相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。
Article
Green & Sustainable Science & Technology
Lena Gurriaran et al.
Summary: Rising global temperatures in the Arabian Peninsula region have led to increased demand for air conditioning, resulting in higher electricity consumption and CO2 emissions. This study focuses on Qatar as a representative country to understand the impact of future regional warming on electricity demand and CO2 emissions. The findings indicate that temperature has a relatively small effect compared to socioeconomic factors, but warming alone could increase electricity demand by 5-35% and CO2 emissions by 20-35% by the end of the century.
JOURNAL OF CLEANER PRODUCTION
(2023)
Article
Green & Sustainable Science & Technology
Ziheng Li et al.
Summary: The Pearl River Delta urban agglomeration is facing the challenge of environmental degradation. The study found that the ecological footprint has significantly increased in the region, but the growth rate has slowed down since 2014. Population growth contributes the most to the expansion of the ecological footprint, followed by per capita GDP and urban green area, while the impact of technology on the ecological footprint remains uncertain. The study also found that there is no classical Environmental Kuznets Curve relationship between economic development and ecological footprint in the region.
JOURNAL OF CLEANER PRODUCTION
(2023)
Article
Energy & Fuels
Yuxuan Shen et al.
Summary: Supported by the combination of advanced BIM technique and intelligent algorithms, this paper develops a systematic framework using explainable machine learning and multi-objective optimization for automatic prediction and optimization of building energy performance towards sustainable development. It has been verified in a case study for green building design, showing highly precise prediction ability and the identification of Pareto optimal solutions in minimizing energy consumption, CO2 emission, and indoor thermal discomfort.
Article
Thermodynamics
Changfeng Shi et al.
Summary: This paper studies the carbon peak by analyzing low-carbon economics and deep learning. It uses the STIRPAT model and ridge regression to distinguish and rank the importance of influencing factors on carbon emissions. Additionally, it constructs an innovative GA-LSTM model for prediction. The results show that China's carbon emissions have been increasing, with only technological level having an inhibitory effect. China's carbon peak is projected to occur around 2030, with peak values of 11.82, 11.94, and 11.64 billion tons under different scenarios. The paper emphasizes the importance of focusing on energy consumption structure, industrial structure, and technological level for emission reduction work in China.
Article
Thermodynamics
Jie Xiong et al.
Summary: Climate change will affect building heating and cooling demands. To anticipate these changes, this study used meteorological data generated from global climate models to simulate the impacts on heating and cooling loads. By 2060, it is estimated that the total energy consumption in urban residential buildings will increase by 5%, with a higher percentage of cooling energy use. Enhanced building thermal performance can effectively reduce both heating and cooling demand, diminishing the effects of climate change.
Article
Construction & Building Technology
Abdulrahim Ali et al.
Summary: Building Performance Simulation (BPS) is a widely used technique to evaluate building design and operation strategies. To overcome the high computational costs of BPS models, researchers have turned to surrogate modeling using machine learning algorithms. This paper evaluates and compares different machine learning algorithms as surrogates for BPS predictions of building performance under different operational settings.
ENERGY AND BUILDINGS
(2023)
Article
Construction & Building Technology
Sheng Liu et al.
Summary: This study investigates the impact of future climatic uncertainties on decision-making regarding building energy retrofits using high-rise public rental housing buildings in subtropical Hong Kong as a case study. The results reveal that future climatic uncertainties significantly influence the optimal values of building energy retrofit measures. The study suggests adopting passive house infiltration standard and thick polyurethane foam insulation for long-term future climatic conditions, while other factors such as glazing materials, shading projection factors, and window-to-wall ratio are not susceptible to future climatic uncertainties.
SUSTAINABLE CITIES AND SOCIETY
(2023)
Article
Construction & Building Technology
Zhaoyun Zeng et al.
Summary: This paper presents a comprehensive optimization framework for designing high-performance building systems, filling the research gap in the literature regarding the interdependencies between objective functions, constraints, and design variables. Two application studies demonstrate the practical use of this framework, including constructing cost models and solving multi-objective optimization problems.
JOURNAL OF BUILDING ENGINEERING
(2023)
Article
Environmental Sciences
Ebenezer K. Siabi et al.
Summary: The effects of climate change in Ghana, particularly in the Greater Accra region, have worsened in the past two decades. Limited availability of climate change data makes it challenging to assess climate change in data-scarce regions like Ghana. This study utilizes multiple regional climate models to evaluate climate change in Greater Accra and finds that temperature, especially at night, is expected to increase in the future. These findings can provide valuable insights for Ghanaian policymaking on sustainable development goals and contributions to the Paris Agreement.
