Demand For Buildings Research Articles

Impact of urban heat island on high-rise residential building cooling energy demand in Hong Kong

This study aims to investigate the impact of urban heat island (UHI) on cooling energy demand in high-rise residential buildings, with a primary focus on cooling-dominated regions. We take Hong Kong, a typical highly urbanised city with a humid subtropical climate, as a representative case. Multiple year-by-year EnergyPlus simulations were conducted using 20 years of actual meteorological data (1999–2018) from one urban site and one rural site for a typical 40-storey residential building. Cross-sectional analyses were performed in inter-annual, seasonal, and diurnal scales to comprehensively examine the temporal dynamics of cooling-relevant energy impacts of UHI. Regression analyses were conducted to reveal the relationship between UHI’s energy impact and UHI intensity (UHII). Results show that UHI causes an increase of 6.0% in cooling energy demand for high-rise residential buildings in Hong Kong. Such impact reaches a daily (intra-annual) minimum/maximum of −1.4%/+21.8% (+3.5%/+28.3%) at 5 pm/6 am (in summer/winter) and diminishes by an average rate of 1.7% per decade likely due to the urbanisation in rural regions. Each 1 °C increase in UHII causes an increase of 7.9%/16.1%/36.9% in seasonal cooling energy demand for summer/transitional seasons/winter, respectively. This study reveals that UHI's intricate temporal dynamics can lead to heterogeneous effects on building energy performance. Besides, distinct patterns of UHI-driven cooling energy profiles between high-rise and low-/medium-rise buildings are identified. Moreover, this study underscores the importance of employing multiple year-by-year meteorological data and addressing season-specific nonlinearity in building energy research to ensure the reliability of findings against the backdrop of climate change.

Energy consumption and thermal comfort assessment using CFD in a naturally ventilated indoor environment under different ventilations

The rising energy demands of modern buildings necessitate the proper balance between occupant comfort and energy efficiency. This study numerically evaluates the effect of different inlet and exhaust vent profiles on indoor IEQ and energy consumption in well-validated, occupied office buildings for a cold climate conditions. Using ANSYS products, the research evaluates the impact of different ventilation configurations on occupant comfort using different comfort parameters such as, PMV, PPD, air temperature, temperature gradient, fluid flow velocity. The study also analyze how different ventilation configurations affect the thermal load on the radiator for indoor thermal comfort. The findings reveal a significant effect of ventilation profiles on both IEQ and energy consumption. Inadequate ventilation configuration selection can lead to a significant increase in radiator heat load, potentially increase up to 117.3%. Conversely, the adoption of an optimized ventilation strategy can simultaneously reduce energy consumption and improve indoor occupant thermal comfort. Indoor occupant comfort with minimal radiator heat load can be achieved using vertically oriented, rectangular inlet and exhaust vents with a perimeter ratio of 0.611. This research contributes to the development of sustainable buildings and describes indoor comfort design for cold climate conditions.

Increasing Strength of Clay Soils with the Use of Basalt Fiber: An Experimental Study

Due to the increase in population, the demand for buildings increases. The engineering properties of the soils on which these structures will be built may not always meet the desired conditions. In such cases, soil improvement methods are used. One of these methods is additive used stabilization, in which additives such as lime, fly ash, volcanic ash and tuff, silica fume, and blast furnace slag are used. This method has been used successfully for years. However, in recent years, interest in the use of different types of fibers has increased. Some of these fibers are glass, basalt, polypropylene, and carbon. Basalt fibers have begun to attract attention in soil reinforcement applications due to the properties of their raw material, basalt rock, which is widely distributed in nature, natural, and has high strength. In this study, the effect of basalt fiber reinforcement on the unconfined compressive strength of high plasticity bentonite clay was revealed. For this purpose, 12 mm long basalt fiber in different proportions (1%, 2%, 3%, 4%, and 5%) was used as reinforcement in bentonite clay, and the optimum fiber ratio that provided the maximum increase in strength was determined. According to the results of the study, when 12 mm long basalt fiber was used as reinforcement in bentonite clay, the maximum strength value was obtained at a 4% basalt fiber ratio. However, the strength value decreased in the sample where 5% BF was used. It has been determined that basalt fiber reinforcement improves the strength properties of bentonite clay.

