Building Energy Use Research Articles

Physics-informed ensemble learning with residual modeling for enhanced building energy prediction

Accurate modeling of building energy use is important to support a diverse spectrum of its downstream applications, such as building energy efficiency assessment, resilience analysis, smart control, etc. As mainstream approaches of building energy modeling, physics-based modeling builds on different fidelities of physics rules yet are usually compromised in modeling accuracy due to insufficiency of physics rules to capture real-world dynamics and incomplete input information. Data-driven approaches are computationally efficient, but black box (uninterpretable) in nature. For improved modeling of building energy use, this work proposes a physics-informed ensemble learning approach in building energy prediction through residual modeling. Specifically, we first analyze the components of building energy use data. Evidence suggests that the building energy use data can be decomposed into the physics-driven part, occupant-driven part, and white noise. Second, high-fidelity physics-based building models (EnergyPlus) and low-fidelity ones (RC models) are developed to capture the physics-driven part while time series methods are explored as the residual modeling approach to capture the occupant-driven discrepancies between physics-based simulation and measured building energy use (i.e., residuals). Finally, the physics-informed ensemble learning is proposed to integrate physics-based and data-driven models for enhanced accuracy of building energy modeling. Results demonstrate 40–90% decrease of discrepancies between modeling and field observations compared to traditional physics-based modeling methods. Moreover, when the training dataset size is small, the proposed ensemble model overperforms the pure data-driven models, demonstrating its higher robustness in extrapolation scenarios. This work makes fundamental contributions to the development of convergent modeling approaches in the building modeling field.

Exploratory Analysis of a Novel Modular Green Wall’s Impact on Indoor Temperature and Energy Consumption in Residential Buildings: A Case Study from Belgium

One possible solution that mitigates the effects of climate change is the implementation of vertical greenery systems, which have the potential to reduce the need for cooling and provide energy savings for heating. This paper evaluates the effects of an innovative modular green wall on indoor temperature and energy use in a residential case study building. This research was carried out on a residential house in the city of Ghent, Belgium, whose southwest facade is covered with a specific type of modular green wall (a structure with a specific substrate and plants that have the ability to purify water so that it can be reused in the house). The monitoring process included four different temperatures (in front of and behind the green wall, in the substrate, and on the wall without greenery) during winter and summer periods. To analyze the effect on the internal temperature and energy use, a DesignBuilder simulation model was built and validated against these experimental results. This green wall has proven to have the greatest effect during the hottest summer days by reducing the indoor temperature by up to 3.5 °C. It also effectively increases the indoor temperature by up to 1.4 °C on a cold winter day, leading to energy savings of 6% on an annual basis.

Modelling Nigerian residential dwellings: bottom-up approach and scenario analysis

Nigeria’s residential buildings consume a substantial amount of the country’s energy, so achieving a net-zero building sector with a rapidly growing population is a key challenge. To bridge the gap in research at a national level and support Nigeria’s commitment to an unconditional 20% reduction in emissions by 2030, this study develops bottom-up archetype models of different residential building typologies to estimate the energy and material use of Nigerian residential buildings. This creates an overview of the residential stock and how different archetypes perform. The study calculates a baseline energy and material use of Nigeria’s residential building stock using the BuildME tool and converts these data into CO2 emissions using a life-cycle assessment. Scenarios are modelled for 2020. Nigeria’s residential dwellings use approximately 0.3 kt of material per dwelling over a lifetime of 50 years and 2404 kWh/yr of energy per dwelling. Annualised, dwellings emit 2500 kgCO2-eq per dwelling due to material and energy use. Scenarios proposed for meeting Nigeria’s emissions targets will require improved energy efficiency, decarbonising the building envelope through a shift in construction materials and decarbonising grid electricity. Policy relevance This study provides a comprehensive analysis of the energy and material use of residential buildings in Nigeria, focusing on achieving the country’s commitment to a 20% reduction in emissions by 2030. It employs a bottom-up approach to model energy and material use, revealing the peculiarities of the dwelling stock across Nigeria’s four climatic zones. The study explores the implications of different policy scenarios on sustainable housing. The research suggests that meeting Nigeria’s emissions targets requires improved energy efficiency, a shift in construction materials and decarbonisation of grid electricity. It also highlights the potential benefits of a policy switch to materials such as timber, earthen blocks, adobe bricks and clay, which could significantly reduce construction-related emissions. These changes could improve the quality of life of households in Nigeria, combat climate change on a global scale and bring economic advantages to Nigeria.