Article
Thermodynamics
Ruijun Chen et al.
Summary: This research proposes an integrated strategy for optimizing building performance from the whole life cycle perspective. By using feature elimination and ensemble learning model, accurate predictions of life cycle carbon emissions, life cycle costs, and indoor discomfort hours can be obtained. Then, the optimal optimization algorithm is selected and the best building scheme is chosen based on the proposed solution. The results demonstrate that this strategy can efficiently optimize building objectives and generate a more balanced and optimal building scheme.
Article
Construction & Building Technology
Delphine Ramon et al.
Summary: Strict energy performance requirements have reduced buildings' operational energy use and environmental impact in the past decades. Life cycle assessment (LCA) is used to assess the environmental impact of buildings, but current studies often ignore the potential changes in operational energy use due to climate change. This study improves the assessment by considering the variations in yearly operational energy use caused by climate change and changes in the electricity mix. The results show that climate change scenarios can significantly affect the energy use and environmental impact of buildings.
ENERGY AND BUILDINGS
(2023)
Article
Construction & Building Technology
Xiaoyu Jia et al.
Summary: This research paper presents the development of occupant behavior modules in DeST 3.0 and discusses the effects of integrated simulation of occupant behavior on building energy consumption through two case studies.
ENERGY AND BUILDINGS
(2023)
Article
Thermodynamics
Jingjing An et al.
Summary: This study developed 151 prototype building models and a large database of 9225 models to simulate building energy in different cities. These models can be used for building energy conservation research, including analysis of energy-saving technologies, advanced controls, and new policies, as well as providing a reference for the development of building energy codes and standards.
BUILDING SIMULATION
(2023)
Article
Green & Sustainable Science & Technology
Razak Olu-Ajayi et al.
Summary: Comparing different machine learning algorithms for energy performance assessment at the design stage of residential buildings, this research shows the feasibility of developing a high performing ML model. The Gradient Boosting (GB) model outperformed others with an accuracy of 0.67 in predicting building energy performance.
ENERGY FOR SUSTAINABLE DEVELOPMENT
(2022)
Review
Thermodynamics
Da Yan et al.
Summary: The paragraph introduces the contribution of buildings to worldwide energy consumption and emphasizes the importance of building energy modeling programs. To meet simulation demands, a new-generation building performance simulation platform called DeST 3.0 has been developed, offering numerous new simulation features. DeST 3.0 has been thoroughly evaluated and applied throughout the building lifecycle.
BUILDING SIMULATION
(2022)
Article
Energy & Fuels
Mohamed El Amine Ben Seghier et al.
Summary: This paper proposes a practical implementation of robust ensemble learning models for accurate prediction of the internal corrosion rate in oil and gas pipelines. The extreme gradient boosting model shows the highest performance in predicting the internal corrosion rate.
JOURNAL OF NATURAL GAS SCIENCE AND ENGINEERING
(2022)
Article
Construction & Building Technology
Hainan Yan et al.
Summary: This study proposes an integrated framework for predicting and optimizing the performance of residential buildings in Singapore. The results show that enhancing building insulation and indoor lighting can better cope with future warming climate conditions.
BUILDING AND ENVIRONMENT
(2022)
Article
Construction & Building Technology
Xue Liu et al.
Summary: This study investigates the performance and interpretability of machine learning-based energy usage models through the comparison of feature selection methods and analysis of model interpretability. The results suggest that the wrapper method is effective in improving model accuracy.
ENERGY AND BUILDINGS
(2022)
Article
Green & Sustainable Science & Technology
Xianguo Wu et al.
Summary: This study proposes an intelligent optimization framework combining BIM-DB and RF-NSGA-III for multiobjective optimization of NZEB. The effectiveness of the method is verified through simulation and establishing the relationship between parameters and NZEB performance. The results show that the method can achieve near zero energy consumption and achieve good energy-saving results.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2022)
Article
Energy & Fuels
Yizhe Xu et al.
Summary: This paper proposes a multi-objective coordinated design optimization method for school buildings, achieving efficient global design optimization through coordinated design variables, envelope optimization, and energy system optimization. The method is validated through a case study, demonstrating its reliability and stability.
Article
Energy & Fuels
Ruijun Chen et al.
