Building Performance Analysis Research Articles

Feasibility and performance analysis of a net-zero energy residential building in tropical climates: A case of Congo-Brazzaville

Current building techniques in developing countries have become extremely critical. The net-zero energy concept has emerged as an essential strategy for minimising energy consumption and greenhouse gas emissions. However, its implementation, particularly in developing countries, remains a critical challenge. The purpose of this work is to assess the feasibility of achieving a net-zero energy building by combining energy-efficient design practices and renewable energy systems under the climatic conditions of the Republic of Congo. To achieve the stipulated objectives, DesignBuilder software was utilised for building modelling, energy load assessment and multiobjective optimisation of building energy efficiency measures, and the multicriteria energy optimisation was carried out using the HOMER tool. The optimisation results showed that the implementation of energy efficiency measures resulted in 39.15% energy savings, 43.45% thermal load savings and 55.68% discomfort hours reduction. Furthermore, the renewable energy system can provide 100% of the building’s energy load and a total savings of 3341.84 kgCO2eq/year. The system’s levelised cost of energy was estimated at 0.256 $/kWh, with a net present cost of $20,231, while the overall life cycle cost was calculated at 188.91 $/m2.

Data-driven approach enabling post-operation evaluation of air conditioning performance regarding thermal conditions attained indoors

Data-driven approaches become more and more attractive in building performance analysis including performance assessment of heat, ventilation, and air conditioning (HVAC) systems. Their popularity is associated with the increasing availability of large amounts of high-quality building-related measurement data. The study is focused on a data-driven approach that enables post-operation evaluation of AC performance based on indoor air monitoring. It allows for the identification of the classifying solution which recognizes the monitoring data that is representative of thermal conditions during AC operation. The major novelty consists in proposing the classification performance criterion, which is F1w-Score (the weighted F1-Score), and the procedure of weights selection. The procedure utilizes Hellinger distance (H). It is applied to measure the similarity between the binary empirical probability distribution of T and RH (EPDtr), associated with AC operation, and the prediction of this distribution (EPDes), made by the classifier. The approach was tested on three years of data from continuous indoor air monitoring in the IT engineers' room equipped with an AC unit. The best weights of F1w-Score were wPPV = 0.85 and wTPR = 0.15. The best classifying solution was identified using F1w-Score. It involved: Logistic Regression as the classifier and feature vector being Fourier spectrum of 16 min long fragments of RH and T time series. The performance of the best classifying solution was high as indicated by F1w-Score = 0.89 ± 0.05. The corresponding H(EPDtr, EPDes) = 0.17 ± 0.04 pointed at the capability to identify the measurement data that characterizes indoor conditions during AC operation well. This work contributes to the development of data-driven approaches serving diagnostics of HVAC systems. It draws attention to the problem of defining the classification performance criterion which takes into account the ultimate, domain-specific use of the classification results.

Assessment and Optimization of Green Buildings in Inpatient Buildings Using EDGE Building Rating (Case Study: Graha Amarilis of Karsa Husada Batu Academic Hospital)

The Graha Amarilis inpatient building has high Energy, Water, and material consumption because it operates 24 hours daily. The building’s performance must be evaluated based on green building standards to ensure the sustainability of the building and the environment surrounding it. This study aims to determine the performance of green buildings and optimize the Graha Amarilis inpatient building. A combined strategy of case studies with simulations is used as research methodology. Field surveys and measurements were carried out on research cases and digitally modeled using Autodesk REVIT. Then, building performance analysis calculations are carried out using the EDGE Building application simulation method, which presents data on green rating of Energy, Water, and Material saving in buildings. The results of this study were obtained on energy and water items that had not reached green building rating in EDGE Building by 20%. So, optimizing energy items at points EEM 18, EEM 07, and EEM 33 and Water at points WEM 01, WEM 02, WEM 14, and WEM 15 is necessary. Finally, after several simulations on that item, Energy saving consumption becomes 20.07 %, Water 34.69%, and Material 23.00%. With these adjustments, the Graha Amarilis inpatient Building can reach the green building standards and become more sustainable and environmentally friendly