Prioritizing sustainability indicators for Uganda's built environment: Expert perspectives using the Delphi technique

The growing emphasis on energy efficiency within the built environment, driven by global concerns over climate change, has resulted in notable shifts in building energy consumption patterns. Achieving reductions in building energy use and CO₂ emissions through sustainable strategies necessitates the development of country-specific policies. While numerous energy assessment tools are available internationally, they often lack contextual relevance for Uganda and many Sub-Saharan African countries.Therefore, there is a pressing need for a tool tailored to Uganda's distinct climatic conditions. This study aimed to identify key building sustainability indicators to promote energy efficiency in Ugandan buildings. Utilizing the Delphi Technique, experts from academia, construction, energy consulting, and government agencies evaluated 35 building indicators over two assessment rounds. The consensus among experts was validated using the Interrater Agreement, with a significant Kendall's W value exceeding 0.6, rising from 0.655 in the first round to 0.704 in the second. Construction consultants showed the highest W value (0.77) in the second round, followed by energy consultants (0.74), while academicians had the lowest (0.7). The Chi-square test demonstrated strong agreement in ranking indicators across 34 degrees of freedom. The most significant indicators identified include "B20 - Optimization of energy use," "B8 - Advanced design and construction technology," and "B27 - Natural ventilation design," among others. The Delphi Technique was instrumental in achieving expert consensus, while interrater agreement was used to validate and ensure the reliability of the findings. The developed framework provides critical insights for policymakers, practitioners, and researchers seeking to advance energy efficiency and sustainability within Uganda's built environment.

From roads to roofs: How urban and rural mobility influence building energy consumption

Understanding the relationship between travel behavior and building energy use at an urban scale is crucial for developing effective energy management strategies. Mobility patterns significantly impact building occupancy, which in turn affects energy consumption. However, existing methods often focus on individual buildings, whereas geographical influences on energy usage are not adequately examined. This study addresses this gap by using transportation origin-destination (OD) data to estimate building occupancy and energy. The proposed method assigns OD trips from census block groups to the building level, incorporating building, travel survey, and census data to derive building occupancy profiles. This method was applied to urban and rural areas with 4062 buildings in 70 census block groups. We found that the OD-informed occupancy profile exhibits smoother energy consumption patterns compared with that of Department of Energy reference occupancy profiles. Our analysis reveals distinct building energy consumption patterns among groups with long and short commutes, emphasizing the effect of commute times and work schedules on residential energy usage. This framework is useful for practitioners in transportation agencies and utility companies, enabling the estimation of building energy based on mobility patterns. Overall, this study shows the potential of integrating transportation and building energy data to inform cross-sector energy management strategies.

Optimizing the Use of Water, Energy and Waste Resources in Green Buildings: Critical Review, Benefits and Challenges

Optimizing the Use of Water, Energy and Waste Resources in Green Buildings: Critical Review, Benefits and Challenges

Preparation and performance study of modified diatomite based PCM for construction

