Affect Building Energy Research Articles

Assessing Building Energy Savings and the Greenhouse Gas Mitigation Potential of Green Roofs in Shanghai Using a GIS-Based Approach

Climate change can significantly affect building energy use and associated greenhouse gas (GHG) emissions in urban areas, as fossil fuels remain a significant energy source. Green roofs can offer multiple benefits to the urban environment, but their effects on GHG mitigation have not been fully investigated, especially under climate change. This study assessed green roofs’ contribution to GHG mitigation by saving building energy and absorbing CO2 under the present (2017–2019) and future (2049–2051) climate scenarios (SSP2-45 and SSP5-85) in Shanghai, China, at the city and township scale. A Geographic Information System (GIS)-based spatial statistical method was developed based on climate change modeling and building energy simulation. The results suggested that installing green roofs can effectively save building energy regardless of building type, yet the amount of savings can vary depending on the weather conditions within the city. The contribution analysis indicated that most saved building energy was attributed to the Heating, Ventilation, and Cooling (HVAC) system, with more energy saved under warmer climate scenarios in the future, particularly during the summer months. More energy was saved from shopping malls on an annual and monthly scale, regardless of the climate scenarios and weather zones. Finally, a case study indicated installing green roofs on all five types of buildings (office, hotel, hospital, shopping mall, apartment) of less than 50 m in height can reduce 8.28% of the CO2 emitted during the building operation stage in the entire city under the present climate scenario. The annual CO2 reduction varied with the location of townships, ranging from 2.18% to 13.78%, depending on the composition of building types and local weather conditions in Shanghai. This study offered policymakers a reference on the environmental benefits and investment values of installing green roofs in large cities.

The synergistic effect of multiple design factors on building energy consumption of office blocks: A case study of Wuhan, China

Design factors such as block morphology, building form and building envelope performance are key parameters affecting building energy consumption of urban blocks. However, the synergistic effect of multi-design factors on building energy consumption has not been studied in detail. This paper aims to investigate the synergistic effect of multi-design factors on building energy consumption of office blocks in Wuhan, China. This study employs field survey, satellite map and open street map to extract the typical building forms and construct parametric models of office blocks. Then, 81 representative scenarios considering the synergy of multiple design factors in office blocks are built. Then, based on the Rhino & Grasshopper parametric platform, a building energy assessment tool that considers the synergistic effect of multiple design factors is developed to simulate and record the building energy consumption of each scenarios. Finally, the synergistic effect of multi-design parameters on building energy consumption of office blocks is quantified using correlation and multiple linear regression methods. The findings reveal that the synergistic effect of multi-design factors on building energy consumption is 18.53 %. Building energy consumption for high-rise pavilion typology is saved by 9.64 % than multi-storey courtyard. The key design parameter combinations affecting building energy consumption in office block are building shape factor, solar heat gain coefficient, wall K-value, building width and window-wall ratio of west and east. This study contributes to a deeper understanding of the synergistic effects of multiple design factors on building energy consumption in office blocks and presents a scalable model that can be applied globally to promote sustainable urban development.

Microclimatic models and their implications on the energy requirements of buildings in warm dry urban areas

Understanding the parameters that amplify or mitigate temperature increases in the built environment is crucial to reducing the energy demand of buildings and achieving urban resilience. This research examines how microclimate scenarios can affect building energy performance forecasts in cities with temperate and dry climates. The aim is to calculate the variation in building energy requirements resulting from using different weather files and urban parameters using the UBEM tool. To achieve this, UWG and Energy Plus software were used to create urban and building models. The study analysed four microclimatic scenarios in the city of Mendoza, Argentina. The scenarios used as a weather file input the annual format of a specific year (AMY files) or long-term (TMY file), whilst also considering the specific urban parameters of the assessed area. The study found that an incorrect weather file can cause a 36% error in energy usage assessments for cooling buildings during summer months. The contribution of this study is the development of a methodology for obtaining a weather file that accurately reflects the microclimate characteristics of the urban area of interest.

