Building Heat Demand Research Articles

Optimal clean heating mode of the integrated electricity and heat energy system considering the comprehensive energy-carbon price

Clean heating is a powerful solution for satisfying the building heat demand by synergizing energy efficiency and carbon emission. For satisfying the newly increased heat load, this paper constructs an alternative integrated electricity and heat energy system (IEHES) to consider different clean heating supply modes, namely electrical-heating mode (EH, electric boiler or electric heat pump), CHP unit with high back-pressure turbine heating mode (HBP–CHP), and auxiliary heat source heating mode (AHS, coal-fired boiler or gas-fired boiler). An optimal configuration planning and dispatch model is proposed for comparing the performance of the three heating modes. Wherein, a new comprehensive energy-carbon price is proposed as the optimization objective to minimize this system's total production costs and carbon emission costs. The results show that the HBP-CHP mode is the optimal solution when the heat load growth rate λ is lower than 14%, followed by the electric heat pump heating way under the EH mode. However, the HBP-CHP mode will limit the flexibility of the IEHES when λ is larger than 8%. Therefore, we propose a combined heating mode by introducing the electric heat pump into the HBP-CHP heating mode that facilitates the flexibility of the IEHES and brings the optimal system coal-saving effect.

Scientific and technological progress and future perspectives of the solar assisted heat pump (SAHP) system

Among all available solar technologies, solar-assisted heat pump (SAHP) is the most popular technology for low- and medium-temperature applications, i.e., drying, space heating, and water heating. This paper presents a critical review of the SAHP, leading to identification of the key technological challenges with the current SAHP technology, including (1) poor energy performance at low ambient temperatures; (2) difficulty in coupling solar thermal panels with heat pumps to achieve increased operational time and reduced electrical energy usage; (3) mismatch between heat demand of the building and heat supply of the SAHP system; and (4) lack of multi-functional modelling tool. A recently developed SAHP technology is introduced, which can effectively tackle the challenges with the current SAHPs, thus achieving enhanced solar efficiency, improved heat pump energy efficiency, and fast responsive time to the heat demand of buildings. The future research directions of the SAHP were identified: (1) multi-source heat pump; (2) heat pump defrosting capacity using waste heat and renewable energy; (3) advanced heat storage and exchange unit (HSEU) technology; and (4) advanced machine learning and multi-objective evolutionary optimisation models for performance prediction and optimisation of the SAHP using big datasets.

Effects of thermal bridges on the heat demand of residential buildings

This paper presents the influence of the thermal bridges effect on heating demand in residential buildings using numerical simulation. Quantitative evaluation on temperature field is performed by means of linear heat transfer coefficient. Specific details of existing thermal bridges within the building envelope have been identified and selected for analysis. The model simulation was performed using COMSOL Multiphysics software. Thermo-physical properties of the building envelope elements were selected from the software database and the boundary conditions have been setup. Meshing was done by dividing the virtual domain into an optimum number of finite elements. Model validation was done by comparative analysis between the values of the linear thermal transmittance obtained through numerical simulation and the reference values stipulated in C107-3-2005 technical regulation (catalogue of building thermal bridges). Specific solutions for correcting the thermal coupling between different building envelope elements were adopted in order to reduce the linear thermal transmittance up to 23% and the heat flow rate up to 71%. The evaluation of space heating demand shows the impact of correct treatment of thermal bridges on the reduction of energy consumption.

Spatial factors influencing building age prediction and implications for urban residential energy modelling

Urban energy consumption is expected to continuously increase alongside rapid urbanization. The building sector represents a key area for curbing the consumption trend and reducing energy-related emissions by adopting energy efficiency strategies. Building age acts as a proxy for building insulation properties and is an important parameter for energy models that facilitate decision making. The present study explores the potential of predicting residential building age at a large geographical scale from open spatial data sources in eight municipalities in the German federal state of North-Rhine Westphalia. The proposed framework combines building attributes with street and block metrics as classification features in a Random Forest model. Results show that the addition of urban fabric metrics improves the accuracy of building age prediction in specific training scenarios. Furthermore, the findings highlight the way in which the spatial disposition of training and test samples influences classification accuracy. Additionally, the paper investigates the impact of age misclassification on residential building heat demand estimation. The age classification model leads to reasonable errors in energy estimates, in various scenarios of training, which suggests that the proposed method is a promising addition to the urban energy modelling toolkit.