Phase change energy storage technology provides a good solution to the spatial and temporal mismatch of energy use in buildings, but organic solid–liquid phase change materials suitable for the building sector limit their application due to leakage problems. To mitigate the effects of the above problems, diatomite (DME) is designed as the carrier for encapsulation in this experiment. Adsorption performance of DME is improved through double effects of 600°C high-temperature calcination and hydrochloric acid modification. The binary low eutectic mixture was prepared by co-melting of lauric acid (LA) and octadecyl alcohol (OD) with mass ratio of 70:30. Shape-stabilized composite phase change material (SSCPCM) was prepared by vacuum adsorption method with LA-OD and DME with a mass ratio of 35:65 and the melting temperature and latent heat of SSCPCM are 39.10°C and 54.02 J/g. It was experimentally evaluated that the surface impurities of modified DME under microscopic conditions were removed and LA-OD was uniformly embedded in the micropores of DME, and the prepared SSCPCM was able to maintain a thermal inertia of 39°C with good thermal properties and thermal stability, thus SSCPCM could be used as a building energy-saving material to reduce building energy consumption, in addition to expanding the utilization of inorganic mineral materials.

Optimizing passive energy savings in rural self-built houses: Integrating phase change materials across China's climate zones

The energy consumption of rural residential buildings in China is increasing, and the integration of phase change materials (PCMs) has emerged as an effective strategy to mitigate building energy use and carbon emissions. Given China's diverse climate zones and the varying construction forms of rural self-built houses (RSHs), the efficacy of PCM applications varies significantly across regions. This study presents a comprehensive investigation into passive energy savings using PCMs in RSHs across various climate zones. We investigate the energy saving potential and thermal comfort effects of installing PCMs with varying phase change temperatures and thicknesses at different laying positions in representative buildings from each climate zone. The results indicate that the optimal phase change temperatures in different regions closely align with the outdoor average temperatures during the application season. When the optimal phase change temperatures and appropriate thicknesses of PCM are applied, cost-effective energy savings are consistently achieved in building roofing across regions, with the most significant effects observed in the Severe Cold region, where the uncomfortable degree hours reduction rate (φUDH) exceeds 90 %.

A simulation analysis on the internal and external application of new silica-aerogel-based (Quartzene) coatings effects on energy use in a typical building in two different climates

The study investigates the impact of improved synthetic amorphous silica, specifically developed aerogel-based insulating coatings, Quartzene (Qz), on energy efficiency in buildings across cold and warm climate zones. Simulations were conducted on various coatings and thicknesses for exterior and interior wall surfaces, as well as roofs. These were evaluated using a prototype residential building under dynamic thermal conditions. Wall coatings were analyzed, including a base plaster and a mixture with 10%Wt aerogel-based Quartzene. Additionally, three roof coating samples were tested: Qz 12.5%Wt, TiO2 3.3%Wt, and a blend of aerogel-based mixtures and TiO2. Mixtures containing Qz exhibited the lowest thermal conductivity (0.05 W/(m·K)) and high short-wave reflectance (up to 0.93 μm), impacting transmission loads, time lag, and decrement factor. Energy savings of up to 11% were observed in warm climates when implementing the coatings (unaged performance). Overall, the coated surfaces increased time lag and reduced decrement factor compared to uncoated surfaces. Aerogel-based coatings showed enhanced effectiveness in lowering transmission loads.

Modeling building energy self-sufficiency of using rooftop photovoltaics on an urban scale

The significant contribution of buildings to global energy-related CO2 emissions and climate change has led to projections of a carbon–neutral building stock by 2050. This study evaluates the potential contribution of rooftop photovoltaics to urban energy self-sufficiency by developing an enhanced CityBEM framework, our in-house urban building energy model (UBEM). This methodology enables city-scale, simultaneous simulations of building energy use and rooftop photovoltaic retrofitting with low computational time. CityBEM’s robustness and high spatiotemporal resolution facilitate transient simulations of individual buildings with diverse usage types across large urban areas, addressing common computational constraints and input data limitations in UBEM applications. The rooftop photovoltaic model in CityBEM utilizes a comprehensive approach with physics-based modeling of crystalline PVs, model validation, and a proper design of array networks to manage self-shading effects. More than 57,000 buildings with a combined footprint of 32.37 million square meters are located in the simulation test case covering downtown Montreal (Quebec, Canada) and the surrounding areas. Results indicate that rooftop PV adoption in these buildings could potentially generate 5.9 TWh of electricity annually, reduce energy demand by 27.3%, and achieve an average annual energy saving of 40%. This local generation could also reduce operational CO2 equivalent emissions by over 0.2 megatons. Overall, this study highlights the significant potential of rooftop photovoltaics for a cleaner, more energy self-sufficient urban future.

