Optimizing Energy Performance Of Building Renovation Using Traditional And Machine Learning Approaches
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Optimizing Energy Performance Of Building Renovation Using Traditional And Machine Learning Approaches
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Author : Seyed Amirhosain Sharif Arani
language : en
Publisher:
Release Date : 2020
Optimizing Energy Performance Of Building Renovation Using Traditional And Machine Learning Approaches written by Seyed Amirhosain Sharif Arani and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
International Energy Agency (IEA) studies show that buildings are responsible for more than 30% of the total energy consumption and an equally large amount of related greenhouse gas emissions. Improving the energy performance of buildings is a critical element of building energy conservation. Furthermore, renovating existing buildings envelopes and systems offers significant opportunities for reducing Life-Cycle cost (LCC) and minimizing negative environmental impacts. This approach can be considered as one of the key strategies for achieving sustainable development goals at a relatively low cost, especially when compared with the demolition and reconstruction of new buildings. One of the main methodological and technical issues of this approach is selecting a desirable renovation strategy among a wide range of available options. The main motivation behind this research relies on trying to bridge the gap between building simulation, optimization algorithms, and Artificial Intelligence (AI) techniques, to take full advantage of the value of their couplings. Furthermore, for a whole building simulation and optimization, current simulation-based optimization models, often need thousands of simulation evaluations. Therefore, the optimization becomes unfeasible because of the computation time and complexity of the dependent parameters. To this end, one feasible technique to solve this problem is to implement surrogate models to computationally imitate expensive real building simulation models. The aim of this research is three-fold: (1) to propose a Simulation-Based Multi-Objective Optimization (SBMO) model for optimizing the selection of renovation scenarios for existing buildings by minimizing Total Energy Consumption (TEC), LCC and negative environmental impacts considering Life-Cycle Assessment (LCA); (2) to develop surrogate Artificial Neural Networks (ANNs) for selecting near-optimal building energy renovation methods; and (3) to develop generative deep Machine Learning Models (MLMs) to generate renovation scenarios considering TEC and LCC. This study considers three main areas of building renovation, which are the building envelope, Heating, Ventilation and Air-Conditioning (HVAC) system, and lighting system; each of which has a significant impact on building energy performance. On this premise, this research initially develops a framework for data collection and preparation to define the renovation strategies and proposes a comprehensive database including different renovation methods. Using this database, different renovation scenarios can be compared to find the near-optimal scenario based on the renovation strategy. Each scenario is created from the combination of several methods within the applicable strategy. The SBMO model simulates the process of renovating buildings by using the renovation data in energy analysis software to analyze TEC, LCC, and LCA and identifies the near-optimal renovation scenarios based on the selected renovation methods. Furthermore, an LCA tool is used to evaluate the environmental sustainability of the final decision. It is found that, although the proposed SBMO is accurate, the process of simulation is time consuming. To this end, the second objective focuses on developing robust MLMs to explore vast and complex data generated from the SBMO model and develop a surrogate building energy model to predict TEC, LCC, and LCA for all building renovation scenarios. The main advantage of these MLMs is improving the computing time while achieving acceptable accuracy. More specifically, the second developed model integrates the optimization power of SBMO with the modeling capability of ANNs. While, the proposed ANNs are found to provide satisfactory approximation to the SBMO model in a very short period of time, they do not have the capability to generate renovation scenarios. Finally, the third objective focuses on developing a generative deep learning building energy model using Variational Autoencoders (VAEs). The proposed semi-supervised VAEs extract deep features from a whole building renovation dataset and generate renovation scenarios considering TEC and LCC of existing institutional buildings. The proposed model also has the generalization ability due to its potential to reuse the dataset from a specific case in similar situations. The proposed models will potentially offer new venues in two directions: (1) to predict TEC, LCC, and LCA for different renovation scenarios, and select the near-optimal scenario, and (2) to generate renovation scenarios considering TEC and LCC. Architects and engineers can see the effects of different materials, HVAC systems, etc., on the energy consumption, and make necessary changes to increase the energy performance of the building. The proposed models encourage the implementation of sustainable materials and components to decrease negative environmental impacts. The ultimate impact of the practical implementation of this research is significant savings in buildings' energy consumption and having more environmentally friendly buildings within the predefined renovation budget.