Building Heat Demand Forecasting by Training a Common Machine Learning Model with Physics-Based Simulator

Accurate short-term forecasts of building energy consumption are necessary for profitable demand response. Short-term forecasting methods can be roughly classified into physics-based modelling and data-based modelling. Both of these approaches have their advantages and disadvantages and it would be therefore ideal to combine them. This paper proposes a novel approach that allows us to combine the best parts of physics-based modelling and machine learning while avoiding many of their drawbacks. A key idea in the approach is to provide a variety of building parameters as input for an Artificial Neural Network (ANN) and train the model with data from a large group of simulated buildings. The hypothesis is that this forces the ANN model to learn the underlying simulation model-based physics, and thus enables the ANN model to be used in place of the simulator. The advantages of this type of model is the combination of robustness and accuracy from a high-detail physics-based model with the inference speed, ease of deployment, and support for gradient based optimization provided by the ANN model. To evaluate the approach, an ANN model was developed and trained with simulated data from 900–11,700 buildings, including equal distribution of office buildings, apartment buildings, and detached houses. The performance of the ANN model was evaluated with a test set consisting of 60 buildings (20 buildings for each category). The normalized root mean square errors (NRMSE) were on average 0.050, 0.026, 0.052 for apartment buildings, office buildings, and detached houses, respectively. The results show that the model was able to approximate the simulator with good accuracy also outside of the training data distribution and generalize to new buildings in new geographical locations without any building specific heat demand data.

A novel spatial–temporal space heating and hot water demand method for expansion analysis of district heating systems

The fourth generation of district heating will play a significant role in the decarbonization of energy systems. In general, only space heating demand is considered when assessing the district heating potential, excluding hot water. In contrast, the hot water demand accounts for up to 18% of total final energy demand in buildings. Hence, in this paper, a spatial–temporal method for annual hot water demand is considered in conjunction with space heating demand while technically and economically assessing the expansion potential of the district heating. A bottom-up heat demand mapping process was carried out for Pristina city to identify the space heating demand of buildings, while a top-down approach was used for spatial mapping of hot water demand. Hourly, daily, weekly and seasonal hot water demand profiles, besides heating degree-day method used for space heating, were considered when estimating the temporal operation of district heating. The findings show that the existing district heating can be increased four times when excluding hot water and five times when considering both space heating and hot water demand of buildings. Moreover, the heat supply capacities needed in district heating to cover space heating and hot water demand would be 600 MW and 70 MW respectively.

Building heating demand vs climate: Deep insights to achieve a novel heating stress index and climatic stress curves

This study offers deep insights into the link between climatic stress and building heating performance. Two representative Italian residential buildings – existing and newly-built, respectively – are investigated by predicting heating demand for 63 locations, covering all typical national climates. Two simulation software are used – i.e., TERMUS® and EnergyPlus –, to compare a standard semi steady-state approach with a more accurate dynamic one. The comparison enables to understand the influence of the climatic stress on different levels of building modeling/simulation. Notably, the semi steady approach can provide reliable outcomes (close to the dynamic one) for existing buildings in cold climates. Then, a sensitivity analysis is conducted to investigate the correlation between climatic parameters and yearly heating demand, showing that the heating degree day index does not provide a complete explanation of such demand. Indeed, it does not take into account latitude and/or solar radiation, whose influence is not negligible. Therefore, a novel heating stress index is proposed, including normalized heating degree day and latitude. Its expression is optimized through a Pareto approach to ensure the best fitting/regression of yearly heating demand for both building typologies. The achieved determination coefficient (R2) is 0.990 for the existing building, 0.995 for the newly-built one. Finally, climatic stress curves are achieved to predict heating demand and related running cost as a function of the proposed index, providing a user-friendly but reliable tool to forecast building heating needs.

Circular economy for clean energy transitions: A new opportunity under the COVID-19 pandemic

This paper models the energy and emissions scenarios for a circular economy based clean energy transitions in a 140,000-population town in China, taking into account the new situation encountered by the COVID-19 pandemic. The modelled scenarios propose new clean energy transition roadmaps towards a sustainable urban system through the implementation of circular economy strategies. This is represented by the cascading use of industrial excess heat to form symbiosis between factories and to cover the growing building heat demand, as well as by the electrification of the transport sector and reusing the batteries for a second life as energy storage devices. The results show that for a circular economy scenario, during 2020–2040, an accumulated saving of 7.1 Mtoe final energy use (34%), a decline in 14.5 Mt CO2 emissions (40%) and 592 t PM2.5 emissions (43%) could be achieved compared with the business-as-usual scenario. The outcomes of the circular economy strategies are at least 7% better than the new policy scenario which simply has energy efficiency improvements. The outbreak of the COVID-19 tremendously impacts the socio-economic activities in the town. If taking the pandemic as an opportunity to enhance the circular economy, by 2040, compared with the scenario without introducing circular economy measures, the extra avoided final energy use, CO2 emissions and PM2.5 emissions could be 1.6 Mtoe (8%), 3.8 Mt (11%) and 229 t (17%) respectively.