Assessing the efficacy of green building design strategies in minimizing energy consumption in commercial buildings of Mumbai: A building performance analysis

This research paper investigates the effectiveness of green building design strategies in minimizing energy consumption in commercial buildings in the bustling metropolis of Mumbai, India. With a rapidly urbanizing and densely populated environment, Mumbai presents a unique setting for assessing green building practices. The research objectives focus on understanding the impact of various green building design features, such as green roofs, solar panels, and floor area optimization, on energy efficiency in commercial structures. The research employs a robust methodology, incorporating regression analysis to analyze data obtained from the Mumbai Municipal Corporation's building energy consumption records. Through this analysis, the study investigates the relationships between green building design parameters, building characteristics, and energy consumption patterns. Key findings indicate that specific green building features, including intensive and biodiverse green roofs and solar panel installations, significantly reduce energy consumption. Additionally, older buildings tend to exhibit lower energy consumption, emphasizing the need for retrofitting existing structures with energy-efficient solutions. The study also underscores the role of occupant density in influencing energy efficiency, with higher occupant rates leading to more efficient space utilization and energy use. The implications of this research are far-reaching, offering insights into tailoring green building strategies to the specific challenges posed by densely populated urban areas. These findings hold significance for sustainable urban development, policy formulation, architectural design, and the global effort to mitigate climate change.

Fire Performance Analysis of Building Integrated Solar System Roofs

Fire Performance Analysis of Building Integrated Solar System Roofs

Development of a fast moisture prediction model based on the moisture buffer value theory

Moisture transfer in buildings significantly affects indoor thermal comfort, energy consumption, and durability of construction materials. It is crucial to predict the moisture storage and transfer in building simulations accurately. However, moisture transfer was neglected in many simulation tools for building performance analysis. In those calculation tools, including a moisture model, moisture transfer has been either estimated by simple approximations or calculated by complex combined heat, air, and moisture transfer (HAMT) models that require orders of magnitude more computing time than simple calculations. In this study, a new moisture prediction model with fast solution time and reasonable accuracy was developed based on the moisture buffer value (MBV) theory. The moisture buffer value was initially designed to quantify the moisture buffering ability of porous building materials. Very little research has been conducted to use MBV for calculating building energy performance directly. This paper first studied the MBVs under different boundary conditions (i.e., square wave and harmonic wave variation of vapour content), and then a time-average MBV was proposed. Experiments were carried out to get the parameters for the new FMBM model. Finally, simulation tests were conducted to compare the FMBM model with other moisture prediction models (e.g. EC, EMPD and HAMT models). The results indicate that the FMBM can provide a fast and reasonably accurate solution for indoor moisture prediction.

Identification of influential factors for combined energy consumption and indoor environmental quality in residential buildings

The development of an overall building performance simulation model requires a multitude of input parameters which can be a challenging and resource-heavy task for building modellers. Furthermore, some parameters have little impact on a building’s overall performance and contribute little towards model prediction accuracy. Feature selection has been employed to identify the most influential input parameters to reduce complexity and computational time. However, previous studies focused mainly on identifying parameters that impact energy consumption in residential buildings, neglecting the important relationship between energy consumption and indoor environmental quality (IEQ). Therefore, this study proposes a novel simulation framework that integrates occupancy-based building archetypes, parametric simulation, and machine learning techniques to develop an overall building performance prediction model. Using this framework, the study generates a synthetic dataset of 40,000 simulations and performed embedded feature selection using two machine learning algorithms, Random Forest (RF) and Gradient Boosting Technique (GBT), to identify parameters that impact heating energy consumption, thermal discomfort hours, and CO2 concentration simultaneously. The results demonstrate that the ranking for importance and the number of required parameters vary depending on the target variable. Also, the set of parameters for combined analysis differs from individual target variable analysis. The GBT algorithm with embedded feature selection provides the most accurate prediction results with lower root mean square error (RMSE) and absolute error (AE) for individual and combined analyses. This study provides valuable insights for accurate parameter selection and analysis of overall building performance.