Relationships between ground motion parameters and energy demands for regular low-rise RC frame buildings

AbstractThe present study aims to analyze the correlation between ground motion parameters and energy demands of low-rise RC buildings without shear walls. Two regular 4- and 7-story residential buildings were seismically designed to represent low-rise RC buildings. In order to establish the requirements of single degree of freedom (SDOF) systems as well as multi degree of freedom (MDOF) systems, the dynamic features of “equivalent” SDOF systems were defined by using MDOF systems. The correlation of 20 ground motion parameters (GMPs) of 44 records with the energy demands obtained from a total of 176 nonlinear time history analyses was examined for the SDOF and MDOF systems within the scope of this study. The ground motion parameters (GMPs) were taken as intensity measures (IMs). In contrast, maximum input energy was used as a demand measure (DM) and these energy values were normalized with the masses of the buildings for cases, where the buildings were evaluated together. Parameters related to acceleration and velocity are generally found to yield better results in comparison to the ones related to frequency and displacement. Velocity Spectrum Intensity (VSI) and Arias Intensity (Ia) were obtained to have the highest correlation values as a single parameter. This study suggested new equations by combining multiple ground motion parameters for SDOF and MDOF systems to better reflect damage potential than a single parameter. The use of multiple parameters in combination results in better correlation coefficients.

Large language models driven BIM-based DfMA method for free-form prefabricated buildings: framework and a usefulness case study

ABSTRACT The escalating demand for free-form buildings presents a formidable technological hurdle in the realm of prefabrication manufacturing and assembly. This research draws inspiration from recent advancements in large language models (LLMs) and seeks to harness their transformative potential in tandem with Building Information Modeling (BIM) to advance the Design for Manufacture and Assembly (DfMA) method. The ultimate aim is to catapult the prefabrication industry into an era characterized by unparalleled design freedom and heightened efficiency. The research includes contributions from the following four areas: (1) A review of the literature on DfMA theories, specifically fabrication and construction. (2) The development of a BIM-based DfMA method framework, including the methodological basis, Design for Manufacturing (DfM), and Design for Assembly (DfA). (3) A pipeline for LLMs driven BIM-based DfMA method framework and a validation using the Decathlon Solar Team design-build practice at the University of Washington’s Lotus House. (4) An evaluation of 692 conversation scenarios to verify the feasibility of our proposal. We get a 91.1% user agreement on average based on GPT4. The research results could guide the workflow in building a lifecycle for DfMA practices in the future smart manufacturing era.

Inform me when it matters: Cost salience, energy consumption, and efficiency investments

Using a large-scale natural experiment in staggered billing dates for energy use in Germany and a unique billing dataset for multi-apartment buildings, this paper shows that the month of billing is a significant determinant of heat energy consumption. A large set of residential buildings demand significantly more heat energy annually, when the bill is issued during off-winter months. The paper finds evidence for salience cycles of heating bills that last up to 4 months, likely because consumer attention to heat energy costs is short-lived and absent during months when heating is off. Importantly, this phenomena is pervasive enough to be detectable even in aggregated building-level consumption data. Results suggest that the mere knowledge of costs is not sufficient and that the response to billing information also hinges on its time-varying salience. These findings underscore the importance of understanding the dynamic and heterogeneous nature of cost salience in the design of effective billing for energy conservation.

Study on the Influence of the Application of Phase Change Material on Residential Energy Consumption in Cold Regions of China

As the impact of climate change intensifies, meeting the energy demand of buildings in China’s cold regions is becoming increasingly challenging, particularly in terms of cooling energy consumption. The effectiveness of integrating phase change material (PCM) into building envelopes for energy saving in China’s cold regions is unclear. The aim of this study is to assess the effectiveness of PCM integration in building enclosures for energy efficiency in these regions. The research monitored and recorded indoor temperature data from typical residential cases from May to September. This measured data was then used to validate the accuracy of EnergyPlus22-1 software simulation models. Subsequently, the calibrated model was utilized to conduct a comparative analysis on the effects of PCM on indoor temperatures and cooling energy consumption across these regions. The results of these comparative analyses indicated that PCM can alleviate indoor overheating to varying degrees in severe cold regions of China. Focusing on north-facing bedrooms, applying PCMs reduced the duration of overheating in non-air-conditioned buildings in severe cold regions of China by 136 h (Yichun), 340 h (Harbin), 356 h (Shenyang), and 153 h (Dalian). In terms of cooling energy consumption, the energy saved by applying PCMs ranged from 1.48 to 13.83 kWh/m2. These results emphasize that the performance of PCM varies with climate change, with the most significant energy-saving effects observed in severe cold regions. In north-facing bedrooms in Harbin, the energy-saving rate was as high as 60.30%. Based on these results, the study offers guidance and recommendations for feasible passive energy-saving strategies for buildings in severe cold and cold regions of China in the face of climate change. Additionally, it provides practical guidance for applying PCMs in different climatic zones in China.