Spatial distribution of energy consumption: Integrating climate and macro-statistics for insights from clustering and sensitivity analysis

Limited comprehensive methods exist for studying spatial energy consumption distribution, integrating statistical and energy data. This paper introduces a novel approach for analyzing residential heating and cooling energy demand distribution. It employs clustering algorithms to study climate variables’ impact on energy demand distribution and assesses building energy demand intensity regionally, taking China as a case study. Initially compiling a dataset comprising climate characteristics, socioeconomic factors, and energy demand through data collection and simulation, the study compares clustering algorithms, highlighting the effectiveness of K-means in clustering high-dimensional climate-energy datasets. K-means analysis reveals temperature-based daily methods significantly affect building energy intensity, alongside factors like radiation intensity and humidity impacting regional energy demand variably. Additionally, climate’s influence on residential building energy consumption intensity varies regionally, with total energy demand influenced by population and economic factors. This paper offers insights for energy management and policy formulation.

Evaluation of energy-saving potential and indoor thermal environment of passive solar houses in various heating climate regions of China

ABSTRACT Passive solar houses comprise crucial strategies of reducing heating and cooling energy for buildings, which have been extensively used in plenty of nations. This study develops a study on indoor thermal environment, the energy-saving performance and natural lighting in different heating climate regions (represented by Lhasa, Xining and Urumqi). An attached-sunspace solar house integrated with phase change material floors is also proposed to ameliorate indoor thermal comfort and simultaneously diminish the heating energy consumption. In addition, the dominant factors analysis of building envelope affecting building energy consumption are sensitively studied. Moreover, the influence of the depth of sunspace and the intensities of internal heat gain on indoor thermal conditions and heating energy consumption are further analyzed. The results indicate that among the three types of solar houses, the passive solar sunspace offers the most stable indoor temperature and Trombe-wall solar houses and attached-sunspace solar houses have striking energy-saving advantages compared with the traditional houses. And the annual power consumption of 695.1 kWh, 427.4 kWh and 688.8 kWh in Lhasa, Xining and Urumqi can be saved in view of natural lighting respectively. Additionally, the whole natural temperature of attached-sunspace solar house is on the rise apparently when the PCM floors are embedded in the house and the energy saving fraction of these three regions is 81.9% for Lhasa, 68.8% for Xining, 55.7% for Urumqi respectively with the integration of phase change floors. The sensitivity analysis of key parameters impacting building energy consumption from high to low are the heat transfer coefficient of external wall, the ratio of window to wall, air tightness, the heat transfer coefficient of external window, and the heat transfer coefficient of roof. The depth of 1.2 m for sunspace is recommended as the optimal choice. Indoor natural temperature is on the rise as intensities of internal heat gain increase. The same downward trend for the main bedroom and the whole building is displayed with the increasing interior heat sources in terms of annual heating load.

A data-driven agent-based model of occupants’ thermal comfort behaviors for the planning of district-scale flexible work arrangements

In a global context of increasing flexibility in the way workplaces and the districts in which they are located are used, there is a need for occupant-driven approaches to plan urban energy systems. Several authors have suggested the use of agent-based models (ABM) of building occupants in urban building energy simulations due to their ability to reproduce emergent behaviors from individual agents’ actions. However, few works in the literature take full advantage of the ABM paradigm, accounting for both occupant presence and energy-relevant behaviors at the district scale. In this work, we propose a methodology to develop a data-driven, agent-based model of building occupants’ activities and thermal comfort in an urban district. Our methodology combines the use of campus-scale Wi-Fi data to derive feasible occupant activity and location plans, along with thermal preference profiles derived from a longitudinal field study where off-the-shelf, non-intrusive sensors were used to capture the right-here-right-now thermal preference of 35 participants in the same case study district. Our model is then used to explore how different district operation strategies could affect building energy performance in the context of increased workspace flexibility. Our results show that by providing a diversity of building operation conditions, with different buildings having different set point temperatures, occupants’ thermal comfort hours could be improved by an average of about 10% with little effect on district energy performance. Meanwhile, a 6%–15% average decrease in space cooling energy use intensity was observed when implementing occupant-driven ventilation and setpoint controls, regardless of location choice scenario.