Awareness of Campus Building Users to Achieve Energy Efficiency in Facing the Implementation of Green Buildings (Case Study: Politeknik Negeri Pontianak Integrated Lecture Building)

Abstract Building users’ awareness is a very crucial factor in efforts to achieve efficient energy use in buildings, especially in the green building concept. However, research that examines the awareness of building users specifically in the campus environment is still very rarely conducted, especially in Indonesia. This research, which uses a case study at Politeknik Negeri Pontianak integrated lecture building, investigates the criteria for awareness of building users and their behavior in using energy in the building. This research tries to identify social parameters in achieving building energy efficiency using a quantitative approach. The series of statistical tools used includes ranking of average values. The results of this research show that the focus of improvement can be done by increasing awareness of several things, namely recognizing the problem of decreasing electrical resources, dividing resource use in order to reduce electricity consumption, and understanding efforts and making preparations in the face of reduced electrical energy sources.

The Doughnut Biotool: A tool to assess life-cycle biodiversity impacts from building projects

Abstract Life-cycle assessment (LCA) is widely used in certifications, regulations, and voluntary declarations covering the climate impact of buildings over their entire value chain. However, biodiversity impacts and other environmental impact categories are often disregarded. Conversely, there is a trend towards more systematic assessments of the biodiversity impacts that occur on-site as part of development projects (e.g. land transformation on the project site). However, off-site biodiversity impacts occurring throughout the rest of the value chain (e.g. from forestry activities, mining, manufacturing, heat and electricity production) are rarely considered. To bridge this gap, this article introduces the Doughnut Biotool, an open-source calculation tool to assess biodiversity impacts throughout the entire life cycle of development projects. The tool was initially developed to help implement Doughnut Economics and absolute sustainability principles in development projects. It constitutes a first step towards a better consideration of biodiversity impacts related to material- and energy use in building and construction projects. This paper presents the design of the tool and the method and data it relies on. The tool is applied to two separate case study projects, and used as a basis to discuss hotspots of biodiversity impacts within the building’s life cycle. Finally, the paper includes a discussion of the main challenges and opportunities for the future development and use of the tool, as well as for biodiversity impact assessments in the building sector as a whole.

Energy management of residential buildings using innovative passive techniques

Abstract Recently, efforts on sustainability and energy savings in buildings have increased due to the growing global awareness of the energy use of buildings and the negative impacts of carbon dioxide emissions on climate change. As a result, it is critical to propose effective strategies to reduce such energy consumption. The goal of this project is to advance the idea of net-zero energy buildings as a worldwide mitigation method for climate change. Thus, a numerical model was developed and validated to investigate the passive cooling capabilities of an innovative phase change material (PCM)-based phase cube in residential buildings. The proposed phase cube includes six various PCM-compact storage module (PCM-CSM) configurations including Rubitherm’s SP-24E plates (Horizontal), Half Circular Inline, Half Circular Staggered, Vertical plates, Inline Cylindrical and Staggered Cylindrical compact storage modules. All the proposed designs were numerically simulated, and the obtained results were compared and analysed at an inlet air temperature and velocity of 35°C, and 4m/s, respectively and each phase cube contained the same mass of Rubitherm’s SP-24E PCM, which was 30.326 kg. The results showed that the Horizontal, Half Circular Inline and Vertical phase cubes can absorb heat at a higher rate from fresh air at nearly 496.19w, 465.9w and 458.16w, respectively translating the lowest outlet air temperature of about 25°C from the cube. Integrating such phase cubes into residential buildings for indoor passive air cooling substantially decreased the building cooling loads, resulting in reduced electric energy consumption by the air conditioning system.