Benchmarking cooling and heating energy demands considering climate change, population growth and cooling device uptake

The planning of future energy policies and energy systems requires an understanding of the intricate relationships between climate change, technology uptake, population growth and building energy demand. Building cooling demand is expected to increase considerably in many parts of the world as the climate warms on average. In temperate climates, this increase is expected to be particularly large due to the increase in the number of days when cooling is required to maintain a comfortable indoor building temperature. We quantify the impact of climate change, cooling device uptake and population growth based on population-weighted climate models, population growth scenarios and measured thermal energy demand data for Switzerland. This study incorporates three climate development scenarios and we find for an extreme case, that up to 17.5 TWh cooling energy would be required by the middle of the 21st century compared to 3–5 TWh in more moderate cases. Heating energy demand is expected to decrease to around 20 TWh by mid-century, which is approximately one-third of the current Swiss building heating demand. The presented combined quantification of future cooling demands for Switzerland provides a set of benchmarked energy demands and highlights the critical role of air-conditioning technology uptake, which significantly contributes to future cooling demands. Pursuing alternative cooling strategies is therefore needed to limit cooling energy demand impacts on the future energy systems particularly in countries with temperate climates.

Improving GIS-Based Heat Demand Modelling and Mapping for Residential Buildings with Census Data Sets at Regional and Sub-Regional Scales

Heat demand of buildings and related CO2 emissions caused by energy supply contribute to global climate change. Spatial data-based heat planning enables municipalities to reorganize local heating sectors towards efficient use of regional renewable energy resources. Here, annual heat demand of residential buildings is modeled and mapped for a German federal state to provide regional basic data. Using a 3D building stock model and standard values of building-type-specific heat demand from a regional building typology in a Geographic Information Systems (GIS)-based bottom-up approach, a first base reference is modeled. Two spatial data sets with information on the construction period of residential buildings, aggregated on municipality sections and hectare grid cells, are used to show how census-based spatial data sets can enhance the approach. Partial results from all three models are validated against reported regional data on heat demand as well as against gas consumption of a municipality. All three models overestimate reported heat demand on regional levels by 16% to 19%, but underestimate demand by up to 8% on city levels. Using the hectare grid cells data set leads to best prediction accuracy values at municipality section level, showing the benefit of integrating this high detailed spatial data set on building age.

On the correlation between building heat demand and wind energy supply and how it helps to avoid blackouts

Keeping the electric and heat grids stable is the major challenge facing the world as it transitions away from fossil fuels to electricity and heat provided by wind, water, and sunlight (WWS). Because building heating and cooling demands and wind and solar energy supplies both depend on the same weather, building demands should be modeled consistently with renewable supplies. However, no model to date has calculated future thermal loads consistently with future renewable supplies. Here, a global weather/climate model is used to do this. Grid stability in 24 world regions encompassing 143 countries is then examined. Low cost solutions are found everywhere. Building heat loads are found to correlate strongly with wind energy supply aggregated over large, cold regions. Moderate correlations are found elsewhere, except no correlation is found in some tropical islands and some small countries. Thus, wind energy in most climates can help to meet seasonal heat loads, thereby helping to reduce the cost of energy. Finally, wind and solar power supplies are negatively correlated, indicating that wind and solar are complementary in nature and should both be built, where feasible, to reduce output variability arising from installing only one of them.

Deep Learning-Based Generation of Building Stock Data from Remote Sensing for Urban Heat Demand Modeling

Cities are responsible for a large share of the global energy consumption. A third of the total greenhouse gas emissions are related to the buildings sector, making it an important target for reducing urban energy consumption. Detailed data on the building stock, including the thermal characteristics of individual buildings, such as the construction type, construction period, and building geometries, can strongly support decision-making for local authorities to help them spatially localize buildings with high potential for thermal renovations. In this paper, we present a workflow for deep learning-based building stock modeling using aerial images at a city scale for heat demand modeling. The extracted buildings are used for bottom-up modeling of the residential building heat demand based on construction type and construction period. The results for DL-building extraction exhibit F1-accuracies of 87%, and construction types yield an overall accuracy of 96%. The modeled heat demands display a high level of agreement of R2 0.82 compared with reference data. Finally, we analyze various refurbishment scenarios for construction periods and construction types, e.g., revealing that the targeted thermal renovation of multi-family houses constructed between the 1950s and 1970s accounts for about 47% of the total heat demand in a realistic refurbishment scenario.