The influence of demographic structure on residential buildings' carbon emissions in China

In the process of advancing China’s carbon peak strategy, the residential building is a crucial sector for carbon mitigation. The energy demand for residential buildings, which is dominated by household consumption, is rapidly rising; coping with aging crisis and reducing carbon dioxide (CO2) emissions are two major challenges facing China. This study explores the complex relation between demographic structure and residential buildings' CO2 emissions, and then simulates the future carbon peaking trajectory based on scenario prediction model. The relevant results are fourfold. 1) The overall coupling coordination degree of demographic structure and residential buildings' CO2 emissions in China has entered the optimal state (i.e. high-quality coordination level) until 2020. 2) The increased household size and proportion of children population from 2010 to 2020 had an inhibitory effect on residential buildings' CO2 emissions, whereas increased population size and proportion of elderly population had a promotional effect. 3) Under the influence of demographic structure change, peak residential buildings' CO2 emissions are predicted to be delayed from 2030 to 2032 in China, and the peak value will increase by 3.56%, reaching 1.527 billion tons. 4) At the provincial degree, under the baseline scenario, Beijing will be the first to achieve peak CO2 emissions in 2026; under the aging scenario, Yunnan and Beijing will be the first to reach peak CO2 emissions in 2028. This study provides a reference for Chinese policymakers and other countries to incorporate demographic structure into future projections to advance carbon reduction targets' achievement in the building sector.

On the effect of pressure coefficient source on the energy demand of an isolated cross-ventilated building

Natural ventilation is a simple and effective measure to both reduce the cooling demand of buildings and improve the indoor air quality. In the prediction of heating and cooling demands by means of building energy simulations (BES), the use of pressure coefficients (Cp) from databases as input for the airflow network model is the common approach. Cp values for the same building typology may differ according to the adopted database and are generally unavailable for buildings with complex geometry. Employed Cp values may lead to differences in BES results. This manuscript presents a comparison, for different wind directions, between the Cp distributions and mean values on the facades of a detached building obtained with full-scale CFD – Reynolds-averaged Navier-Stokes (RANS) and large eddy simulation (LES) – simulations, from a database and from wind-tunnel experiments. The obtained pressure coefficients are used in the BES of a naturally ventilated building and the energy demand difference between the four approaches is quantified. Four climate zones (tropical, dry/desertic, temperate, continental) are considered. Although, in terms of accuracy of Cp prediction, LES outperforms RANS for all the wind directions considered, annual cooling energy demand is found to be relatively insensitive to the source of Cp for the current case study, while predicted peak cooling values differ up to 10.8%. On the other hand, the prediction of annual heating energy demand in cold climates varies up to 3% depending on the Cp source employed for BES simulations.

Incorporating Citizen Science to Enhance Public Awareness in Smart Cities: The Case Study of Balaguer

The concept of a smart city is becoming increasingly popular to improve citizens’ quality of life. Institutions are also committed to enhancing the sustainability of cities by implementing the Sustainable Development Goals (SDGs). This paper presents a Balaguer case study investigating energy demand monitoring, decreasing energy demand, and citizen acceptance in a municipality district. The study collected data from three sources: (1) quantitative data coming from on-site sensors; (2) quantitative data from a simulation of the area; and (3) qualitative data from questionnaires developed with a totem located in the city center. This study shows the importance of citizen science in contributing towards the increased awareness of energy demand, renewable energy, and climate change. But it also shows how citizen science can improve research quality involving the municipality authorities. This study also was instrumental in contributing to the increase in awareness among municipality authorities and capacity building on the topic. This activity may also contribute towards the implementation of actions to reduce the energy demand in public buildings and helping them in deploying policies to decrease energy demand in buildings, increase the use of renewable energy, and increase awareness among citizens. The government will use the information gathered to develop policies for citizen improvement.

