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Building performance analysis
Explores and brings together the existent body of knowledge on building performance analysis Building performance is an important yet surprisingly complex concept. This book presents a comprehensive and systematic overview of the subject. It provides a working definition of building performance, and an in-depth discussion of the role building performance plays throughout the building life cycle. The book also explores the perspectives of various stakeholders, the functions of buildings, performance requirements, performance quantification (both predicted and measured), criteria for success, and the challenges of using performance analysis in practice. Building Performance Analysis starts by introducing the subject of building performance: its key terms, definitions, history, and challenges. It then develops a theoretical foundation for the subject, explores the complexity of performance assessment, and the way that performance analysis impacts on actual buildings. In doing so, it attempts to answer the following questions: What is building performance' How can building performance be measured and analyzed' How does the analysis of building performance guide the improvement of buildings' And what can the building domain learn from the way performance is handled in other disciplines' -Assembles the current body of knowledge on building performance analysis in one unique resource -Offers deep insights into the complexity of using building performance analysis throughout the entire building life cycle, including design, operation and management -Contributes an emergent theory of building performance and its analysis Building Performance Analysis will appeal to the building science community, both from industry and academia. It specifically targets advanced students in architectural engineering, building services design, building performance simulation and similar fields who hold an interest in ensuring that buildings meet the needs of their stakeholders.
Book
Machine-Learning-Enhanced Building Performance-Guided Form Optimization of High-Rise Office Buildings in China’s Hot Summer and Warm Winter Zone—A Case Study of Guangzhou
Given their dominant role in energy expenditure within China’s Hot Summer and Warm Winter (HSWW) zone, high-fidelity performance prediction and multi-objective optimization framework during the early design phase are critical for achieving sustainable energy efficiency. This study presents an innovative approach integrating machine learning (ML) algorithms and multi-objective genetic optimization to predict and optimize the performance of high-rise office buildings in China’s HSWW zone. By integrating Rhino/Grasshopper parametric modeling, Ladybug Tools performance simulation, and Python programming, this study developed a parametric high-rise office building model and validated five advanced and mature machine learning algorithms for predicting energy use intensity (EUI) and useful daylight illuminance (UDI) based on architectural form parameters under HSWW climatic conditions. The results demonstrate that the CatBoost algorithm outperforms other models with an R2 of 0.94 and CVRMSE of 1.57%. The Pareto optimal solutions identify substantial shading dimensions, southeast orientations, high aspect ratios, appropriate spatial depths, and reduced window areas as critical determinants for optimizing EUI and UDI in high-rise office buildings of the HSWW zone. This research fills a gap in the existing literature by systematically investigating the application of ML algorithms to predict the complex relationships between architectural form parameters and performance metrics in high-rise building design. The proposed data-driven optimization framework provides architects and engineers with a scientific decision-making tool for early-stage design, offering methodological guidance for sustainable building design in similar climatic regions.
Journal Article
Multi-Objective Optimization of Building Environmental Performance: An Integrated Parametric Design Method Based on Machine Learning Approaches
Reducing energy consumption while providing a high-quality environment for building occupants has become an important target worthy of consideration in the pre-design stage. A reasonable design can achieve both better performance and energy conservation. Parametric design tools show potential to integrate performance simulation and control elements into the early design stage. The large number of design scheme iterations, however, increases the computational load and simulation time, hampering the search for optimized solutions. This paper proposes an integration of parametric design and optimization methods with performance simulation, machine learning, and algorithmic generation. Architectural schemes were modeled parametrically, and numerous iterations were generated systematically and imported into neural networks. Generative Adversarial Networks (GANs) were used to predict environmental performance based on the simulation results. Then, multi-object optimization can be achieved through the fast evolution of the genetic algorithm binding with the database. The test case used in this paper demonstrates that this approach can solve the optimization problem with less time and computational cost, and it provides architects with a fast and easily implemented tool to optimize design strategies based on specific environmental objectives.
