Building Heat Demand Research Articles

Performance investigation of a solar-driven cascaded phase change heat storage cross-seasonal heating system for plateau applications

The mismatch between solar radiation resources and building heating demand on a seasonal scale makes cross-seasonal heat storage a crucial technology, especially for plateau areas. Utilizing phase change materials with high energy density and stable heat output effectively improves energy storage efficiency. This study integrates cascaded phase change with a cross-seasonal heat storage system aimed at achieving low-carbon heating. The simulation analyzes heat distribution and temperature changes from the heat storage system to the heating terminal. The results indicate that although the solar collectors operate for 26.3% of the total heat storage and heating period, the cumulative heat stored is 45.4% higher than the total heating load. Heat transferred by the cross-seasonal heat storage system accounts for up to 61.2% of the total heating load. Therefore, the system reduces fuel consumption by 77.6% compared to conventional fossil fuel heating systems. Moreover, radiant floor heating terminals, with a wide range of operating temperatures, match well with cascaded phase change heat storage and can reduce operation time by 19.5% and heat demand by 5.2% compared to conventional radiators. In addition to demonstrating the feasibility of applying cascaded phase change technology in cross-seasonal heat storage heating, this study reveals the lifecycle sustainability due to the shortened heat storage period. The configuration, parameters, and simulation results provide a reference basis for system application and design.

Decarbonising well-insulated buildings in a warming climate: The case of adaptive thermal comfort with geothermal space heating

Buildings contribute to 40% of global energy demand and are responsible for a third of CO2 emissions. Strategies to decarbonise buildings are, therefore, crucial for reaching the net-zero emissions targets by most of the countries in the world. This study investigates the feasibility of decarbonising well-insulated buildings with current and future weather scenarios by applying adaptive thermal comfort strategy in combination with a closed-loop deep geothermal system. An office building has been chosen as a case study because of the comprehensive sensor system installed in the building which allows for detailed data extraction and analysis. The results show that the building’s heating and cooling demand can be reduced by 51% using an adaptive thermal comfort approach, while this decreases to 45% in 2080 under a warming climate. Combined with this adaptive approach, geothermal energy can satisfy 50% of building heating demand at present when combined with intermittent storage scenarios, while 5 deep borehole heat exchangers (DBHEs) would be required to meet all the heating demand directly. In conclusion, this study shows that using a DBHE and adaptive thermal comfort approach to well-insulated buildings presents an innovative and low-cost opportunity for buildings to significantly lower building thermal demand and carbon emissions.

Modeling of a PV/Thermal Hybrid Panel for Residential Hot Water System

Abstract Despite the extensive body of research on PV/thermal systems, a gap remains in evaluating their performance in residential settings. This study aims to bridge this gap by focusing on the energy modeling of a PV/Thermal (PVT) hybrid panel that incorporates heat pipe technology. The evaluation is conducted through MATLAB code to assess the system's capability to fulfill the electricity and heating demands of residential buildings. The model's reliability is affirmed by comparing it with experimental data from a PVT panel tested in Sydney, exploring the transient variations in both water heat gain rates and power generation. The model's precision is evident from the percentage of error in the estimated temperatures of the PV panel based on the test results under various weather conditions, which ranged from −8% to 6%. This method was also utilized to determine the overall energy efficiency of the PVT under different climatic conditions. The results reveal that the overall energy efficiency of the proposed PVT panel, on a typical day, is approximately 45%, significantly outperforming traditional PV panels by more than double. Furthermore, the payback period for a typical residential PVT system, providing both hot water and electricity, is found substantially shorter than that of installing separate PV and solar hot water systems, highlighting the economic and environmental benefits of the proposed hybrid system.