Thermal performance of an active casing pipe macro-encapsulated PCM wall for space cooling and heating of residential building in hot summer and cold winter region in China

Phase change material (PCM) wall is expected to match the building type and its climate region effectively. In order to meet the space cooling and heating of residential buildings in hot summer and cold winter regions in China, a new active casing pipe macro-encapsulated PCM wall is proposed, in which the cold storage and heat storage PCMs are sealed between the inner pipe and outer pipe with no leakage. To obtain the optimal structure parameters and better thermal performance of the PCM wall, the mathematical model of the PCM wall is established and validated by experiment, and the effects of PCM filling pattern, structural parameter of casing pipe, water temperature in inner pipe and indoor setting temperature are simulated and analyzed. The results show that the optimal PCM filling pattern is to fill the cold storage PCM and heat storage PCM in the gap between the inner pipe and outer pipe separately; the optimal PCM filling thickness is 13 mm; the optimal structural parameters are inner pipe diameter of 20 mm and casing pipe centre spacing of 61 mm. The water temperature in the inner pipe and PCM energy storage/release time reasonably match the indoor setting temperature. Under the optimal operating conditions, the average PCM wall surface temperatures of 20.5 and 26.8 ℃ and the PCM wall surface heat fluxes of 45.9 and 57.2 W/m2 can be obtained in summer and winter conditions, respectively. The research provides an optimized casing pipe macro-encapsulated PCM wall system that can reliably and efficiently meet both the space heating and cooling demands of residential buildings in hot summer and cold winter regions in China.

Polymer/cement composite electrolyte with high strength and high ionic conductivity for structural supercapacitors

All-solid-state structural supercapacitors (ASSC) based on polymer/cement electrolytes (PCE) are promising energy storage devices in buildings due to the tremendous demand for zero-energy buildings. However, the challenge of PCE remains in well balancing the mechanical properties and ionic conductivity. Herein, an integrated electro-mechanical PCE introducing PAM (20 wt%, synthesized by in-situ polymerization) and PAM (5 wt%, commercial) simultaneously largely improves the ionic conductivity (68.54 mS/cm) and mechanical properties (47.96 MPa). Surprisingly, PAM (20% wt%, in-situ polymerization synthesis) and PAM (5% wt%, commercial) were introduced simultaneously to modify PCE, which not only had a synergistic effect on compressive strength but also had a synergistic effect on ionic conductivity. Moreover, an assembled ASSC based on 20-AM-5-PAM PCE exhibits excellent performance with an areal energy density of 25.17 μWh/cm2 at a power density of 50 μW/cm2 and ～96% capacitance retention after 5000 cycles. These results demonstrate the great potential for the application of 20-AM-5-PAM PCE in high-strength and large-volume building structure energy storage devices.

The influence of torsion on acceleration demands in low-rise RC buildings

AbstractThis paper presents a study of acceleration demands in low-rise reinforced concrete (RC) buildings with torsion, evaluated by quantifying peak floor accelerations (PFAs) and floor response (acceleration) spectra (FRS). The study was performed with the aim to provide simple empirical formulas to quantify the amplification effects due to torsion, which can occur in most of the existing and new RC buildings. With this goal in mind, a set of eight archetype buildings was selected, characterized by an increasing floor eccentricity obtained by moving the centre of rigidity (CR) away from the centre of mass (CM). Numerical models of the proposed set of archetype RC buildings were considered in both linear elastic and nonlinear configurations. For the latter, the properties of models were widely varied, by systematically modifying parameters of plastic hinges, in order to obtain a sample of 1000 models. Non-structural components (NSCs) were considered linear elastic in all cases. To investigate acceleration demands, a set of forty Eurocode 8 spectrum-compatible ground motion records were used as input. For linear elastic building models, it was observed that the change of demands depends on the position of the NSC (in-plan and in-height), and on the distance between CR and CM. On the other hand, for nonlinear models, additional parameters must be considered, such as the building ductility (μ) and yielding force (Vy). New regression models were proposed for quantifying the observed differences in PFAs and FRS when torsion occurs. The efficiency of the proposed models was assessed by testing the new formulas on an existing case study building, as well as on the well-known SPEAR building.