Data Mining And Machine Learning In Building Energy Analysis
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Author : Frédéric Magoules
language : en
Publisher: John Wiley & Sons
Release Date : 2016-02-08
Data Mining And Machine Learning In Building Energy Analysis written by Frédéric Magoules and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-02-08 with Computers categories.
The energy consumption of a building has, in recent years, become a determining factor during its design and construction. With carbon footprints being a growing issue, it is important that buildings be optimized for energy conservation and CO2 reduction. This book therefore presents AI models and optimization techniques related to this application. The authors start with a review of recent models for the prediction of building energy consumption: engineering methods, statistical methods, artificial intelligence methods, ANNs and SVMs in particular. The book then focuses on SVMs, by first applying them to building energy consumption, then presenting the principles and various extensions, and SVR. The authors then move on to RDP, which they use to determine building energy faults through simulation experiments before presenting SVR model reduction methods and the benefits of parallel computing. The book then closes by presenting some of the current research and advancements in the field.
Data Driven Modelling Of Non Domestic Buildings Energy Performance
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Author : Saleh Seyedzadeh
language : en
Publisher: Springer Nature
Release Date : 2021-01-15
Data Driven Modelling Of Non Domestic Buildings Energy Performance written by Saleh Seyedzadeh and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-15 with Architecture categories.
This book outlines the data-driven modelling of building energy performance to support retrofit decision-making. It explains how to determine the appropriate machine learning (ML) model, explores the selection and expansion of a reasonable dataset and discusses the extraction of relevant features and maximisation of model accuracy. This book develops a framework for the quick selection of a ML model based on the data and application. It also proposes a method for optimising ML models for forecasting buildings energy loads by employing multi-objective optimisation with evolutionary algorithms. The book then develops an energy performance prediction model for non-domestic buildings using ML techniques, as well as utilising a case study to lay out the process of model development. Finally, the book outlines a framework to choose suitable artificial intelligence methods for modelling building energy performances. This book is of use to both academics and practising energy engineers, as it provides theoretical and practical advice relating to data-driven modelling for energy retrofitting of non-domestic buildings.
Development Of A Methodology For Fast Optimization Of Building Retrofit And Decision Making Support
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Author : Pengyuan Shen
language : en
Publisher:
Release Date : 2018
Development Of A Methodology For Fast Optimization Of Building Retrofit And Decision Making Support written by Pengyuan Shen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
The condition of current building stock in the United States raises the question of whether the energy performance of existing buildings can ever be environmentally sustainable. In the United States, buildings accounted for 39% of total energy consumption and 72% of total electricity consumption (USEPA 2009). In addition, current building energy use is projected to increase by 1.7% annually until 2025 (J.D. Ryan 2004). The great potential for energy reduction in existing buildings has created opportunities in building energy retrofit projects (Noris et al. 2013). A building renovation project must not only be affordable, taking into account factors such as investor budgets, payback period, economic risks and uncertainties, but also create a thermally comfortable indoor environment and is sustainable through its lifetime. The research objective of this dissertation is to develop a novel method to optimize the performance of buildings during their post-retrofit period in the future climate. The dissertation is organized in three sections: a) Develop a data-driven method for the hourly projection of energy use in the coming years, taking into account global climate change (GCC). Using machine learning algorithms, a validated data-driven model is used to predict the building's future hourly energy use based on simulation results generated by future extreme year weather data and it is demonstrated that GCC will change the optimal solution of future energy conservation measure (ECM) combination. b) Develop a simplified building performance simulation tool based on a dynamic hourly simulation algorithm taking into account the thermal flux among zones. The tool named SimBldPy is tested on EnergyPlus models with DOE reference buildings. Its performance and fidelity in simulating hourly energy use with different heating and cooling set points in each zone, under various climate conditions, and with multiple ECMs being applied to the building, has been validated. This tool and modeling method could be used for rapid modeling and assessment of building energy for a variety of ECM options. c) Use a non-dominated sorting technique to complete the multi-objective optimization task and design a schema to visualize optimization results and support the decision-making process after obtaining the multi-objective optimization results. By introducing the simplified hourly simulation model and the random forest (RF) models as a substitute for traditional energy simulation tools in objective function assessment, certain deep retrofit problem can be quickly optimized. Generated non-dominated solutions are rendered and displayed by a layered schema using agglomerative hierarchical clustering technique. The optimization method is then implemented on a Penn campus building for case study, and twenty out of a thousand retrofit plans can be recommended using the proposed decision-making method. The proposed decision making support framework is demonstrated by its robustness to the problem of deep retrofit optimization and is able to provide support for brainstorming and enumerate various possibilities during the process of making the decision.
