducts a comparative analysis of the operational footprint based on a range of electricity carbon footprint and suggests possible improvement of 1200 % with targeted improvments. Furthermore, the study calculates the global warming potential using both the seasonal performance factor specified by a heat pump manufacturer and real-world field trial data. The findings unveil a significant disparity of 20 % between the two methodologies, underscoring the paramount importance of incorporating in-field heat pump performance data when evaluating their environmental impact.

A novel heating strategy and its optimization of solar–air source heat pump heating system for rural buildings in northwest China

In the rural areas of Northwest China, the utilization of clean and renewable energy is deemed a crucial measure for reducing building energy consumption and environmental pollutant emissions. This paper focuses on constructing a simulation platform for a solar-assisted air source heat pump heating system. A rural residential building in Yongshou County, Shaanxi Province, serves as an illustrative example. A novel flexible temperature control method with a feedback controller in sub-area and period is proposed in this paper, alongside the selection of three distinct objective functions aimed at optimizing the heating system. The simulation results indicate an average temperature of 17.0 °C throughout the heating cycle, with a peak temperature of 18.7 °C. Moreover, the solar fraction is measured at 25.11%, underscoring the significance of collector area and heat storage tank volume as primary factors in system design. The results also demonstrate that across various optimization objectives, the life cycle cost optimization scheme yields greater economic benefits, while the target building unit heating cost optimization scheme boasts the shortest static payback period and lowest unit heating cost. Conversely, the solar fraction optimization scheme stands out for its superior environmental benefits. These findings offer valuable insights for the design of heating systems tailored to diverse objectives.

Experimental Study on a Photovoltaic Direct-Drive and Municipal Electricity-Coupled Electric Heating System for a Low-Energy Building in Changchun, China

This paper takes a low-energy building in Changchun, China, as an object to test and study the characteristics of two heating modes, AC/DC (Alternative current/Direct current) switching and AC/DC synthesis, from the perspectives of temperature change, irradiation intensity, power generation, electricity consumption, etc. Firstly, the experimental research was conducted under two heating cable modes by establishing mathematical models and a test rig, and it was found that the photoelectric conversion efficiency on sunny, cloudy, and overcast days was 18%, 14.5%, and 12%, respectively. A simulation model was established by TRNSYS to run an ultra-low-energy building throughout the year. It was found that the highest and lowest monthly power generation occurred in February and July, respectively. The annual power generation of the system was 6614 kWh, and the heating season power generation was 3293.42 kWh. In the current research, the DC electricity consumption was slightly higher than the AC electricity consumption. Under conditions of similar radiation intensity and power generation, the indoor temperature of the AC/DC synthesis cable heating mode were 1.38% higher than the AC/DC switching heating able mode, and the electricity consumption were 10.9% and 4.76% higher, respectively, than those of the AC switching heating cable mode. This is of great significance for clean-energy heating, energy savings, and emissions reduction in northern China.

Two-Stage Solar–NaOH Thermochemical Heat Pump Heating System for Building Heating: Operations Strategies and Theoretical Performance

Heating buildings with solar energy is challenged by the seasonal mismatch between solar availability and heating demand. Thermochemical energy storage is a promising technology to overcome this challenge because of its high energy density. In building applications, space requirement is also an important consideration. Therefore, both the storage space and collector areas are important considerations, with only the latter often being neglected in previous studies. This paper proposes a novel two-stage thermochemical heat pump heating system based on the working pair of NaOH/H2O. We demonstrate that this system can work with a concentration difference (70% wt–30% wt) for the climate in hot summer and cold winter regions in China. The energy storage density based on the discharged solution is 363 kWh/m3. With this solar-driven thermochemical heat pump heating system, 35.13 m2 of collectors and 10.48 tons of 70% wt NaOH solution are sufficient to complete a full charge–discharge cycle and meet the heating demand of a single-family house (winter space heating + DHW: 9370 kWh, summer DHW: 2280 kWh). The theoretical maximum storage for solution (discharged + water tank) is 32.47 m3. Compared with the sensible seasonal storage alternative, the collector area is reduced by 12.5% and the storage space is reduced by 59%, with a possible further reduction through optimization. With the potential to be further optimized for space saving, the two-stage solar–NaOH heat pump heating system is an energy-efficient and space-efficient heating system for buildings in the hot summer and cold winter regions of China.