Research on complex network modeling of building materials supply and demand and characteristics of communities

Currently, China's building materials market is large and the supply and demand transactions are very frequent, which requires us to have a comprehensive understanding of the supply and demand transactions of building materials. Based on complex network theory, this paper constructs a complex network model of supply and demand of building materials. And the Louvain algorithm is also improved to identify and characterize the network community relations based on the characteristics of this network. This paper also applies prefabricated components as an example for empirical research and obtains the following findings: (1) From 2017 to 2022, large- and medium-sized communities in the network gradually increase while small communities gradually decrease; the internal connectivity of large communities is higher than that of small communities; and the regional network also has the structural characteristics of the network. (2) The characteristics of geographic agglomeration gradually emerge in the individual communities in the supply–demand network of prefabricated components with the passage of time. Most of these communities are bounded by provinces, and large-scale communities are distributed in the eastern and southern coastal areas. Thus, this paper visualizes the supply and demand of construction materials to provide a theoretical basis and methodological support for the supply and demand of construction materials and the development of the construction industry.

Exploring the effects of a warmer climate on power and energy demand in multi-family buildings in a Nordic climate

The need to understand how a warmer climate affects the power and energy demand in cold countries is important for urban planners and policymakers. By using data from utility bills that are commonly available today, together with outdoor temperatures, it is possible to analyze historical and future power and energy demand. The scientific value of this research includes the development of a methodology to explore effects on future heat demand in the Nordic region based on a combination of historical data, building properties, and predictions of future climate. This is achieved by using an energy signature model and regression analysis. Seventy multi-family buildings in Linköping, Sweden, are investigated from 1980 to 2050. The results show that the effects from historical variations in internal heat gains (average annual increase of 1 %) on the specific energy use for space heating (SPH) is minor for the district, i.e., less than 2 % when comparing 2020 and 1980. The opposite is found for variations in outdoor temperatures, where the average specific energy use is predicted to decrease by about 25 % in 2050 compared to 1980, with the used forecast of future climate. This corresponds to a decrease from 127 kWh/(m2·year) to 93–96 kWh/(m2·year). Additionally, the maximum heating power demand of the district is predicted to decrease by about 30 %, from 4,855 kW in 1980 to 3,468 kW in 2050. In conclusion, our results demonstrate a strong effect of decreased SPH and heating power demand in residential districts due to a warmer climate.

A CASE STUDY: INTELLIGENT SHADING RETROFIT TO EXISTING HOME-OFFICE USING MULTI-OBJECTIVE OPTIMIZATION

ABSTRACT Improved energy performance and occupant comfort are driving building design decisions due to the increasing demand for sustainable and green buildings. However, despite the variety of technological developments in other fields, the range of solutions to improve building performance is limited. One of the main limitations for an early designer is a performance evaluation method to facilitate the design process. This paper offers a new shading performance optimization process that can help designers evaluate both daylighting and energy performance and generate optimized and flexible designs that can be further improved by implementing user-specific automation. The proposed performance optimization method utilizes parametric design, building simulation models, and Genetic Algorithms. Common shading design systems are explored through parametric design, and daylighting and energy modeling simulations are performed to evaluate shading device performance. Genetic Algorithms are used to identify design options with optimal energy and daylighting performance. A case study is conducted to verify the effectiveness of the overall process. Results are used to analyze the influence of design decisions among different shading designs. Finally, future directions in both shading design and energy optimization are presented.