Applicability and comparison of solar-air source heat pump systems between cold and warm regions of plateau by transient simulation and experiment

Solar-air source heat pump (solar-ASHP) system has a potential application in the field of hot water and space heating in residential buildings. Such system features the complementary advantages to solve the discontinuous operation of the single solar system and the frosting issue of the single ASHP system. This paper built the solar-ASHP systems in Kunming and Shangri-La, and tested the system performance under different weather conditions in these two regions of plateau. Meanwhile, the transient heat balance models of the system were established under the sunlight time and non-sunlight time and were verified by the experimental results. Moreover, the verified model was applied to reveal the energy balance performance between the energy supply and building heat demand. The law of the system performance affected by the ambient temperature, effective heat collecting area, and cumulative heating capacity of collector was explored by the validated model. The results indicate that when the ambient temperature decreases by 1 °C during non-sunlight time, the energy efficiency ratio decreases by about 0.07. A square meter decline in the effective heat collecting area pushes an increase in the heating capacity of 5.75 MJ. Meanwhile, the cumulative heating capacity of collector increases by 5 MJ, and the ASHP energy consumption decreases by 0.54 kWh. The dynamic changes of the ambient temperature and instantaneous solar radiation are the main reasons of the heat balance errors. Therefore, both the developed system and model are feasible and reliable in different climate regions.

Characterization of an ettringite-based thermochemical energy storage material in an open-mode reactor

The mismatch between renewable energy supply and demand requires energy storage technologies to work out the dilemma. In the thermal energy field, ettringite-based energy storage seems to be a good solution thanks to its high energy density and low material cost. It can store excess solar energy to meet the heating and domestic hot water demand in buildings. Therefore, the current work experimentally examines the energetic performance of an ettringite-based material made of commercial cements. The TG-DSC analysis shows the energy storage capacity of the investigated material is as high as 282 kWh/m3 original hydrated materials. Besides, the laboratory reactor tests prove the charging temperature is as low as 55–65°C for ettringite. Under operating conditions, the average energy-releasing power during the full period is about 33.3 W/kg while the maximum power is about 915 W/kg original hydrated materials, which are significantly higher than most materials from the literature. The best volumetric energy-releasing density and the corresponding prototype storage density of the fixed-bed obtained are 176 kWh/m3 original hydrated materials and 104 kWh/m3, respectively.

High Level Controller-Based Energy Management for a Smart Building Integrated Microgrid With Electric Vehicle

This paper presents an effective approach for the modelling and optimization of a smart building integrated microgrid (BIM). The main objective is the development of a smart building energy management system (BEMS) which is in charge of optimally controlling the operation of a building-integrated-microgrid. More specifically, we consider a BIM consisting of a roof-top solar photovoltaic, wind turbine, energy storage unit, electric vehicle, micro-CHP, metering infrastructure and loads. The BIM loads are associated to satisfy the electric needs of occupants as well as considering the operating flexibility of thermal load that includes building heating and hot water demand. The emphasis is given to the optimal performance of the BIM, where the objective is to ensure the power balance between production and loads in the microgrid (electric and thermal). A centralized algorithm is implemented for the power management of all components as well as power exchanges with the electrical grid. The developed algorithm is tested through a case study, where the effects of both loads and renewable resources variabilities on the operation of the BIM are analyzed via numerical simulations.

Assessing the energy efficiency of a district’s existing building stock glazing – Case Study TU Dresden

Restoration of existing buildings becomes inevitable due to urbanization, population growth, conservation laws and scarcity of resources. Upgrading existing glazing could reduce building heating demand by 10% and reduce the TUD emissions by 573.4 tco2-eq/a. However, renovations are often postponed due to unknown energetic glazing quality. Traditional measures such as removing the glazing to a test-lab to identify the ug-value are often not permitted nor practical for existing (heritage-listed) occupied buildings. In this article, methods suitable for the assessment of the energetic quality of existing glazing without removing the glazing from the building, i.e. (a) the use of literature values per building age classification, (b) glazing built up measurement with ug-value calculations per standard and (c) direct ug-value measurement using a portable tool, are tested on the existing window stock of the TUD campus, compared and evaluated in terms of accuracy and easiness of application. Results show that the energetic quality can range widely from a ug-value of 0.5 to 3.5W/m2K within a single building, and that method (a) is not accurate and method (b) is as accurate as method (c), but 20 minutes faster per glazing which, extrapolated to a district like TUD, can save months of work and significantly increases the chances of glazing renovations being carried out.