On the impact of hollow silica powder on the performance of aerogel glazing systems in buildings: Results from laboratory and simulation analyses

Aerogel-based glazing systems are an effective solution to reduce space heating energy use in buildings, even if overheating risks in temperate climates exist due to the high solar transparency of the systems. This study analyzes the potential of an innovative solution, based on a mixture of granular aerogel and hollow silica powder, in small concentrations, to increase the reflectance of the aerogel layer and reduce solar transmission. A set of glazing samples, different in aerogel layer and powder concentration, were produced and test in laboratory to determine the relevant solar and thermal properties. It was found that at 7.5 % powder concentration the solar and light transmittance were reduced up 0.48 and 0.45 in a 0–1 scale, respectively. Analogously, the solar and light reflectance increased up 0.49 and 0.46, respectively. The thermal resistance of the system increased up to 0.18 m2K/W, peaking at 1.88 m2K/W for the 34 mm aerogel layer sample. The thermo-physical properties were used as input to simulate an office building in different climatic conditions; it was found that the proposed technology ensured total energy savings for 4 % powder concentration and higher; cooling energy savings were up to 21 % and total energy savings up to 21 %.

Enhancing Power and Thermal Gradient of Solar Photovoltaic Panels with Torched Fly-Ash Tiles for Greener Buildings

Solar photovoltaic (PV) panels that use polycrystalline silicon cells are a promising technique for producing renewable energy, although research on the cells’ efficiency and thermal control is still ongoing. This experimental research aims to investigate a novel way to improve power output and thermal performance by combining solar PV panels with burned fly-ash tiles. Made from burning industrial waste, torched fly ash has special qualities that make it useful for architectural applications. These qualities include better thermal insulation, strengthened structural integrity, and high energy efficiency. Our test setup shows that when solar PV panels are combined with torched fly-ash tiles, power generation rises by 7% and surface temperature decreases by 3% when compared to standard panels. The enhanced PV efficiency is ascribed to the outstanding thermal insulation properties of fly ash tiles and their capacity to control panel temperature. To ensure longevity and safety in building applications, the tiles employed in this study had a water absorption rate of 5.37%, flexural strength of 2.95 N/mm2, and slip resistance at 38 km/h. Furthermore, we find improved structural resilience and lower cooling costs when up to 30% of the sand in floor tiles is replaced with torched fly ash, which makes this method especially appropriate for sustainable buildings. Key performance indicators that show how effective these tiles are in maximizing energy use in buildings include thermal emissivity (0.874), solar reflectance (0.8), and solar absorption (0.256). While supporting more ecofriendly building techniques, this study highlights the advantages of utilizing burned fly ash in solar PV systems: enhanced power generation and thermal comfort. The main results open a greater potential for fly ash use in different building materials. The use of torched fly ash in building materials enhances thermal insulation and structural integrity while lowering cooling costs, making it an ideal choice for eco-friendly construction and highlighting the potential for further research into environmentally responsible, energy-efficient solutions.

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.

Circuit categorization approach of office building energy consumption based on data features for energy-saving diagnosis