Effect of different generated weather climate data file on building energy consumption in different climates of Iran

Accurate estimation of building energy consumption is critical, and it can usually be calculated using simulation software like Energy Plus. The input weather data for software is typically a typical or reference year as a standard weather file, such as TMY2. To best estimate building energy consumption, TMY2 data must be generated intelligently. In the present study, first, several TMY2 data files have been generated from different periods (5–21 years) of available measured weather data (9 separate files). Available measured weather data is for 1998 to 2019. Also, a TMY2 file has been generated from the long-term average for the total period (1998–2019), and two TMY2 files have been generated using the CCWeatherGen software for 2020 and 2050. In the next step, the calculated building energy consumption from those files for an educational building will be compared using Energy Plus software. The primary purpose of this study is to investigate the impact of different generated TMY2 (from different weather data periods) on energy consumption and determine the best period to generate TMY2 files from the available period of 5 to 21 years for the capital of Iran (Tehran), which has a semi-arid climate, and Tabriz, which has a cold and mountainous climate. These cities have different climates. Results show that for Tehran, building heating energy consumption changes by about 10% from other generated TMY2 files (one file generated from 2009 to 2019 and another generated from the 2007 to 2019 period). The result shows that generating TMY2 files from different weather data periods affects building energy consumption. So, a suitable strategy for generating TMY2 files is necessary. As a new strategy, the present study proposes that TMY2 files should be generated based on building cooling and heating energy from recent years. Also, because climate change is an important parameter, a TMY2 file should be generated from recent years’ weather data to accurately predict building energy consumption.

BEM to BIM in the early design phase: A comparison between static and dynamic heating energy predictions

Due to climate change and increasing pressures on resources, the demand for more energy-efficient buildings is increasing globally. Building information modeling (BIM) and building energy modeling (BEM) are two essential tools to make the necessary transition to net-zero energy buildings (NZEB). This article presents two case studies aiming to automate information from the BIM model to predict annual heating energy use at the early design phase (EDP) using static energy calculations. This article presents a comparison between results obtained with the static and dynamic energy calculations with the building energy simulation (BES) package IDA-ICE. The goal of the static calculations is to allow working directly in the BIM model in real time to obtain annual energy use based on building surfaces, heated floor area, heated volume, and other inputs related to the heating degree-day (HDD) method. This article shows that the static method provides results that differ by ±25% from results of the dynamic method, which is sufficiently precise at an early design phase to provide guidance to the architects, who make key decisions affecting building energy performance.

Statistical Building Energy Model from Data Collection, Place-Based Assessment to Sustainable Scenarios for the City of Milan

Building energy modeling plays an important role in analyzing the energy efficiency of the existing building stock, helping in enhancing it by testing possible retrofit scenarios. This work presents an urban scale and place-based approach that utilizes energy performance certificates to develop a statistical energy model. The objective is to describe the energy modeling methodology for evaluating the energy performance of residential buildings in Milan; in addition, a comprehensive reference dataset for input data from available open databases in Italy is provided—a critical step in assessing energy consumption and production at territorial scale. The study employs open-source software QGIS 3.28.8 to model and calculate various energy-related variables for the prediction of space heating, domestic hot water consumptions, and potential solar production. By analyzing demand/supply profiles, the research aims to increase energy self-consumption and self-sufficiency in the urban context using solar technologies. The presented methodology is validated by comparing simulation results with measured data, achieving a Mean Absolute Percentage Error (MAPE) of 5.2%, which is acceptable, especially considering city-scale modeling. The analysis sheds light on key parameters affecting building energy consumption/production, such as type of user, volume, surface-to-volume ratio, construction period, systems’ efficiency, solar exposition and roof area. Additionally, this assessment attempts to evaluate the spatial distribution of energy-use and production within urban environments, contributing to the planning and realization of smart cities.