Transformer based day-ahead cooling load forecasting of hub airport air-conditioning systems with thermal energy storage

The air conditioning system constitutes more than half of the total energy demand in hub airport buildings. To enhance the energy efficiency and to enable intelligent energy management, it is vital to build an accurate cooling load prediction model. However, the current models face challenges in dealing with dispersed load patterns and lack interpretability when black box approaches are adopted. To tackle these challenges, we propose a novel k-means-Temporal Fusion Transformer (TFT) based hybrid load prediction model. Specifically, the daily load patterns are grouped using an improved k-means clustering method that considers both input feature weights and dynamic time warping (DTW) distances. Additionally, the statistical features of the clustering output are inputted into the TFT. By further incorporating context information, the integration of data between different schema categories is achieved, thus reducing errors that may occur during the transition process. As a result, the prediction performance and interpretability are significantly improved. The Chongqing Jiangbei Airport T3A terminal is used as a case study, and experiments are conducted using cooling data from the No.1 energy station, as well as the airport traffic data and the meteorological station data. Results are compared with other mainstream models, confirming that the proposed day-ahead load forecasting model achieves improvements in several performance indicators, including MAE, MAPE, CV-RMSE, and R2, which are 384 kW, 3%, 5%, and 0.058 respectively.

3D Modeling of Façade Elements Using Multi-View Images from Mobile Scanning Systems

There is a growing demand for detailed building façade models (Level-of-Detail 3: LoD 3) in a variety of applications. Despite the increasing number of papers addressing this issue in the literature, occlusions are still a significant problem when processing building façade elements. Conversely, depending on the view angle of the images, the detected elements might not be projected to their accurate locations causing uncertainties in their 3D locations. In this paper, we address the aforementioned issues utilizing multi-view images. Using a building footprint layer, we first locate the points belonging to buildings. We then detect the 2D windows and doors in images by combining Faster R-CNN and Segment Anything (SAM) deep learning models. The 2D borders are projected into the 3D object space using a pinhole camera model and collinearity equations. Utilizing the multi-view capabilities of mobile scanning systems, this method effectively mitigates uncertainties associated with occlusion and exterior orientation parameters (EOP). This study provides a comprehensive evaluation of 3D spatial accuracy, achieving an average of 84% Intersection over Union (IoU) accuracy for 12 different single-sided façades over 750 multi-view images for 312 windows and doors of various sizes with rectangular and curved shapes.

The impact of climate change on an office building in Portugal: Measures for a higher energy performance

Even though global warming is expected to raise the energy demand in office buildings, most energy analyses and design studies neither consider its impacts nor adaptive energy solutions. This study evaluates the effects of future climate on the energy demand of an office building in Portugal and proposes mitigation measures to improve its energy performance and environmental sustainability. The study simulates the energy demand of the building in 2055 and 2090 under different future scenarios using data from the latest climate model experiments, considering systems, envelopes, and operations interventions, and assesses the potential of photovoltaic panels to meet the energy demand. The results show that the interventions may reduce electric energy consumption between 16 % and 25 %, and the photovoltaic panels can meet 23 %–59 % of the total demand, depending on the scenario and the year. However, storage technology and equipment efficiency must evolve favorably to reach low energy. The findings reveal if a building designed and optimized for the current climate will remain optimal in the future and demonstrate the benefits of applying cleaner production and sustainable consumption principles to the office building sector. The study provides valuable insights for practitioners and policymakers.

Towards a net-zero-energy building with smart control of Trombe walls, underground air ducts, and optimal microgrid composed of renewable energy systems

In recent years, permanent outages and lack of access to electricity have been significant problems in most cities of Iran. The increasing growth of fossil fuel consumption made it necessary to change the design of buildings. Designing a net-zero energy building (with natural ventilation and renewable energy systems) is a solution for reducing fossil fuels. In this paper, dynamic energy modeling of the building for the smart operation of the trapezoidal Trombe wall and underground air ducts is performed. In the first step, the Trombe Zones are considered for heating. Then the combination of the Trombe zones with underground air ducts for cooling is simulated. Then the system combination with a solar hot water system to supply hot water energy is evaluated. Finally, the economic analysis of the optimal hybrid system (with a photovoltaic system, wind turbine, battery, and inverter) for supplying the electricity of the building equipped with both passive energy strategies was evaluated by Homer software. The results show that by integrating the Trombe wall, the heating load is reduced by 44 %, and by integrating a coupled geothermal cooling system with the air duct and the Trombe wall, the cooling load is reduced by 39%. Also, the net present cost over the 25-year life of the optimized renewable system is reduced by about 12.5% due to both passive strategies. The average daily electricity energy demand of the base building is equal to 86.37 kWh and for the passive building, it is equal to 67.49 kWh.