Numerical safety and performance analysis of a multipurpose building’s structure: case study

This report presents a case study of a multi-use building situated in Brasília-DF. The building's structural model is made up of elements of a spatial portico and four-knot plate, measuring 25.75 by 25.75 m with a height of 11.65 m. The structure is supported by rotating pillars at the base. The ground floor has a ceiling height of 3.25 m, while the other floors have a ceiling height of 2.80 m. The closure is made of bored brick masonry. In this article, numerical analyses were conducted using the SAP2000 structural software to verify the structure's displacement, deformation, and stress. The study followed the ultimate and in-use limit state criteria for the Brazilian standard of reinforced concrete (NBR 6118). The report also presents the building's structural pathologies, detected through visual inspection. The main structural elements and their respective pathologies are described, and numerical tests are shown. Due to the presence of various cracks and other pathologies in the building, an elastic instability analysis was conducted using the Rankine-Marchant analysis model to verify the collapse load factor. After approximately 17 years of use, the building is revisited to assess the durability and performance of the recovered structural components and procedures.

An integrated pipeline for building performance analysis: Daylighting, energy, natural ventilation, and airborne contaminant dispersion

An integrated pipeline for building performance analysis: Daylighting, energy, natural ventilation, and airborne contaminant dispersion

Daylight performance analysis of a residential building in a tropical climate

The present study investigates the effects of visible light transmittance in glazing and window-to-wall ratios on the ground floor daylighting performance in a two-storey residential building in a warm-humid climate. The metrics used to optimize daylighting performance with minimal glare are useful daylight illuminance, annual sunlight exposure, and spatial daylight autonomy. The daylighting performance of a residential building is assessed by empirical method and Design-Builder simulation, focusing on overcast sky situations. The useful daylight illuminance is the primary metric for analysing the amount of daylight throughout the year. Annual sunlight exposure and spatial daylight autonomy complement useful daylight illuminance in evaluating the daylighting performance. A window-to-wall ratio of 16%, a visible light transmittance of 0.62, and a glare of 0.52 can meet the daylighting requirements and standards. A design change in the window position helps to obtain annual sunlight exposure within 10% while maintaining high daylighting performance. When installed in the upper position of the wall with a higher sill and lintel height, glazing with a window-to-wall ratio of 16% and a visible light transmittance of 0.62 functions well without creating glare. The significant findings benefit all stakeholders in improving daylighting strategies in tropical climates and satisfying building standards.

The feeling of comfort in residential settings I: a qualitative model

Building science commonly studies comfort—a subjective concept—through quantitative methods. These methods are often not the most appropriate to study subjective concepts because they struggle to consider non-quantifiable factors that are sometimes relevant in the determination of people’s comfort. Complementing the already used quantitative methods with qualitative ones can help illuminate some of the areas where the former struggle, but this is an uncommon practice in building science. This paper presents the results of a qualitative study that aimed at understanding comfort without the constraints of it being quantifiable. It introduces the feeling of comfort model, which summarises what was found. This model suggests that most building science comfort models ignore more than two out of the three factors that determine people’s comfort. Additionally, it shows that it is potentially possible to make sense of the psychology and subjectivity of comfort in an organised and structured manner. This example of how qualitative methods can be a powerful addition to building science’s comfort research. Practice relevance This paper reports a project that sought to understand an occupant-centred meaning of ‘comfort’ (constrained to what has conventionally been described as thermal, visual and acoustic domains). A qualitative model of the feeling of comfort was developed from the empirical data collated in the study. This model demonstrates that it was possible to make sense of the subjective nature of comfort in a coherent and ordered manner. This study argues that the qualitative and subjective nature of comfort should be embraced within building design and building science practice. Although this model was not meant to predict comfort quantitatively, it can serve as a theoretical basis for informing policymaking, building performance analysis and comfort research. For instance, it can help evaluate the assumptions behind building performance simulation (e.g. questioning whether people will truly open windows in certain scenarios).