Occupant-centric dynamic heating demand in residential buildings based on a temporal-spatial combined quantification method

The occupant-centric Part-Time-Local-Space heating mode holds paramount significance for the carbon neutralization of buildings in China. It ensures a harmonious match between heat supply and personal demand in both temporal and spatial dimensions, effectively reducing space heating consumption. However, current studies predominantly focus on the temporal-spatial heating demands of occupants at the room scale, but the zonal scale heating demands remain inadequately explored. Thus, we developed a temporal-spatial combined quantification method to analyze residents’ indoor movement characteristics. It aims to identify the localized subzones in a room where residents have prolonged stays, with spatial boundaries of subzones entirely defined based on high-resolution positioning data. Using this approach, we analyzed the temporal-spatial heating demands of four households under single- and multiple-resident scenarios in typical rooms. Furthermore, we discussed the differences in demand between different heating modes. Results revealed distinctive “local space” demand features in living room and “part time” demand features in dining room. The areas of sofa subzones generally occupied less than 15 % of a room (within 4 m2), and stay durations in dining-table subzones ranged from 0.3 h to 1.2 h. Considering the entire monitored rooms, residents occupied only 4.1–12.1 % in space with 10.7–47.9 % in time per day, highlighting the significant energy-saving potential of the occupant-centric Part-Time-Local-Space heating mode. This evaluation provides a reference for the design and implementation of personalized environmental control system.

Allocation optimization of two-stage compression-absorption heat exchanger

Space heating demand of buildings is a significant component of global energy consumption. Lowering the operating temperatures can increase the overall energetic and exergetic efficiencies. To obtain a lower return water temperature, researchers have proposed a variety of approaches. Absorption heat exchanger is an effective way to reduce return water temperature. However, in two-stage absorption heat exchanger and compression-absorption heat exchanger, extra temperature difference exists in evaporator of absorption heat pump. To eliminate the extra temperature difference, optimized compression-absorption heat exchanger is put forward in the article. Based on the typical case, the energy saving rate of optimized compression-absorption heat exchanger can reach 17.9 %, and the principle of energy saving is analyzed in the article, the key point is the extra temperature difference and the cooling capacity of evaporator of absorption heat pump. Further, the energy savings of the optimized heat changer in different supply and return temperature of primary and secondary network is analyzed. Main factors affecting the energy saving rate are primary network supply temperature and secondary network supply-return temperature difference. As the result, when primary network supply temperature is 130 °C and secondary network supply and return temperature is 50 °C and 40 °C, energy saving rate can reach 28.4 %.

Comfort in cold: A novel approach to sustainable building energy efficiency

Kyrgyzstan's high-altitude rural housing sector consumes 3–5 times more energy than European buildings due to ageing infrastructure, lack of insulation, and reliance on non-sustainable resources. One potential solution is the implementation of thermal insulation. However, due to limited public awareness of energy efficiency, inadequate government policies, insufficient technology, and challenging geography, people in rural areas rely on non-sustainable resources such as coal, cow dung, and firewood for heating, which creates a negative impact on the local ecosystems. To close the energy efficiency gap, the paper proposes a sustainable and holistic approach that integrates thermal insulation with effective energy efficiency planning using a staged-renovation approach by utilising locally available insulation materials / resources. The feasibility study presented in the paper was conducted with a simulation-based parametric study to recognise the potential of novel and sustainable insulation structures on building heat demand. This innovative approach can potentially reduce heat demand in high-altitude houses by as much as 70 %, offering a transformative solution. Furthermore, its adaptability makes it transferable to similar high-altitude communities, thus advancing sustainable energy practices for climate change mitigation and contributing to broader sustainable development goals.

Automatic Identification and Evaluation of Building Clusters for Optimal District Heating Networks Phasing

Considering future city changes when designing district heating networks can be tedious. This paper tackles this problem by automatically identifying potential future building clusters connections, and evaluating relevant financial and planning criteria. The methodology uses open GIS data for cluster identification and building heat demand estimation, and thermal-hydraulic models for simulating the network. On a neighbourhood scale, area characteristics such as total heat demand, distance to source, and road surface type mainly affect the decision of where to expand the network to. However, on a street scale, the sizing and location of the initial network mainly affect the network’s phasing.