Summary: In recent years, the development of techniques and strategies for predicting building energy consumption and thermal comfort has been driven by increasing climate change. This study evaluated 25 features of an office building model and explored the effects of different sampling methods and input parameters on the prediction performance of building carbon emission and indoor discomfort hour using 10 machine learning algorithms. The results indicated that the Sobol sampling method achieved the best prediction effect in different combinations of features and machine learning algorithms. Additionally, the artificial neural network was the best learning algorithm when the contribution rate was 100%, but the optimal algorithm varied at different contribution rate stages.
Review
Green & Sustainable Science & Technology
Rajan Kumar Jaysawal et al.
Summary: This paper provides a literature review on Net Zero Energy Buildings (NZEBs), discussing their definition, development, and the potential for using renewable energy sources for self-sustainability. In addition, it explores the impact of climatic conditions in different regions on NZEB.
CLEANER ENGINEERING AND TECHNOLOGY
(2022)
Article
Construction & Building Technology
Maria Anastasiadou et al.
Summary: This study aims to improve the energy performance of existing buildings by identifying the latest and most appropriate machine learning and statistical techniques through analyzing a large amount of building energy performance certification data and other data sources. Using a systematic literature review and meta-analysis method, specific factors that influence building energy performance were detected, leading to the identification and analysis of machine learning and statistical approaches based on building energy certification data. The main contribution of this study is a conceptual and theoretical framework for analyzing the energy performance of buildings using intelligent computational methods.
Article
Construction & Building Technology
Keivan Bamdad et al.
Summary: A simulation-based optimisation method is developed in this paper to compare energy-optimised designs under current and future climates. Results suggest that optimising under future climate conditions can lead to different optimal building designs.
ENERGY AND BUILDINGS
(2021)
Article
Construction & Building Technology
Yong Ding et al.
Summary: Machine learning algorithms were used to analyze energy consumption data of public buildings, identifying the top ten most important features and providing insights for database establishment and big data analysis.
ENERGY AND BUILDINGS
(2021)
Review
Construction & Building Technology
Alexander Hollberg et al.
Summary: Life Cycle Assessment (LCA) is increasingly used in decision-making in building and neighborhood design processes. The visualization of LCA results is crucial for interpretation and decision-making. The current state of art in visualizing LCA results is reviewed in this paper to provide a structured overview, with discussions on potential future developments for intuitive and design-integrated visualizations.
BUILDING AND ENVIRONMENT
(2021)
Article
Energy & Fuels
Robin Mutschler et al.
Summary: Future energy policy and system planning needs to consider the complex relationships between climate change, technology uptake, population growth, and building energy demand. Studies show that climate warming will significantly increase building cooling demand, especially in temperate climates, highlighting the critical role of air-conditioning technology in reducing future cooling energy demand.
Article
Construction & Building Technology
Yukai Zou et al.
Summary: This study used simulation-based method to predict the life cycle energy performance of residential buildings in different climate zones of China, showing that heating energy is expected to decrease and cooling energy is expected to increase in the future under climate change. The research also discovered different trends in residential building energy demand across various climate zones.
BUILDING AND ENVIRONMENT
(2021)
Article
Green & Sustainable Science & Technology
Bin Chen et al.
Summary: The study proposed a framework combining Building Information Modeling with LSSVM and NSGA-II to analyze the impact of building envelope parameters on energy consumption. By optimizing the building envelope design parameters, the energy consumption efficiency and thermal comfort of the building were significantly improved.
JOURNAL OF CLEANER PRODUCTION
(2021)
Article
Construction & Building Technology
Yukai Zou et al.
Summary: Building performance is significantly influenced by weather conditions, and optimizing building performance under future climate conditions can greatly improve performance. This study demonstrates that considering future climate changes in optimization processes can lead to notable improvements in energy efficiency, thermal comfort, and daylighting performance, especially in hot and humid regions.
JOURNAL OF BUILDING ENGINEERING
(2021)
Article
Green & Sustainable Science & Technology
Rebecca Neves et al.
Summary: This study analyzes the path to net-zero energy for HVAC + PV systems, determining optimal combinations for different climate zones and providing a template for predicting net-zero systems in areas beyond the 12 studied. Geothermal HVAC system + PV is favored in climates with significant winter heating demand, while baseline + PV is preferred in cooling-dominant climates, with annual household savings exceeding $3,900 and $3,500 respectively. Patterns in local soil properties, sun intensity, and financial incentives allow for nationwide net-zero system predictions.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2021)
Article
Engineering, Environmental
Mark Kyeredey Ansah et al.
Summary: This study introduces a new tier-hybrid uncertainty assessment approach to improve the effectiveness of building LCAs. Through a case study, it is found that uncertainties are the smallest in the materials production stage and reduce in other lifecycle phases. Additionally, alternative design strategies and materials explored can effectively reduce energy use and carbon emission.