Comprehensive performance analysis of a rural building integrated PV/T wall in hot summer and cold winter region

The building-integrated PV/T technologies have garnered significant attention due to their considerable potential in low-carbon and zero-energy buildings. To gain deeper insights into the practical application of this technology, a comprehensive study was conducted on a rural building integrated with PV/T walls for demonstration purposes. A novel simulation approach, coupling the numerical models of the PV/T walls with the building energy software, was proposed and experimentally validated. Based on the validated simulation approach, this study investigated the comprehensive performance of the demo building and explored the optimal capacity of the energy storage system (ESS). The main findings are as follows: (1) The proposed simulation approach could accurately predict the energy behaviors of the multi-room building integrated PV/T wall. (2) The demo building with the optimized EES yielded 1355.32 kWh of clean electricity and 455.3 kWh of heat gain for water heating, while reducing cooling and heating loads by 5% and 12%, respectively. (3) The optimal azimuth for the building in the studied region was 45°, and the total energy benefits of the building decreased by 28.5% with an increase in dust density from 0 g/m2 to 10 g/m2.

Application of an Architect-Friendly Digital Design Approach to the Wind Environment of Campus Dormitory Buildings

Good natural ventilation can improve the comfort of campus dormitories and effectively avoid pollution caused by particle accumulation. Parametric design can effectively address the feedback and connection between building performance analysis and design. This study employs an architect-friendly digital design method based on the Rhino/Grasshopper parametric platform. It takes campus dormitories in the cold region as a case, using parameterized digital tools, such as the Butterfly plugin to simulate wind performance under three influencing factors: building layout, opening position, and building façade (shape and spoiler). Finally, the optimal design that can simultaneously meet the local winter and summer wind environment requirements is selected and validated. In addition, the reasonable design of external balconies and bathrooms in a dormitory can form buffer spaces to achieve effective wind shelter and insulation effects in cold regions. This article describes how to use digital tools to quickly and easily optimize the design of building forms based on wind simulations to promote campus sustainability.

Rethinking the role of efficiency for the decarbonization of buildings is essential

Antoine Levesque is a postdoctoral researcher at the Potsdam Institute for Climate Impact research (PIK), where he started working in 2014. His research focuses on the decarbonization of buildings, efficiency policies, and energy demand projections. Prior to the PIK, he worked at the International Institute for Applied Systems Analysis (IIASA) in Austria. He earned a PhD in economics at the Technische Universität Berlin (2021). Sebastian Osorio is a postdoc researcher at the PIK in the “Climate and Energy Policy” group. His work concentrates on applying energy systems models to evaluate low-carbon policies, with a clear focus on the EU power sector, the Emission Trading System, and their interaction with the decarbonization of buildings. Sebastian holds a PhD in information systems from the University of Lausanne in Switzerland. Sebastian Herkel is head of the “Energy Efficient Buildings” department at Fraunhofer Institute for Solar Energy Systems in Freiburg, Germany. He graduated from University of Karlsruhe with the degree of diplom-ingenieur in mechanical engineering. Sebastian Herkel is a researcher working in applied research in building energy performance and renewable energy systems. His focus is on integral energy concepts for buildings and neighborhoods, scientific analysis of building performance, and technologies for integration of renewable energy in buildings. Michael Pahle is head of the working group “Climate and Energy Policy” at the PIK. He holds a PhD in economics from TU Berlin. His research focuses on carbon pricing and more recently on climate policies for the buildings sector. He is co-principle investigator for Germany’s largest household panel on energy efficiency. Based on his research, Michael advises the German government and EU institutions. He is a member of the Global Climate Policy Partnership, a US-based global network of research institutions that helps major economies and businesses achieve ambitious climate goals. Antoine Levesque is a postdoctoral researcher at the Potsdam Institute for Climate Impact research (PIK), where he started working in 2014. His research focuses on the decarbonization of buildings, efficiency policies, and energy demand projections. Prior to the PIK, he worked at the International Institute for Applied Systems Analysis (IIASA) in Austria. He earned a PhD in economics at the Technische Universität Berlin (2021). Sebastian Osorio is a postdoc researcher at the PIK in the “Climate and Energy Policy” group. His work concentrates on applying energy systems models to evaluate low-carbon policies, with a clear focus on the EU power sector, the Emission Trading System, and their interaction with the decarbonization of buildings. Sebastian holds a PhD in information systems from the University of Lausanne in Switzerland. Sebastian Herkel is head of the “Energy Efficient Buildings” department at Fraunhofer Institute for Solar Energy Systems in Freiburg, Germany. He graduated from University of Karlsruhe with the degree of diplom-ingenieur in mechanical engineering. Sebastian Herkel is a researcher working in applied research in building energy performance and renewable energy systems. His focus is on integral energy concepts for buildings and neighborhoods, scientific analysis of building performance, and technologies for integration of renewable energy in buildings. Michael Pahle is head of the working group “Climate and Energy Policy” at the PIK. He holds a PhD in economics from TU Berlin. His research focuses on carbon pricing and more recently on climate policies for the buildings sector. He is co-principle investigator for Germany’s largest household panel on energy efficiency. Based on his research, Michael advises the German government and EU institutions. He is a member of the Global Climate Policy Partnership, a US-based global network of research institutions that helps major economies and businesses achieve ambitious climate goals.