The role of socio-demographic and economic characteristics on energy-related occupant behavior

Building-related sectors use more energy than all other energy consuming sectors. Energy consumption behavior of occupants and their socio-demographic profiles are some of the key factors affecting building energy consumption. This study aims to understand the relationships between the socio-demographic and economic characteristics of occupants and their energy-related behavior. To achieve this aim, six hypotheses are developed and tested over three steps: (1) identification of the changes in the occupants’ energy-related behavior before and after being exposed to energy-saving interventions, (2) measurement of the socio-demographic profiles of the occupants, and (3) use of several machine learning methods to capture the relationships and test the hypotheses. Using decision tree learning to interpret the results, we find that education level, income, and age have the largest impact to predict the energy consumption behavior of occupants. The results also show four occupant profiles prone to switch to lower energy use: (1) age between 20 and 39 years old, education level of high school degree or lower, and income below 20 k; (2) age of 30 years old or younger, education level of high school degree or lower, and income above 100 k; (3) age of 40 years old or older, education level of bachelor's degree or lower, and income below 20 k; and (4) age of 59 years old or younger, education level of Master's degree or higher, and income above 100 k. This study can help decision-makers (e.g., utility companies, policy makers) to tailor incentive programs and policies to ultimately lower global building energy consumption.

How national decarbonisation scenarios can affect building refurbishment strategies

Energy transition is radically changing national energy systems. Nevertheless, the dynamics of this transformation are not considered by end-users in the design of building systems. The present work aims at assessing how the renewable share increase, in both electricity and gas grids, can affect building energy performance. To do this, building energy performance indicators, taking into account growing renewable shares, have been proposed. Four national decarbonisation scenarios have been considered. In a case-study in Italy, conventional boilers, heat pumps, combined heat and power plants and hybrid systems have been analysed. Heat pumps turn out to be the best option if the renewable penetration in the power grid is higher than 40%. The substitute natural gas deployment can increase the competitiveness of cogeneration systems, but not enough to represent the best configuration. National decarbonisation scenarios significantly affect the primary energy and emissions savings of building refurbishment strategies. Conventional indicators, taking primary energy factors as fixed, lead to correct assessment for the reference year, but are unable to describe the actual building energy performance over the system lifetime. Depending on the scenario, the average specific primary energy consumption ranges in 17% and 55% lower than the one assessed with conventional analyses.

A data-driven framework for building energy benchmarking and renovation decision-making support in Sweden

In Europe, the buildings sector is responsible for 40% of energy use and more than 30% of buildings are older than 50 years. Due to ageing, a large number of houses require energy-efficient renovation to meet building energy performance standards and the national energy efficiency target. Although Swedish house owners are willing to improve energy efficiency, there is a need for a dedicated platform providing decision-making knowledge for house owners to benchmark their buildings. This paper proposes a data-driven framework for building energy renovation benchmarking as part of an energy advisory service development for the Västerbotten region, Sweden. This benchmark model facilitates regional homeowners to benchmark their building energy performance relative to the national target and similar neighbourhood buildings. Specifically, based on user input data such as energy use, location, construction year, floor area, etc., this model benchmarks the user’s building performance using two benchmark references i.e., 1) Sweden’s target to reduce buildings by 50% energy use intensity (EUI) by 50% by 2050 compared to the average EUI in 1995, 2) comparing user building with the most relevant peer group of buildings, using energy performance certificates (EPC) big data. Several building groups will be classified based on influential factors that affect building energy use. Hence, this benchmark provides decision-making supportive knowledge to homeowners e.g., whether they need to perform an energy-efficient renovation. In the future, this methodology will be extended and implemented in the digital platform to provide helpful insights to decide on suitable EEMs. This work is an integral part of project AURORAL aims to deliver an interoperable, open, and integrated digital platform, demonstrated by cross-domain applications through large-scale pilots in 8 regions in Europe, including Västerbotten.

The impact of urban morphology on the building energy consumption and solar energy generation potential of university dormitory blocks

Urban morphology is a major factor affecting building energy consumption and solar potential in the urban block. The aim of this research was to evaluate the impact of urban morphology on both building energy consumption and solar energy generation potential for university dormitory blocks in Wuhan. This paper proposed a classification method for dormitory blocks, calculated the building energy consumption and solar energy generation potential of 55 blocks, and analyzed the correlation between urban morphology and three energy performance indicators: Energy Use Intensity (EUI), Solar Energy Generation Intensity (SEGI) and Net Energy Use Intensity (NEUI). Multiple regression models were used to predict energy performance by urban morphological parameters. The results indicate that different block types could lead to up to 12.25% difference in EUI, and 35.85% significant difference in building NEUI with the deployment of photovoltaic (PV) panels. The EUI is mainly affected by three morphological parameters, which are Average length of block, Shape factor and Building density; while the SEGI and NEUI are mainly affected by Average height of block, Shape factor and Sky view factor. This study could serve as guidelines for planners and policymakers in campus planning and architectural design to improve building energy conservation at block-scale.