RESOURCES CONSERVATION AND RECYCLING
(2021)
Article
Construction & Building Technology
Ali Shehadeh et al.
Summary: This study explores accurate modeling of residual value prediction for heavy construction equipment using machine learning methods, comparing three different algorithms for prediction accuracy through experiments.
AUTOMATION IN CONSTRUCTION
(2021)
Article
Construction & Building Technology
Dnyandip K. Bhamare et al.
Summary: This study developed a machine learning and deep learning-based model for predicting the thermal performance of PCM integrated roof buildings, with results showing that Gradient boosting regression is the best-performing model.
BUILDING AND ENVIRONMENT
(2021)
Article
Environmental Sciences
R. Andrew Tirpak et al.
Summary: Climate stationarity is challenged by global climate change, introducing uncertainty in the design of urban drainage networks and green infrastructure practices. Multiple climate models should be considered to address contradictions in future climate change projections. Increasing bioretention surface area relative to the contributing catchment provides the greatest overall return on historic performance under future climate conditions.
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2021)
Article
Energy & Fuels
Ana Vukadinovic et al.
Summary: This paper discusses the optimization of a detached passive building with a sunspace using a non-dominant sorting genetic algorithm (NSGA-II). The study shows that the window-to-wall ratio is the most influential factor in energy performance, and recommends the use of low-emissivity argon-filled glazing, properly insulated opaque walls, and minimal shading on the south facade for optimal building performance.
Article
Construction & Building Technology
Sheng Liu et al.
Summary: This study on future long-term monthly electricity demand in Hong Kong found that the Gradient Boosting Decision Tree (GBDT) method performed the best in terms of accuracy, generalization ability, and time-series stability, while the Artificial Neural Network (ANN) method exhibited the lowest accuracy and lower generalization ability.
SUSTAINABLE CITIES AND SOCIETY
(2021)
Article
Construction & Building Technology
Ruijun Chen et al.
Summary: This study aims to evaluate the comprehensive percentage influence of input parameters on building energy and comfort performance in 24 coastal cities of China and identify key parameters that can impact 70% of energy demand and comfort performance. The Pearson with Quasi-random sampling method is recommended as the most reliable SA assessment method in building simulation.
Article
Energy & Fuels
Eugenio Rodrigues et al.
Article
Green & Sustainable Science & Technology
Xiao-Juan Li et al.
JOURNAL OF CLEANER PRODUCTION
(2020)
Article
Energy & Fuels
A. T. D. Perera et al.
Article
Construction & Building Technology
Yue Teng et al.
BUILDING AND ENVIRONMENT
(2020)
Article
Construction & Building Technology
Joao Bravo Dias et al.
BUILDING AND ENVIRONMENT
(2020)
Article
Green & Sustainable Science & Technology
Xiaocun Zhang et al.
JOURNAL OF CLEANER PRODUCTION
(2020)
Article
Energy & Fuels
Ivan Garcia Kerdan et al.
Article
Computer Science, Artificial Intelligence
Ke Li et al.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2019)
Article
Thermodynamics
Fabiana Silvero et al.
BUILDING SIMULATION
(2019)
Article
Construction & Building Technology
Martin Roeck et al.
BUILDING AND ENVIRONMENT
(2018)
Article
Thermodynamics
Delia D'Agostino et al.
Article
Construction & Building Technology
Vanessa Gomes et al.
ENERGY AND BUILDINGS
(2018)
Article
Construction & Building Technology
Zhibin Wu et al.
ENERGY AND BUILDINGS
(2018)
Review
Green & Sustainable Science & Technology
Wei Tian et al.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2018)
Article
Green & Sustainable Science & Technology
Fangqiu Xu et al.
JOURNAL OF CLEANER PRODUCTION
(2017)
Article
Construction & Building Technology
Pedro Munoz et al.
SUSTAINABLE CITIES AND SOCIETY
(2017)
Article
Thermodynamics
Ambrose Dodoo et al.
Article
Construction & Building Technology
Debaditya Chakraborty et al.
SUSTAINABLE CITIES AND SOCIETY
(2016)
Article
Engineering, Environmental
Stephanie Muller et al.
INTERNATIONAL JOURNAL OF LIFE CYCLE ASSESSMENT
(2016)
Article
Environmental Sciences
Detlef P. van Vuuren et al.
Article
Construction & Building Technology
A. R. Day et al.
ENERGY AND BUILDINGS
(2009)
Article
Construction & Building Technology
T Frank
ENERGY AND BUILDINGS
(2005)