Research on optimization of the heating system in buildings in cold regions by energy-saving control

Building heating is an indispensable part of people's winter life in cold regions, but energy conservation and emission reduction should also be taken into account during the heating process. This paper provides a concise overview of the heating system based on air-source heat pump radiant floor and its control strategy. It also optimizes a control system based on thermal comfort and energy efficiency ratio, and analyzes a room in Xining City, Qinghai Province, to test the heating system performance under two control strategies. The final results show that under the traditional control strategy, the cumulative working time of the heating system within a day was 15 hours, the average indoor temperature was 17.36℃, the temperature standard deviation was 2.08℃, and the average power consumption was 189.6 kWh. Under the improved control strategy, the cumulative working time of the heating system within a day was reduced to 10 hours, the average indoor temperature was 18.56℃, the temperature standard deviation was 0.92℃, and the average power consumption was 132.5 kWh.

Advancing energy efficiency in livestock building: Simplified building energy simulation tool for geometric design of pigsty

Concerns related to food security and animal welfare have led to increasing interest in analyzing the energy demands of heating, ventilation, and air conditioning (HVAC) systems in livestock buildings. However, building energy simulation (BES) model, which facilitates precise HVAC systems energy analysis, is difficult to use for non-experts, and particularly, its usability drops significantly in livestock buildings, which are often constructed without involvement of building engineers. This study, therefore, developed a tool that can analyze the energy demand of pigsties by changing geometric design variables with only several numerical inputs without separate 3D modeling. Pigsty geometric design types were classified by examining pigsty architectural design manual and web portal map search. A simplified tool, utilizing EnergyPlus and MATLAB graphical user interface, was developed to analyze energy demand in pigsties by leveraging simple user inputs for geometric design modeling. This tool simplifies exploring potential energy savings through geometric design changes in the early design stages of livestock buildings. The methodology introduced in this study lays the groundwork for future enhancements, including the integration of various design-phase variables beyond geometric design.

Demand response for residential building heating: Effective Monte Carlo Tree Search control based on physics-informed neural networks

To reduce global carbon emissions and limit climate change, controlling energy consumption in buildings is an important piece of the puzzle. Here, we specifically focus on using a demand response (DR) algorithm to limit the energy consumption of a residential building's heating system while respecting user's thermal comfort. In that domain, Reinforcement learning (RL) methods have been shown to be quite effective. One such RL method is Monte Carlo Tree Search (MCTS), which has achieved impressive success in playing board games (go, chess). A particular advantage of MCTS is that its decision tree structure naturally allows to integrate exogenous constraints (e.g., by trimming branches that violate them), while conventional RL solutions need more elaborate techniques (e.g., indirectly by adding penalties in the cost/reward function, or through a backup controller that corrects constraint-violating actions). The main aim of this paper is to study the adoption of MCTS for building control, since this (to the best of our knowledge) has remained largely unexplored. A specific property of MCTS is that it needs a simulator component that can predict subsequent system states, based on actions taken. A straightforward data-driven solution is to use black-box neural networks (NNs). We will however extend a Physics-informed Neural Network (PiNN) model to deliver multi-timestep predictions, and show the benefit it offers in terms of lower prediction errors (−32% MAE) as well as better MCTS performance (−4% energy cost, +7% thermal comfort) compared to a black-box NN. A second contribution will be to extend a vanilla MCTS version to adopt the ideas applied in AlphaZero (i.e., using learned prior and value functions and an action selection heuristic) to obtain lower computational costs while maintaining control performance.

Research on the optimization of the clean heating system for regional residential buildings based on the multi-objective optimization strategy

For the residential building district heating (RBDH) system, choosing the appropriate combination of heat sources according to local conditions is the key to improving economic efficiency. In this study, three climatic regions in China were selected, namely, a hot summer and cold winter region, cold region, and severe cold region. Among them, Nanjing, Tianjin, and Shenyang were selected as typical representative cities in the hot summer and cold winter region, cold region, and severe cold region, respectively. Taking the levelized cost of heat (LCOH) as the economic evaluation index and considering the carbon emission cost of the system operation, the energy consumption and the CO2 emissions were analyzed. TRNSYS software was used to simulate and analyze the system performance. The multi-objective optimization based on a genetic algorithm was proposed to optimize system parameters. From an economic point of view, the SA system was suitable for the hot summer and cold winter region, the SAS system was suitable for the cold region, and the SE system was suitable for the severe cold region. The operation control strategy based on quality adjustment can reduce heating energy consumption and maintain indoor temperatures at approximately 20°C. The proportion of clean energy heating in the optimized heating system decreased after the multi-objective optimization strategy. However, the initial investment and maintenance costs of the system were reduced, which reduced the LOCH of the system. Therefore, the multi-objective optimization strategy can effectively reduce the heating costs.