Performance Analysis of a Zero-Energy Building Using Photovoltaics and Hydrogen Storage

The exploitation of renewable energy sources in the building sector is a challenging aspect of achieving sustainability. The incorporation of a proper storage unit is a vital issue for managing properly renewable electricity production and so to avoid the use of grid electricity. The present investigation examines a zero-energy residential building that uses photovoltaics for covering all its energy needs (heating, cooling, domestic hot water, and appliances-lighting needs). The building uses a reversible heat pump and an electrical heater, so there is not any need for fuel. The novel aspect of the present analysis lies in the utilization of hydrogen as the storage technology in a power-to-hydrogen-to-power design. The residual electricity production from the photovoltaics feeds an electrolyzer for hydrogen production which is stored in the proper tank under high pressure. When there is a need for electricity, and the photovoltaics are not enough, the hydrogen is used in a fuel cell for producing the needed electricity. The present work examines a building of 400 m2 floor area in Athens with total yearly electrical demand of 23,656 kWh. It was found that the use of 203 m2 of photovoltaics with a hydrogen storage capacity of 34 m3 can make the building autonomous for the year period.

Various Methodologies for BIM Implementation on Green Building Construction Projects: State of Art Review

Green building has attracted overall consideration because of the unfavourable effect of development on the climate. The best decisions connected with the eco-friendly design of a green structure can be done during the phases of planning and designing. As of Building Information Modelling (BIM) considers multidisciplinary data to be layered inside a single model, it outlines the freedom for adopting sustainability measures from the beginning of the design phase. In this review paper, various Methodologies for BIM Implementation on Green Building Construction Projects through literature review have been explored. The explored methodologies are Case study-based, Survey based, Green Building Certificate based, Novelty Explored, Literature Review based, and Numerical calculation based. Out of these, three methodologies were reviewed, which are Case study-based, Survey based, and Green Building Certificate- based. The primary objective of the case study-based paper is to determine the ultimate goals of case studies in BIM Implementation on Green Building Construction Projects and integration of BIM applications with International Journal of Scientific Research in Engineering and Management (IJSREM) Volume: 07 Issue: 03 | March - 2023 Impact Factor: 7.185 ISSN: 2582-3930 © 2023, IJSREM | www.ijsrem.com DOI: 10.55041/IJSREM18140 | Page 2 building performance analysis tools for sustainability analysis. Furthermore, in the survey-based paper, the information such as stakeholders who participated in the survey, main purposes of the survey, major survey questions, and methods used for the analysis carried out. Ultimately, the functions of BIM in obtaining a green building certification. Keywords: Building information modelling (BIM), Construction, Green Building, Methodologies

Competency-Based Education & Training (CBET), A Practical Strategy For Sustainable Self-Employment of Building Technologist

The study determined competency based educational training as a practical strategy for sustainable self-development of building engineering technologists in Enugu state. Two research questions were answered and two hypotheses were tested at 0.05 level of significance. Descriptive survey design was adopted for the study. The population for the study was 115 practicing building engineering technologists. Instrument for data collection was a structured questionnaire. Two experts validated the instrument for data collection. The internal consistency of the instrument items was determined by the use of Cronbach alpha reliability method and 0.84 reliability coefficient was obtained. The data generated were analyzed using mean to answer research questions while t-test was used to test the hypotheses. Findings revealed that competency based educational training have the potentials to foster self-employment of building engineering technologists. It fosters self-employment of building engineering graduates by equipping them with industrial based competencies such as in-depth knowledge of construction practice, building drawing and interpretation skills, building performance analysis skills and determination of building materials behaviours among others. It was recommended that effective partnership between universities and other higher institutions and relevant industries should be encouraged, as this would provide an environment for students to interact with experts such that, relevant contents that meets labour needs would be transmitted to participating students.