Development of reference building models based on statistical data for the analysis of the building heat demand

For the transition of the energy system from the status quo to a point where the political targets are achieved, the largest potentials for energy and CO2 savings are in the building sector, especially due to older unrenovated buildings. For the state of Bavaria, preliminary investigations have shown that approximately 27 % of the total final energy demand is provided for space heating and domestic hot water preparation of the residential building stock. Extensive measures must be taken in this sector for a successful energy transition.To make the best use of available resources, investigations with realistic reference building models are essential. Previous studies have shown that a significant classification of the building stock can performed and large parts of it can be represented with a limited number of reference buildings models. In this study, it is shown that the representativity can be further increased by selective grouping of the categories while keeping the number of necessary models as small as possible. In addition, a method is presented to model reference buildings using statistical data. The methods of grouping and modelling as well as possible applications and outputs of the generated models are shown for a realistic example.

Sensitivity analysis to reduce duplicated features in ANN training for district heat demand prediction

Artificial neural network (ANN) has become an important method to model the nonlinear relationships between weather conditions, building characteristics and its heat demand. Due to the large amount of training data required for ANN training, data reduction and feature selection are important to simplify the training. However, in building heat demand prediction, many weather-related input variables contain duplicated features. This paper develops a sensitivity analysis approach to analyse the correlation between input variables and to detect the variables that have high importance but contain duplicated features. The proposed approach is validated in a case study that predicts the heat demand of a district heating network containing tens of buildings at a university campus. The results show that the proposed approach detected and removed several unnecessary input variables and helped the ANN model to reduce approximately 20% training time compared with the traditional methods while maintaining the prediction accuracy. It indicates that the approach can be applied for analysing large number of input variables to help improving the training efficiency of ANN in district heat demand prediction and other applications.

Technical, economic and ecological effects of lowering temperatures in the Moscow district heating system

This paper focuses on evaluating the technical, economic and ecological effects of the transition from the current high temperature charts to the lower temperature charts in the Moscow DHS by means of a developed spreadsheet-based model. A methodology suitable for assessing results of potential transition to lower temperatures in DHS of cities in Russia and worldwide is proposed and implemented in the model. The reference case of 2016 and three cases with decreased heat demand in buildings by 5, 10, and 20% were considered. The results show that fuel savings of 678–872 ktce a can be achieved with respect to the current temperature charts in the Moscow DHS. The 110/50°С temperature chart is the most profitable option, with net present values varying from 4.64 to 10.74 bn RUB depending on the case. The 95/50°С chart, which leads to a reduction of 1.325–1.387 MtCO2/a, has the least impact on the environment. A more significant CO2 emissions reduction can be achieved by strong energy-saving measures and broad utilization of renewable and waste energy. The essential prerequisite for the transition is a reduction of the heat demand in buildings by at least 20%.

Building heat consumption and heat demand assessment, characterization, and mapping on a regional scale: A case study of the Walloon building stock in Belgium

Energy consumption in buildings results in CO2 emissions and it is necessary to reduce energy consumption thus its related emissions. This research is included in the Wal-e-cities project, which is funded by the European Regional Development Fund (ERDF) and aims to create tools that facilitate the transition toward smart territory. The annual heat consumption (HC) and heat demand (HD) of Wallonia building stock of more than 1,700,000 buildings are assessed. Subsequently, the developed energy models are coupled with a geographic information system (GIS) to calculate and map the HC and HD. The HC and HD are calculated for each building and are represented by different levels of territorial aggregation, namely neighbourhood, municipality, and urban region scales. The highest HC values were observed in large cities and main industrial areas, whereas the lowest values were observed in rural areas. For residential sector, HC is mainly related to the number of dwellings, which differs from that of tertiary and industrial sectors where HC also depends on the nature and function of buildings. Based on mean values at the neighbourhood scale, the HD is 16.44% lower than the HC for the residential sector, 15.78% lower than the HC for the tertiary sector, and 9.26% lower than the HC for the industrial sector. The proposed energy models are validated. The relative differences between annual HC calculated in this study and that provided in the regional energy reports are −5.82% for the residential sector, −14.29% for the tertiary sector, and −2.02% for the industrial sector.