Development of measures to modernize the heating system in ESSUTM academic building No. 7 and evaluation of economic efficiency

Development of measures to modernize the heating system in ESSUTM academic building No. 7 and evaluation of economic efficiency

Low carbon operation optimisation strategies for heating, ventilation and air conditioning systems in office buildings

Heating, ventilation, and air conditioning (HVAC) systems play a crucial role in production environments. Optimising the control strategy of an HVAC system is an effective approach to achieving energy savings and reducing emissions in a production environment. In existing optimal control models for HVAC systems, occupant behaviour has been incorporated as a key influencing factor. However, model predictive control algorithms have not introduced to examine its performance in optimising HVAC system operation. An optimisation strategy considering occupant (leaders and users of HVAC systems) behaviour (OSOB) is proposed based on model predictive control algorithm. The concept of environmentally specific transformational leadership is introduced to characterise the behaviours of leaders. Then the distinct decisions driven by double objectives (carbon emissions and electricity costs) can be explained. The OSOB was applied to a representative office building in Beijing. The findings indicate that incorporating occupant behaviour into algorithm can lead to a significant reduction in carbon emissions, up to 45%, as well as a decrease in electricity bills, up to 17%. This study not only incorporates occupant behaviour as a significant influencing factor, but also offers valuable insights for product manufacturers seeking to reduce the carbon emissions produced during building operations.

ASSESSMENT OF THE IMPACT OF AUTONOMOUS RESIDENTIAL HEATING SYSTEMS ON AIR POLLUTION IN MUNICIPALITIES (CASE STUDY OF THE BAIKAL REGION)

The article suggests a method for calculating emissions from the autonomous heating systems (AHS) of individual residential buildings based on the assessment of the number and area of farmsteads using remote sensing data, the volume and type of fuel and the type of combustion according to population surveys data and data provided by the administrations of municipal districts and settlements. The importance of AHS as a source of atmospheric pollution was evaluated at three spatial levels: the regions of Russia, where the AHS are of the greatest importance, were identified according to the structure of fuel consumption; the emissions from the combustion of residential heating fuel were calculated for the municipalities of the Baikal natural territory (BNT) and the types of territories were identified according to the structure of emission sources; and the role of AHS in air pollution of the residential areas was assessed for settlements. AHS are the dominant air pollution source (over 90%) for most of the BNT territory. Unlike CHPPs and boiler facilities, emissions from individual residential buildings using coal and firewood in most cases significantly affect air quality (up to 4,9 MPC of particulate matter and sulfur dioxide), and the areas of such impact are limited to the residential area of settle-ments. Strategies to reduce pollution depend on the location of a territory, type of settlement, income levels, the current structure of fuel consumption, the presence of exploited coal deposits and other factors

Data and knowledge fusion-driven Bayesian networks for interpretable fault diagnosis of HVAC systems

Timely diagnosis and maintenance of faults in chiller units is beneficial for reducing energy consumption in buildings. In order to improve the diagnostic accuracy and provide reasonable explanations for fault occurrences of the heating, ventilation, and air conditioning (HVAC) systems in buildings, a fusion-driven Bayesian network fault diagnosis method is proposed based on broad learning system and fuzzy expert knowledge. Firstly, from the data-driven perspective, an efficient broad learning system is developed as benchmarking model to build the prior Bayesian network structure. Secondly, from the knowledge-driven perspective, the fuzzy logic system is used to fuzz expert knowledge, which then used to optimize the Bayesian network. Finally, the information obtained by experts on-site is incorporated into the optimized Bayesian network as new evidence nodes to determine the Bayesian network. The parameters of the Bayesian network are learned through Noisy-MAX processing of the conditional probability table. The performance of the proposed method is evaluated based on the fault diagnosis of a chiller system. The results demonstrate that the Bayesian network established through this method combines the advantages of data-driven and knowledge-driven approaches. It not only performs well in terms of diagnostic accuracy, with accuracy rates above 98 % for six typical faults, but also provides effective explanations for the underlying mechanisms of the faults through causal relationship diagrams.