Experimental investigation of the effect of salt on the hygroscopic properties of autoclaved aerated concrete

The hygroscopic properties of autoclaved aerated concrete (AAC) significantly affect building energy consumption. However, the impact of salt on the hygroscopic properties of AAC in buildings in marine environments has not been sufficiently investigated. Thus, we tested the hygroscopic performance of AAC specimens with different salt contents at various humidity levels. The results show that the equilibrium moisture content (EMC) of the AAC is 0.98–1.25 (0.99–3.68) times higher in the presence of salt than in the absence of salt at low (high) relative humidity (RH) (RH threshold of 75.5%). Furthermore, an equation to predict the EMC of salt-containing materials was proposed based on existing adsorption equations and the results of this study. A comparison with related studies indicates that the reason for the high prediction value of the proposed prediction model may be an insufficient moisture absorption time. The proposed model can be used to calculate the EMC of building materials with different salt contents.

A Review of Data-Driven Building Energy Prediction

Building energy consumption prediction has a significant effect on energy control, design optimization, retrofit evaluation, energy price guidance, and prevention and control of COVID-19 in buildings, providing a guarantee for energy efficiency and carbon neutrality. This study reviews 116 research papers on data-driven building energy prediction from the perspective of data and machine learning algorithms and discusses feasible techniques for prediction across time scales, building levels, and energy consumption types in the context of the factors affecting data-driven building energy prediction. The review results revealed that the outdoor dry-bulb temperature is a vital factor affecting building energy consumption. In data-driven building energy consumption prediction, data preprocessing enables prediction across time scales, energy consumption feature extraction enables prediction across energy consumption types, and hyperparameter optimization enables prediction across time scales and building layers.

Research on indoor thermal comfort and energy consumption of zero energy wooden structure buildings in severe cold zone

Indoor thermal comfort level not only affects the indoor thermal environment but also affects building energy consumption. Generally speaking, the higher the thermal comfort of the building, the greater its energy consumption. Therefore, in severe cold zone with the most stringent thermal engineering standards in China, how to reduce building energy consumption while improving building thermal comfort is of great significant for the building in this region to achieve zero energy consumption. In order to achieve this research goal, this paper designs two kinds of wooden structure buildings with water-passing wall in severe cold zone. Through combination of simulation and scale model experiments, the performance of water-passing wall, thermal comfort performance of wooden structure building, and building energy consumption were studied. The research results indicate that the water-passing wall of the wooden structure building has good thermal stability and thermal insulation performance, which can isolate outdoor cold in winter. Moreover, due to the function of water-passing wall, the two kinds of wooden structure buildings have high thermal comfort levels throughout the year, with occupants feeling uncomfortable for no more than 6% of the year. It is compared with the energy consumption before the water supplying to the wall, the total energy consumption of the two kinds of wooden structure buildings were reduced by 44.3% and 46.2% respectively. Therefore, these two kinds of wooden structure building can pursue higher thermal comfort with lower energy consumption, it has high energy-saving benefits.

Multi-objective building energy system optimization considering EV infrastructure

With increasing concerns over carbon dioxide emissions, the concept of Zero Energy Building (ZEB) has emerged. Electric Vehicles (EVs) are also considered environmentally friendly since they reduce greenhouse gas emissions, with a rapidly growing market. With these global trends of increasing EV amount and infrastructure, building energy systems should incorporate the ZEB concept and the increasing electricity requirements for EV charging. However, it is unclear how EV charging demand can affect building energy system design while aligning with ZEB requirements. Therefore, this paper develops a new framework to find the optimal energy system design that meets EV charging demand and ZEB requirements. The charging demand for EVs is predicted by the machine learning model, which combines the building energy demand from EnergyPlus. Ultimately, the Genetic Algorithm and PROBID method are applied to optimize the Total Annual Cost (TAC) and Self-Energy Sufficiency Ratio. EV charging demand has been found to affect energy system design, especially in small-size buildings. Using the proposed method, the building owner can determine the optimal capacity of an energy system based on economic and ZEB conditions, contributing to the future net ZEB and transportation systems.