Thermal Performance and Energy Analysis of Phase Change Material-Integrated Building with the Auxiliary Heating System in Different Climate Regions

Phase change material (PCM) embedded in the building contributes to enhancing indoor thermal comfort and reducing the heating load in winter by absorbing/releasing solar energy. The application effects of PCM incorporated into a single-space passive solar building with the auxiliary heating system (AHS) were investigated numerically in the six representative regions of western China. In the calculation and analysis by the enthalpy model, it was found that the effectiveness of PCM greatly depended on local climate conditions, thickness, and location of PCM. The results indicated that it was necessary to adopt the AHS for winter heating of the passive solar building. On this basis of using AHS, it showed a favorable thermal energy storage effect after the application of PCM resulting in the minimum indoor air temperature being maintained above 15°C within the comfort range stably. The integrated discomfort degree in each region was decreased by more than 96% compared to the original building, and the average temperature in Lhasa was 2°C higher than that in Urumqi. Furthermore, the highest rate of energy saving and carbon dioxide emission saving in the six regions was 19.7% and 17.4% in Lhasa, and the lowest were 4.3% and 4.2% in Urumqi, while other regions were relatively close, respectively. As a result, it can be stated that for a passive solar building using the AHS during the heating period, PCM was the most suitable for application in Lhasa, unsuitable in Urumqi, and generally suitable in other regions.

The Application of Building Information Model in the Structural Design of Intelligent Buildings

Due to the irreversibility of building structures in the construction process, unreasonable construction methods are likely to cause economic losses and great negative social impacts. Therefore, the study of scientific and efficient intelligent construction methods for building structures has become a research hotspot in recent years, mainly including intelligent building design, construction control and safety analysis methods. Therefore, it is very meaningful and necessary to carry out research on intelligent construction technology based on BIM technology. By applying BIM technology to the process of intelligent construction of structures, this paper intends to achieve six major objectives such as building performance analysis and parametric design of structures, and independently develop a BIM construction process management platform, which combines construction progress, quality, safety, cost and green construction through BIM technology for information management of projects, making it more convenient for owners and construction unit staff to software application, thus greatly improving the communication and use efficiency, and providing important guidance for the construction process control and progress control of the project.

An improved adaptive learning path recommendation model driven by real-time learning analytics

The advancements in the education sector made e-learning more popular in recent years. The velocity of learning content creation and its availability is also growing exponentially day after day. It is challenging for a learner to find a learning path for a course with a vast content repository. So, recommending a learning path helps the learners streamline the learning materials systematically and achieve their goals. This article proposes a learning path recommendation approach focused on knowledge building and learning performance analysis. The model considers both static and dynamic learner parameters for learning path generation. The difficulty level of the learning resources is tuned based on the real-time performance analysis of the students. The learning resources are recommended based on learning preferences and the ability of a learner to learn the specific learning resource. The model also predicts the learning time and the expected score for each learner. Root Mean Square Deviation and Coefficient of Determination (R-Squared error) measures are used to find the correctness of the prediction. The model is also checked for its adaptivity to the learners’ changing behavior and diversity of the LOs recommended for different learners. Ninety-six undergraduate learners participated in the study. The experimentations are conducted with 530 LOs from selected courses. The comparison results with three existing models show a better performance from the proposed approach with an average accuracy rise of 30% in learning path prediction based on the expected duration of learning 27.8% in expected score prediction with the second-best performing model. It is observed that the real-time learning analytics using the implicit learner log data benefits the recommendation process. LO rating strongly indicated the enhancement of learner satisfaction and experience with a rise of 25.5% when comparing the rating share with the second-best model.