In Situ Performance Analysis of Hybrid Fuel Heating System in a Net-Zero Ready House

The global population’s growth and increased energy consumption have driven greenhouse gas (GHG) emissions. In Canada, the residential sector accounts for 17% of secondary energy use and 13% of GHG emissions. To mitigate GHG emissions, promoting renewable energy and efficient heating systems is crucial, especially in cold climates like Canada, where there is a heavy dependency on fossil fuels for space heating applications. A viable solution is hybrid fuel heating systems that combine electric-driven air-source heat pumps (ASHPs) with natural gas tankless water heaters (TWHs). This system can alternate its operation between the ASHP and TWH based on efficiency and real-time energy costs, reducing grid peak demand and enhancing resilience during power outages. Although lab experiments have shown its benefits, in situ performance lacks evaluation. This study analyzes the in situ energy performance of a net-zero ready house and its hybrid fuel heating system, assessing energy consumption, hourly space heating output, and system heating performance. HOT2000 is a robust simulation software designed for assessing energy consumption, space heating, cooling, and domestic hot water systems in residential buildings. An artificial neural network model was developed to predict the energy performance of the hybrid fuel system, which was used as a substitute for monitored data for evaluating the HOT2000’s simulation results under the same weather conditions. Therefore, this study proposes a comprehensive framework for the in situ performance analysis of hybrid fuel heating systems. This study then, using HOT2000 energy consumption results, evaluates the life cycle costs of the hybrid fuel system against conventional heating systems. Furthermore, this study proposes an economical control strategy using in situ data or manufacturer specifications.

Modernization of local heat distribution stations of city heat supply system

The PURPOSE of this work is to develop technical solutions to increase the efficiency of functioning of centralized urban heating systems by modernizing local heat distribution stations. The article considers the issues of functioning of district heating systems with open water intake. The work presents technical solutions that increase the efficiency of the local heat distribution station with a hot water supply unit. A technological scheme has been developed, the selection of equipment has been made. The feasibility of using the developed technical solutions is confirmed by technical, economic and investment indicators. The objectivity of determining the technical and economic effect of the modernization of the local heat distribution station is ensured by comparing the obtained indicators of the modernized and similar facilities.METHODS. To fulfill the objectives of the study, the following methods were applied: full-scale tests on a real object; processing of experimental data using application programs; feasibility study in the municipal heat power industry.THE RESULTS show an improvement in the technical and economic performance of the heating and hot water supply system of an apartment building in Yoshkar-Ola city: reduced network water consumption by 36–39%; temperature drop in the return pipeline was 13.5%; the heat content of hot water has been reduced to 0.168 Gcal/m3 (normative heat content is 0.145 Gcal/m3), the required circulation was ensured in all risers of the hot water supply system. The total losses from October 2021 to September 2022 are 3486,4 Gcal or 11.4% of heat supplied to consumers, which is less than 13% set by the tariff. Compared to a similar house without a regulation depending on the outside air temperature, the consumption of thermal energy during the heating season is reduced by 80 Gcal (10.9%).CONCLUSION. The new scheme of a local heat distribution station has been proposed, including the regulation of domestic hot water (DHW) recirculation, as well as the installation of a hydroelevator with weather regulation. The developed technical solution is relevant for district heating systems with open water intake. For the first time, an experimental study of the effect of the joint installation of the following equipment was carried out: a pump and a control valve on the DHW recirculation line before the recirculation mixing unit with the return pipeline of the heating system; hydraulic elevator with weather regulation. The results of the study can be used in the feasibility study of similar circuit solutions for the modernization of DHW systems. Operating experience has shown that the application of the developed technological scheme of local heat distribution station has positive effects: reduction of network water consumption, temperature in the return pipeline and heat losses; improvement of circulation in all risers of the DHW system; reduction of thermal energy consumption during the period of the “lower” cutoff of the temperature graph; curbing the growth of tariffs for thermal energy.

Thermal evaluation and optimization of a building heating system: radiant floor coupled with a solar system

Thermal evaluation and optimization of a building heating system: radiant floor coupled with a solar system

Multi-objective deep reinforcement learning for a water heating system with solar energy and heat recovery

Deep reinforcement learning (DRL) has gained attention from the scientific community due to its potential for optimizing complex control schemes. This study describes the implementation of a DRL platform that allows training smart agents to manage a complex water heating system in an institutional building that uses solar energy and waste heat from a water chiller as energy sources. In addition to optimizing the use of energy while delivering hot water, the agents are also trained to activate the chiller, to avoid the premature degradation of the system, and to continue providing hot water in case of failures of heating devices of the system. The definition of the reward function, which is fundamental to simultaneously impose all these goals on the agent, is tested by comparing different agents trained to prioritize different goals. In comparison with a previous study done on the same system with standard reinforcement learning techniques, this method allows far more freedom to control the system. The deep neural networks and the DRL algorithm were programmed without specialized libraries, implying that the algorithm could be used to train smart agents in programs without direct access to deep learning libraries, or in the actual system with a simple programmable controller.