Bottom-up LCA building stock model: Tool for future building-management scenarios

Residential buildings have a substantial impact on global environmental impacts. In this paper we present a model that assesses impacts from materials (embodied impacts) as well as from heating (operational impacts) for all residential buildings in Switzerland. Our model builds on an existing high-resolution spatio-temporal building stock model, which estimates the space heating demand of buildings, based on building property and site data. In this work we additionally include the impacts of materials extraction, production, transportation, and disposal of building components (floors, roofs, walls, ceilings and windows). Our model comes with a site- and building-specific default parametrization for all buildings, but it is developed as a tool that allows architects and planners to adapt building and site data with specific information to increase the accuracy of the environmental assessment of the current buildings and future building-management scenarios. We applied the model to ten Swiss buildings and assessed a selection of building-management scenarios to illustrate the application of the tool. The results show that our model estimates fairly accurate values of thermal and ventilation heat transmittance, operational space heating demand, and operational and embodied climate-change impacts for the considered buildings. The detailed modeling results for the ten case study buildings outperformed the simplified version of the model with generic default values for material building composition (using age-grouped building archetypes), emphasizing the importance of adding detailed material information in building life cycle assessments. Therefore, a focus should be placed on obtaining accurate material inventories of buildings in future research. The here-developed model allows for integration of such detailed material inventories, overwriting the provided default values.

Performance Study of a Hybrid Solar-Assisted Ground-Source Heat Pump System Used for Building Heating and Hot Water Demands

Performance Study of a Hybrid Solar-Assisted Ground-Source Heat Pump System Used for Building Heating and Hot Water Demands

Deep clustering of reinforcement learning based on the bang-bang principle to optimize the energy in multi-boiler for intelligent buildings

The bang-bang relays of the multiple-boiler system (MBS) control, are characterized by complex limiter saturation functions and classified as fixed parameters. Their action signals cannot precisely control the nonlinear dynamic building heating demand over their entire range of operation. Moreover, in a mono-boiler system, the bang-bang controller endures increasing short cycling over partial load time due to the heating system being considered to have an oversized boiler at most times of running, thus promoting high energy consumption and fluctuating indoor thermal comfort. So, it is difficult to cope with uncertainties in outdoor environments and indoor heating load. Hence, this study formulates the MBS control problem as a dynamic Markov decision process and applies a deep clustering of reinforcement learning approach to obtain the optimal control policy through interaction with the environment based on multi-agent learning according to bang-bang action. With such an approach, adopting a new boiler sequencing control (BSC) strategy using deep clustering of reinforcement learning based on a bang-bang (DCRLBB) manner. The deep clustering is configured to break Lagrangian trajectory curves into piecewise segments to represent the RL agent's action policy. The agent's action policy signals are configured from the bang-bang reward formula based on trade-off implications to be more adjustable than traditional fixed parameters such as fuzzy bang-bang controller (FBBC). The agent of BSC significantly affects the energy performance of the MBS, whereas the other agent resizes boiler capacity by acting to adjust the boiler solenoid fuel valve. The comparison of results between the proposed strategy and conventional FBBC shows distinct differences in the superior response of DCRLBB under dynamic indoor/outdoor actual conditions and energy saving by more than 32% while maintaining the indoor thermal in the comfortable range.

Thermodynamic analysis of mechanical booster pump-assisted sorption thermochemical heat transformer driven by low-grade heat for building applications

Thermochemical heat transformers (THT) can offer the potential for efficient energy storage and upgrade based on a reversible solid-gas reaction. A mechanical booster pump (MBP)-assisted water-based sorption thermochemical heat transformer driven by low-grade solar thermal energy is proposed to handle variations in the heat demand of buildings. The MBP operates during the discharging process to adjust the magnitudes of temperature lift by compression ratio depending on the user’s demands. The performances of the proposed cycle employing three different reactive salts are investigated and compared with the conventional THT cycle under various operating conditions. Results indicate that compared to the conventional THT cycle, the proposed cycle achieves a maximum temperature lift of 15–17°C, 17–19°C, and 23–26°C for SrBr2, LiOH, and CaCl2 in the evaporating temperature range of 20–40°C, respectively. In the same operating conditions, SrBr2 demonstrates the highest energy and exergy efficiencies, while CaCl2 is inferior to the others due to its greater sensible heat consumption and lower reaction heat under the studied conditions. A suggestion is put forth for enhancing the temperature lift by employing a two-stage MBP-assisted cycle that utilizes the reactive salt SrBr2. Compared to the single-stage MBP-assisted cycle, the heat output temperature can be further increased by up to 3–16°C at the expense of a maximum decrease of 6.6%, 84.4%, and 9.0% in coefficient of performance (COP) based on total energy input, COP based on electricity input, and exergy efficiency, respectively, at 30°C evaporating temperature. The economic and environmental analysis indicates that the proposed system is economically and environmentally feasible and could be a promising alternative to residential water heaters.