Impact of urban form on building energy consumption and solar energy potential: A case study of residential blocks in Jianhu, China

Urban form can significantly affect building energy consumption and solar energy potential, which is essential to sustainable urban development. However, few studies have proposed a comprehensive framework to discuss this. In addition, how to bridge the gap between theory and practice, and how to combine quantitative research results with urban form design is also a matter of concern. Based on this background, this study proposes a multi-objective urban form design optimization framework based on the Grasshopper platform. Three optimization objectives were identified: minimum building energy consumption, maximum solar potential and maximum sunlight hours. Then, taking Jianhu in China as an example, the building types are extracted from the actual urban context to create blocks. The Ladybug Tools plugin is used for performance simulation, and multi-objective optimization is run in Wallacei X. Furthermore, based on the optimization process data, a correlation analysis of urban form factors with building energy consumption and solar energy potential is conducted. Finally, the research findings are integrated, and energy-efficient urban form design strategies are proposed. The research results show that a block form with excellent energy performance in Jianhu has the following characteristics: (i) The open space is often located in the center of the block. High-rise buildings are located on the west side. (ii) The block presents a layout consisting of a low south side and high north side. (iii) The orientation of the block is 15° south by westor due south. (iv) Avoid building types with large shape coefficient such as P-4, C-1, S-3 and C-2. In addition, building density (BD), open space ratio (OSR), shape coefficient (SC) and average perimeter-area ratio (PAR) have significant correlations with the total building energy use intensity of the block. For solar potential, floor area ratio (FAR), BD, the average number of floors (AF), OSR, SC, and PAR are highly correlated with rooftop PV energy generation, and sky view factor (SVF) is significantly correlated with rooftop PV energy generation.

Heat and moisture transfer in wall-to-floor thermal bridges and its influence on thermal performance

A significant portion of the cooling or heating load of buildings is contributed by relatively large heat flux through thermal bridges. Water vapour concentration significantly alters thermal conductivity and heat transfer characteristics within the thermal bridges, affecting building energy consumption, particularly in humid climates. Moreover, the condensation on the building envelope is highly related to the temperature and moisture distribution, which threatens the safety of the building structure. The heat and moisture transfer presents strong coupling effects. Nevertheless, the coupled heat and moisture transfer (HAMT) within thermal bridges and its influence on the building energy consumption were less studied. In this study, field experiments and validated numerical models were conducted to investigate the heat and moisture transfer in the wall-to-floor thermal bridge (WFTB) in a humid and hot summer cold winter region of China (Hangzhou city). The results show that the heat transfer is underestimated if the moisture is not considered. The differences in heat transfer between the WFTB and the main part of the wall were reduced when considering the coupling effect of heat and moisture transfer, as the thermal performance of the wall is more sensitive to moisture than that of the WFTB region. Furthermore, if the significant moisture transfer is considered, the overall thermal conductivity of the WFTB has a risk of counterintuitively increasing when a thermal insulation layer is added. The reason for this is that the thermal insulation layer may raise the moisture content in the WFTB region, subsequently increasing the thermal conductivity.

Explanatory Optimization of the Prediction Model for Building Energy Consumption.

Traditional prediction models, which are based on artificial neural networks (ANNs), consider the various factors affecting building energy consumption comprehensively. However, their explanatory power is not ideal in actual application, resulting in prediction errors of building energy consumption. Thus, this paper pursues the explanatory optimization of the prediction model for building energy consumption. First, the authors displayed the architecture of the prediction model for building energy consumption, which is based on the temporal pattern attention mechanism (TPAM), and explained the principle of predicting building energy consumption. Then, the input of the TPAM was illustrated, and the execution steps of the model were depicted. Based on feature importance and the Shapley additive explanations (SHAP) method, the explanatory power of the proposed prediction model was analyzed, from the perspective of the time series features of building energy consumption prediction. The proposed model was proved effective through experiments.