Journal Article
Evaluating the Environmental Footprint: BPE Framework for Sustainable and Energy-Efficient Residential Buildings in India
In the context of enhancing building performance assessment, this study introduces the BPE (Building Performance Evaluation) framework and explores its application through a residential complex in India. An expert evaluation of the questionnaire is carried out to investigate the main element and obstacles to the execution of BPE. The framework, designed to scrutinize five parameters, initially assesses design aspects, including building form, orientation, and aesthetics. Findings reveal that the building design lacks efficient circulation, storage facilities, and satisfactory spatial allocation. Building energy monitoring, essential for comprehensive analysis, faces limitations due to insufficient data availability, emphasizing the need for thorough planning. Thermal comfort analysis, based on temperature and humidity measurements, unveils significant fluctuations beyond comfort thresholds. Expert surveys and occupant feedback further expose reduced utilization of natural ventilation, high air conditioner adoption rates, and adaptive behaviours. The framework's insights prompt opportunities for improvement, yet validation requires broader application across diverse buildings. The study's academic survey emphasizes the importance of integrating BPE in industries with government policies. Field observations highlight challenges in space utilization, material selection, and occupant engagement. This study's findings underscore the BPE-RBPI framework's potential to refine performance assessment, sustainable and energy efficient to foster industry confidence, and drive holistic improvements in India's building sector.
Journal Article
Quantifying the Enhanced Performance of Multifamily Residential Passive House over Conventional Buildings in Terms of Energy Use
In response to escalating energy demands and global warming concerns, the Passive House Standard has emerged as a solution in residential construction, aiming to drastically reduce energy consumption and operational costs primarily through high-performance building envelopes. While a considerable volume of the literature has focused on the Passivhaus Institute (PHI) standards, predominantly in European contexts, there is a gap in research on the Passive House Institute US (Phius) standards, particularly in North American climates. This study conducts a quantitative comparative analysis of two adjacent multifamily residential buildings in Central Pennsylvania, Climate Zone 5A—one built using conventional construction methods and the other following Passive House (PHIUS+ 2015) certification standards—to validate the energy efficiency improvements attributed to Passive House designs. A comparative analysis of the whole building energy use over two years reveals that the Passive House building consumes approximately 50% less energy than its conventional counterpart in terms of whole building energy use and the national median recommended benchmark metric defined by the Energy Star Portfolio Manager. These findings emphasize the potential for significant energy savings and greenhouse gas reductions in residential buildings, highlighting the necessity for policymakers and governments to incentivize the adoption of Passive House standards to achieve environmental sustainability and reduce energy costs for society.
Journal Article
The world's greenest buildings : promise versus performance in sustainable design
\"The World's Greenest Buildings provides the first way to compare building performance, using cost and energy use data that has been verified by independent third parties and to understand how building performance can be upgraded. The book provides: an overview of the rating systems and shows \"best in class\" building performance in North America, Europe, the Middle East, India, China, Australia and the Asia-Pacific region practical examples of best practices for greening both new and existing buildings, useful for architects and engineers, contractors, building owners and managers, facility professionals, developers, lenders and investors, brokers and appraisers, and everyone charged with managing commercial and institutional buildings a response to the intense need for a practical reference for design professionals, building owners, developers and facility managers on how green buildings actually perform at the highest level, one that takes them step-by-step through many different design solutions. interviews with architects, engineers, building owners and developers and industry experts, to provide added insight into the greening process a complete guide to world-class green building performance primarily for new buildings, including corporate, commercial, educational, governmental and other large building types a welath of exemplary case studies of successful green building projects using actual performance data from which to learn a \"recipe,\" based on others' experiences, for delivering successful green building projects in the various countries profiled\"-- Provided by publisher.
Book
Urban-Metabolic Farming Modules on Rooftops for Eco-Resilient Farmscape
The scarcity of land resources and food security challenges have prompted more effective uses of the rooftop as well as façade spaces in the urban city of Singapore. Urban rooftop spaces are used for mechanical and electrical (M&E) amenities such as air-conditioning cooling units and water tanks, so the spacious span of the roof area on HDB flats in Singapore is not available. Urban-metabolic farming modules (UmFm) built on 1.5 to 2 m terrace-step terrains have been modelled using BIM Revit to mimic such constraints in rooftop spaces. CFD simulation was conducted for the structure with consideration of the prevailing wind directions at different months of the year. The airflow with the inclusion of mesh netting and varying tiltings of the polycarbonate side façade was simulated to understand their impact on airflow in the growth envelope of the UmFm units under different prevailing wind directions. The amount of solar irradiance received by the crops at different heights in the UmFm due to the sun’s path, and shading of crops grown on the A-frame, was studied using Climate Studio. A comparative verification was done with a scaffold modular unit mounted with temperature, humidity, airflow, and Photosynthesis Photon Flux Density (PPFD) sensors. The digital model of the UmFm unit enables a prior assessment of site feasibility before actual physical implementation on an existing rooftop. It also facilitates plug and play for the UmFm unit to generate an eco-resilient farmscape for an urban city.
Journal Article