The flexibility of virtual energy storage based on the thermal inertia of buildings in renewable energy communities: A techno-economic analysis and comparison with the electric battery solution

The Renewable Energy Community (REC) concept has been introduced into the European decarbonization guidelines to promote the utilization of Renewable Energy Sources (RES) and to incentivize their self-consumption at the local level. This paper analyzes the flexible use of Heat Pumps (HP) for building heating in an REC context. The Power-to-Heat (P2H) energy conversion process of HP allows the flexibility of the thermal sector to be exploited within the electricity sector: in this way, it is possible to store energy in the form of heat inside the building mass and then use the stored energy to reduce the building heating demand in the hours following the accumulation of energy. This energy storage solution has been defined as building-based Virtual Energy Storage (VES). The flexibility enabled by VES has been used to optimize the self-consumption of an REC. The flexible VES solution was evaluated, from a technical and economic point of view, through a sensitivity analysis on the variation of the RES penetration, and the results were compared with those based on a more traditional centralized electric battery (EB) storage system. The results demonstrated that the VES solution is less flexible than electric batteries. Nevertheless, both flexible solutions (VES and EB) can significantly increase the REC self-consumption: the self-consumed energy increased by between 6% and 44% thanks to the exploitation of the VES flexibility, while the EB flexibility enabled an increase in the self-consumed energy of 19% to 63% according to the scenario analyzed. However, due to the high investment cost of EB, the VES configuration resulted to be the best solution from an economic point of view.

Neural network approach for quick dimensioning of energy walls

neural network approach for quick dimensioning of energy walls

Performance of a BAPVT modules coupled TEHR unit fresh air system based on micro heat pipe array

Energy consumption for heating and cooling fresh air has become a major component of building energy consumption, and there is a growing focus on highly-efficient and energy-conserving fresh-air treatment technology. This paper presents a new fresh-air system based on a high-efficiency heat transfer element, the micro heat pipe array (MHPA). It consists of a novel air-cooled building attached to a photovoltaic/thermal module (MHPA-BAPVT) and a thermoelectric heat recovery unit (MHPA-TEHR). The system uses solar energy, photovoltaic power generation, and waste heat for fresh air preheating. A series of fresh-air systems were designed, constructed, tested, and analyzed. The results reveal that on sunny winter days, the temperature of fresh air flowing through MHPA-BAPVT modules can increase by 22.15 °C. The average thermal and electric efficiency of the MHPA-BAPVT modules was 12.16% and 7.10%, respectively, and MHPA-TEHR could exceed the upper limit of passive heat recovery technology, which could generate 1.25 kW of heat per 1 kW of input electric power. The maximum ratio of total heating power to the total power consumption of the fresh-air system reached 14.94. During a typical day in the winter season, solar preheating exceeded the fresh air heat requirement by 24.34%, and the excess heat provided 292.92 MJ for building heating demand. Comparing the electric heating fresh-air unit with conventional heat recovery, the former achieved nearly zero energy consumption during the winter season. During the transitional season, the system could provide 711.82 MJ of solar energy, which improved the indoor temperature. Finally, using the experimental data and meteorological parameters of Zibo, the annual heat and electricity benefits and reduced carbon dioxide emissions of the fresh-air system were calculated. The fresh-air system in this study can fully utilize solar energy for heat collection and power generation to reduce building energy consumption and can be applied widely.