Advances In Digitalization And Machine Learning For Integrated Building Transportation Energy Systems
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Author : Yuekuan Zhou
language : en
Publisher: Elsevier
Release Date : 2023-11-20
Advances In Digitalization And Machine Learning For Integrated Building Transportation Energy Systems written by Yuekuan Zhou and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-20 with Computers categories.
Advances in Digitalization and Machine Learning for Integrated Building-Transportation Energy Systems examines the combined impact of buildings and transportation systems on energy demand and use. With a strong focus on AI and machine learning approaches, the book comprehensively discusses each part of the energy life cycle, considering source, grid, demand, storage, and usage. Opening with an introduction to smart buildings and intelligent transportation systems, the book presents the fundamentals of AI and its application in renewable energy sources, alongside the latest technological advances. Other topics presented include building occupants’ behavior and vehicle driving schedule with demand prediction and analysis, hybrid energy storages in buildings with AI, smart grid with energy digitalization, and prosumer-based P2P energy trading. The book concludes with discussions on blockchain technologies, IoT in smart grid operation, and the application of big data and cloud computing in integrated smart building-transportation energy systems. A smart and flexible energy system is essential for reaching Net Zero whilst keeping energy bills affordable. This title provides critical information to students, researchers and engineers wanting to understand, design, and implement flexible energy systems to meet the rising demand in electricity. Introduces spatiotemporal energy sharing with new energy vehicles and human-machine interactions Discusses the potential for electrification and hydrogenation in integrated building-transportation systems for sustainable development Highlights key topics related to traditional energy consumers, including peer-to-peer energy trading and cost-benefit business models
Integrative Approach To Comprehensive Building Renovations
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Author : Vesna Žegarac Leskovar
language : en
Publisher: Springer
Release Date : 2019-03-18
Integrative Approach To Comprehensive Building Renovations written by Vesna Žegarac Leskovar and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-18 with Architecture categories.
This book presents a new approach to building renovation, combining aspects of various professional disciplines, integrating green building design, structural stability, and energy efficiency. It draws attention to several often-overlooked qualities of buildings that should be comprehensively integrated into the context of building renovation. The book presents an overview of the most important renovation approaches according to their scope, intensity, and priorities. Combining basic theoretical knowledge and the authors’ scientific research it emphasizes the importance of simultaneous consideration of energy efficiency and structural stability in building renovation processes. It simultaneously analyses the effects of various renovation steps related to the required level of energy efficiency, while it also proposes the options of building extension with timber-glass upgrade modules as the solution to a shortage of usable floor areas occurring in large cities. This book offers building designers and decision makers a tool for predicting energy savings in building renovation processes and provides useful guidelines for architects, city developers and students studying architecture and civil engineering. Additionally, it demonstrates how specific innovations, e.g., building extensions with timber-glass modules, can assist building industry companies in the planning and development of their future production. The main aim of the current book is to expose various approaches to the renovation of existing buildings and to combine practical experience with existing research, in order to disseminate knowledge and raise awareness on the importance of integrative and interdisciplinary solutions.
Cost Effective Energy Efficient Building Retrofitting
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Author : F. Pacheco-Torgal
language : en
Publisher: Woodhead Publishing
Release Date : 2017-01-03
Cost Effective Energy Efficient Building Retrofitting written by F. Pacheco-Torgal and has been published by Woodhead Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-01-03 with Technology & Engineering categories.