Energy-saving in office buildings is crucial. Research on data-driven diagnostics of building energy consumption is pivotal. The emergence and development of itemized electricity platforms have expanded the scope of diagnosis from total building energy consumption to individual electrical equipment. However current studies rarely address the characteristics of sub-circuits. This paper highlights the importance of recognizing the features of electrical equipment sub-circuits based on historical energy consumption data by examining various aspects of energy use in office buildings, including electrical consumption disaggregation, equipment operational strategies, sub-item consumption proportion, and sub-circuit composition. A circuit categorization approach in office buildings by features analysis of historical data is innovatively proposed. Operational conditions, time series stationarity, and correlations with factors affecting energy consumption are considered in historical data feature analysis. The method categorizes the electric circuits into 5 categories with typical features based on the ADF-KPSS co-test and Spearman correlation analysis. The rationality and validity of the circuit categorization method were verified with actual building data in case studies. On this basis, an interpretable energy consumption anomaly diagnosis method based on the categorization results is proposed. The circuit categorization approach explores how to further investigate the typical features of sub-circuits, and provides new directions for subsequent building operation and maintenance(O&M) research, including energy consumption diagnostics. Additionally, it offers decision support for building managers for O&M evaluation from an interpretable perspective.

A Review of Research on Building Energy Consumption Prediction Models Based on Artificial Neural Networks

Building energy consumption prediction models are powerful tools for optimizing energy management. Among various methods, artificial neural networks (ANNs) have become increasingly popular. This paper reviews studies since 2015 on using ANNs to predict building energy use and demand, focusing on the characteristics of different ANN structures and their applications across building phases—design, operation, and retrofitting. It also provides guidance on selecting the most appropriate ANN structures for each phase. Finally, this paper explores future developments in ANN-based predictions, including improving data processing techniques for greater accuracy, refining parameterization to better capture building features, optimizing algorithms for faster computation, and integrating ANNs with other machine learning methods, such as ensemble learning and hybrid models, to enhance predictive performance.

Integrating Occupant Behaviour into Urban-Building Energy Modelling: A Review of Current Practices and Challenges

Urban-Building Energy Modelling (UBEM) tools play a crucial role in analysing and optimizing energy use within cities. Among the available approaches, the bottom-up physics-based one is the most versatile for urban development and management applications. However, their accuracy is often limited by the inability to capture the dynamic impact of occupants’ presence and actions (i.e., Occupant Behaviour) on building energy use patterns. While recent research has explored advanced Occupant Behaviour (OB) modelling techniques that incorporate stochasticity and contextual influences, current UBEM practices primarily rely on static occupant profiles, due to limitations in the software itself. This paper addresses this topic by conducting a thorough literature review to examine existing OB modelling techniques, data sources, key features and detailed information that could enhance UBEM simulations. Furthermore, the flexibility of available UBEM tools for integrating advanced OB models will be assessed, along with the identification of areas for improvement. The findings of this review are intended to guide researchers and tool developers towards creating more robust and occupant-centric urban energy simulations.

Impact of newer climate data for technical analysis of residential building energy use in the United States

In this study, we describe an analysis indicating the relative importance of newer hourly weather files for simulation analysis of the energy use of residential buildings in North America.Results show that older TMY3 (ending 2005) data compared with newer TMY2021 (2007–2021) data are less appropriate for the best prediction of low-energy buildings. The balance of heating and cooling is significantly altered in the more recent TMY files with potential impacts on best performing energy efficiency measures. With 64 high profile North American locations evaluated, we found heating to be approximately 11% lower with the newer data, while cooling was increased by a similar amount percentage wise. However, the dominance of space heating in North America made decreases to heating considerably larger in an absolute sense so that overall space conditioning energy fell.Small changes in annual temperature can have large impacts. For all electric homes with air source heat pumps, we found that a change of 1 °C to outdoor temperature can produce about a 19 % change in space conditioning energy. The newer data also shows increased average solar electric output from simulated rooftop photovoltaic systems, likely due to more sophisticated solar irradiance data. Water heating was about 3 % lower given rising groundwater temperatures and inlet water temperatures to water heating systems. Results also showed that the newer data tended to increase savings from comprehensive building efficiency efforts in all electric buildings. This largely takes place because higher temperatures in winter allow air source heat pumps to meet heating needs more efficiently. Generally, the direction of these influences ease pathways to reach zero energy buildings in North America.