An Agent-Based Decision Support Framework for a Prospective Analysis of Transport and Heat Electrification in Urban Areas

One of the main pathways that cities are taking to reduce greenhouse gas emissions is the decarbonisation of the electricity supply in conjunction with the electrification of transport and heat services. Estimating these future electricity demands, greatly influenced by end-users’ behaviour, is key for planning energy systems. In this context, support tools can help decision-makers assess different scenarios and interventions during the design of new planning guidelines, policies, and operational procedures. This paper presents a novel bottom-up decision support framework using an agent-based modelling and simulation approach to evaluate, in an integrated way, transport and heat electrification scenarios in urban areas. In this work, an open-source tool named SmartCityModel is introduced, where agents represent energy users with diverse sociodemographic and technical attributes. Based on agents’ behavioural rules and daily activities, vehicle trips and building occupancy patterns are generated together with electric vehicle charging and building heating demands. A representative case study set in London, UK, is shown in detail, and a summary of more than ten other case studies is presented to highlight the flexibility of the framework to generate high-resolution spatiotemporal energy demand profiles in urban areas, supporting decision-makers in planning low-carbon and sustainable cities.

Dynamic thermal demand analysis of residential buildings based on IoT air conditioner

With the development of communication technology and artificial intelligence, the application of IoT (Internet of Things) has driven the development of smart cities. Real-time and non-intrusive IoT air-conditioning data as a supporting source provides new possibilities for studying dynamic heat demand in residential buildings. However, the data structure of IoT is very different from that of traditional sources. Therefore, this study conducts a dynamic thermal demand analysis of residential buildings at a regional scale based on IoT air conditioners. Firstly, the data preprocessing paradigm for the IoT air conditioner was constructed. Next, the change of dynamic thermal demand was comparatively analyzed through the coupling regulation of the air conditioner set temperature and wind velocity, as well as the frequency adjustment of active functions in two functional rooms and three demand groups. There were two further findings, by evaluating the PMV fluctuation performance under different operating conditions, the retaliatory and economic thermal demand in different groups were found to exist. Meanwhile, another finding was that there were seasonal adaptive adjustments and delayed compensatory adjustments for air conditioners. Moreover, the energy-saving potential of air conditioner operation under energy-saving awareness was quantified. From lower to upper class, 0.90%, 0.94%, and 0.38% of the accumulative time spent by bedroom users have thermal demand energy-saving potential of 75%, and 0.31%, 0.44%, and 0.33% have thermal demand energy-saving potential of 6%.

Regression versus probabilistic approach for operational data – Heat demand of buildings to be connected to a district heating system

The robust design of 5th generation district heating and cooling (5GDHC) systems requires accurate and sufficient operational data on the energy consumption and production of the connected prosumers. Often these data requirements are not met. Historical energy-related data for buildings is sometimes no longer accessible, and if available, often limited in time span and temporal resolution. Also, technical details on the utilization of the energy within the heating, ventillation and air-conditioning (HVAC) system of a building and its relationship with building occupation and outside weather conditions are often difficult to obtain. This lack of data requires careful pre-processing of this data, to ensure a robust 5GDHC system design. To cope with this lack of data, we propose a two-step model. First, techniques of linear regression are used to detect relations with contextual key factors, such as occupation, day of the week, hour of the day and outside temperature. In a second step, a piece-wise stochastic approach is used to generate artificial data for any combination of the key factors, allowing for the detection and regeneration of intermittent behavior that is typical present in the obtained operational data. The advantage of this method is that it takes into consideration the unknowns and randomness of the system’s behavior. It allows for variance within the artificial time series (peaks and gaps), which makes it better suited for the robust design of 5GDHC systems. We applied this model on operational data from 23 buildings in Flanders, Belgium and produced artificial data for these buildings. The model can separate HVAC energy data related to the outdoor weather conditions from non-HVAC related energy data. It can also forecast energy data for other weather conditions and building occupation.