Cost-Effective Energy Efficient Building Retrofitting:Materials, Technologies, Optimization and Case Studies provides essential knowledge for civil engineers, architects, and other professionals working in the field of cost-effective energy efficient building retrofitting. The building sector is responsible for high energy consumption and its global demand is expected to grow as each day there are approximately 200,000 new inhabitants on planet Earth. The majority of electric energy will continue to be generated from the combustion of fossil fuels releasing not only carbon dioxide, but also methane and nitrous oxide. Energy efficiency measures are therefore crucial to reduce greenhouse gas emissions of the building sector. Energy efficient building retrofitting needs to not only be technically feasible, but also economically viable. New building materials and advanced technologies already exist, but the knowledge to integrate all active components is still scarce and far from being widespread among building industry stakeholders. Emphasizes cost-effective methods for the refurbishment of existing buildings, presenting state-of-the-art technologies Includes detailed case studies that explain various methods and Net Zero Energy Explains optimal analysis and prioritization of cost effective strategies
Machine Learning Paradigms For Building Energy Performance Simulations
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Author : Arfa Nawal Aijazi
language : en
Publisher:
Release Date : 2017
Machine Learning Paradigms For Building Energy Performance Simulations written by Arfa Nawal Aijazi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
This research seeks to overcome a technical limitation of building energy performance simulations, the computation time, by using surrogate modeling, a class of supervised machine learning techniques where the output is a performance metric. Though early machine learning methods were introduced decades ago, the convergence of computation power, more data collection, and maturation of methods has led to an explosion in the types of problems machine learning can be applied to. A comparison of several common surrogate modeling techniques found that parametric radial basis functions and Kriging are highly accurate regression techniques for predicting building energy consumption. For a single climate, these regression techniques can predict the total energy consumption to within 2% of a detailed energy simulation, but in a fraction of a second, about five orders of magnitude faster. Integrating a Kriging surrogate model with multi-objective optimization, allowed for finding retrofit recommendations in Lisbon that are cost effective and can reduce the present-day energy consumption of an existing apartment by up to 20%. Similarly, integrating surrogate model with multi-objective optimization can find retrofit options in Boston that can reduce the present-day energy consumption and unmet hours in the future. Combined this body of works strives to add value to existing building energy performance simulation tools as more than just an exercise for code compliance but as a real design tool that can guide decision making.
Building Energy Audits Diagnosis And Retrofitting
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Author : Constantinos A. Balaras
language : en
Publisher: MDPI
Release Date : 2021-01-12
Building Energy Audits Diagnosis And Retrofitting written by Constantinos A. Balaras and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-12 with Science categories.
The book “Building Energy Audits-Diagnosis and Retrofitting” is a collection of twelve papers that focus on the built environment in order to systematically collect and analyze relevant data for the energy use profile of buildings and extended for the sustainability assessment of the built environment. The contributions address historic buildings, baselines for non-residential buildings from energy performance audits, and from in-situ measurements, monitoring, and analysis of data, and verification of energy saving and model calibration for various building types. The works report on how to diagnose existing problems and identify priorities, assess, and quantify the opportunities and measures that improve the overall building performance and the environmental quality and well-being of occupants in non-residential buildings and houses. Several case studies and lessons learned from the field are presented to help the readers identify, quantify, and prioritize effective energy conservation and efficiency measures. Finally, a new urban sustainability audit and rating method of the built environment addresses the complexities of the various issues involved, providing practical tools that can be adapted to match local priorities in order to diagnose and evaluate the current state and future scenarios towards meeting specific sustainable development goals and local priorities.
Handbook Of Energy Efficiency In Buildings
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Author : Umberto Desideri
language : en
Publisher: Butterworth-Heinemann
Release Date : 2018-11-12
Handbook Of Energy Efficiency In Buildings written by Umberto Desideri and has been published by Butterworth-Heinemann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-12 with Technology & Engineering categories.
Handbook of Energy Efficiency in Buildings: A Life Cycle Approach offers a comprehensive and in-depth coverage of the subject with a further focus on the Life Cycle. The editors, renowned academics, invited a diverse group of researchers to develop original chapters for the book and managed to well integrate all contributions in a consistent volume. Sections cover the role of the building sector on energy consumption and greenhouse gas emissions, international technical standards, laws and regulations, building energy efficiency and zero energy consumption buildings, the life cycle assessment of buildings, from construction to decommissioning, and other timely topics. The multidisciplinary approach to the subject makes it valuable for researchers and industry based Civil, Construction, and Architectural Engineers. Researchers in related fields as built environment, energy and sustainability at an urban scale will also benefit from the books integrated perspective. Presents a complete and thorough coverage of energy efficiency in buildings Provides an integrated approach to all the different elements that impact energy efficiency Contains coverage of worldwide regulation