Parametric Assessment of Building Heating Demand for Different Levels of Details and User Comfort Levels: A Case Study in London, UK

The Level of Detail (LoD), a parameter used to define the information contained in building models, is an important factor to consider in modeling building energy at the urban scale. In this research, we conducted a parametric study regarding the data requirements for the estimation of the annual residential heat demand in London. More particularly, the requirement of the observation of the actual roof type (LoD2) and the window-to-wall ratio (LoD3) was examined in two different case study areas. Meanwhile, an adaptive comfort level study was implemented using LoD5 models, and its results were assessed holistically with the heat demand to reveal the energy performance of the buildings. The results showed that there was a minor difference in the upgrade of a lower to higher LoD regarding these parameters. At an urban scale, the energy demand of buildings could be estimated using an assumption of archetypes and building ages. However, with a scalable parametric script developed in places, models with a high LoD could provide more detailed insights in the energy performance assessment without generating excessive workload.

The impact of clustering strategies to site integrated community energy and harvesting systems on electrical demand and regional GHG reductions

The current work examines the Integrated Community Energy and Harvesting system (ICE-Harvest), an integration of thermal and electrical distributed energy resources. The system prioritizes the harvesting of community waste energy resources—for example, residual heat rejected from cooling processes and peak electricity fossil-fuel fired generators, as well as energy from curtailed clean grid electricity resources—to help in satisfying the heating demands of commercial and residential buildings. As such, ICE-Harvest systems provide a solution that can minimize greenhouse gas emissions from high-energy-consumption buildings in cold-climate regions such as North America and Northern Europe. The current work focuses on where to locate these systems and introduces different clustering methods for integrated energy systems that focus on thermal load diversity among buildings in each cluster. In the first technique, buildings with the highest amount of rejected heat from their cooling systems (anchor buildings) are clustered with nearby high heating demands buildings to create a large opportunity for harvesting wasted energy however, buildings not located close to anchor buildings are clustered using density-based clustering methods. The energy harvesting capabilities of this technique are compared to those provided by full DB clustering without specifying the anchor buildings, as well as to full density-based clustering with a post-processing step in which the nearest anchor building is added to each cluster. The selected clustering method also demonstrates the grid level potential of the ICE-Harvest systems to impact greenhouse gas emissions, heating, and electricity consumption by presenting a reduced model for the ICE-Harvest system that can be applied to any number of clusters on a municipal or provincial/state scale. Specifically, the model is applied to a database of 14,832 high-energy-consumption buildings with the potential for forming 1,139 clusters in the province of Ontario, Canada. The results of this case study reveal that density-based clustering with post processing resulted in the largest emission reduction per unit piping network length of 360 t CO2eq /km/year. In addition, the use of ICE-Harvest systems can displace the energy required from the gas-fired heating resources by 11 TWh, accounting for over 70% of the clusters’ total heating requirements. This results in a 1.9 Mt CO2eq reduction in overall sites’ emissions, which represents around 60% of the clusters’ emissions.

Impact of Climate Change on the Heating Demand of Buildings. A District Level Approach

In the 21st century, the importance of energy generation and carbon emissions in developing countries is indisputable. In the whole wide world, the building stock is responsible for the two fifths of the total world annual energy consumption. Considering the predictions regarding future climate due to climate change, a good understanding on the energy use due to future climate is required. The aim of this study was to evaluate the impact of future weather in the heating demand and carbon emissions for a group of buildings at district level, focusing on an area of London in the United Kingdom. The methodological approach involved the use of geospatial data for the case study area, processed with Python and Anaconda through Jupyter notebook, generation of an archetype dataset with energy performance data and TABULA typology and the use of python embedded in QGIS to calculate the heating demand in the reference weather data, 2050 and 2100 in accordance to RCP4.5 and RCP 8.5 scenarios. A validated model was used for the district level heating demand calculation. On the one hand, the results suggest that a mitigation of carbon emissions under the RCP4.5 scenario will generate a small decrease on the heating demand at district level, so slightly similar levels of heating generation must continue to be provided using sustainable alternatives. On the other hand, following the RCP 8.5 scenario of carbon emission carrying on business as usual will create a significant reduction of heating demand due to the rise on temperature but with the consequent overheating in summer, which will shift the energy generation problem. The results suggest that adaptation of the energy generation must start shifting to cope with higher temperatures and a different requirement of delivered energy from heating to cooling due to the effect of climate change.