Building energy simulation and its application for building performance optimization: A review of methods, tools, and case studies

[1]  Mark B. Adams,et al.  How close are urban scale building simulations to measured data? Examining bias derived from building metadata in urban building energy modeling , 2022, Applied Energy.

[2]  S. Wieprecht,et al.  Energy load prediction on structures and buildings-Effect of numerical model complexity on simulation of heat fluxes across the structure/environment interface , 2022, Applied Energy.

[3]  Xianguo Wu,et al.  Intelligent optimization framework of near zero energy consumption building performance based on a hybrid machine learning algorithm , 2022, Renewable and Sustainable Energy Reviews.

[4]  A. Gasparella,et al.  Large scale energy analysis and renovation strategies for social housing in the historic city of Venice , 2022, Sustainable Energy Technologies and Assessments.

[5]  Jinqing Peng,et al.  Optimal battery schedule for grid-connected photovoltaic-battery systems of office buildings based on a dynamic programming algorithm , 2022, Journal of Energy Storage.

[6]  Yiqun Pan,et al.  Surrogate modeling for long-term and high-resolution prediction of building thermal load with a metric-optimized KNN algorithm , 2022, Energy and Built Environment.

[7]  A. Simonsen,et al.  Assessing the impact of urban microclimate on building energy demand by coupling CFD and building performance simulation , 2022, Journal of Building Engineering.

[8]  J. Maguire,et al.  Carbon emission responsive building control: A case study with an all-electric residential community in a cold climate , 2022, Applied Energy.

[9]  Qianchuan Zhao,et al.  Energy baseline prediction for buildings: A review , 2022, Results in Control and Optimization.

[10]  E. Rosenberg,et al.  Comparing Model Projections with Reality: Experiences from Modelling Building Stock Energy Use in Norway , 2022, Energy and Buildings.

[11]  A. Zarrella,et al.  Evaluation of the impact of input uncertainty on urban building energy simulations using uncertainty and sensitivity analysis , 2022, Applied Energy.

[12]  J. Widén,et al.  Advancing Urban Building Energy Modelling through new model components and applications: A review , 2022, Energy and Buildings.

[13]  C. Bay,et al.  Distributed model predictive control for coordinated, grid-interactive buildings , 2022, Applied Energy.

[14]  Ke Yan,et al.  Chiller faults detection and diagnosis with sensor network and adaptive 1D CNN , 2022, Digit. Commun. Networks.

[15]  A. Katal,et al.  Urban building energy and microclimate modeling – From 3D city generation to dynamic simulations , 2022, Energy.

[16]  Zhihan Lv,et al.  Deep learning for assessment of environmental satisfaction using BIM big data in energy efficient building digital twins , 2022, Sustainable Energy Technologies and Assessments.

[17]  O. Knio,et al.  A weather-clustering and energy-thermal comfort optimization methodology for indoor cooling in subtropical desert climates , 2022, Journal of Building Engineering.

[18]  Anjukan Kathirgamanathan,et al.  Enhancing energy management in grid-interactive buildings: A comparison among cooperative and coordinated architectures , 2022, Applied Energy.

[19]  M. Jafari,et al.  Adaptable scheduling of smart building communities with thermal mapping and demand flexibility , 2022, Applied Energy.

[20]  Yahui Du,et al.  Multi-regional building energy efficiency intelligent regulation strategy based on multi-objective optimization and model predictive control , 2022, Journal of Cleaner Production.

[21]  Yiqun Pan,et al.  A Deep-Learning-Based Meta-Modeling Workflow for Thermal Load Forecasting in Buildings: Method and a Case Study , 2022, Buildings.

[22]  Kai Cao,et al.  Better Understanding on Impact of Microclimate Information on Building Energy Modelling Performance for Urban Resilience , 2022, Sustainable Cities and Society.

[23]  Yelun Peng,et al.  Tube-based robust model predictive control of multi-zone demand-controlled ventilation systems for energy saving and indoor air quality , 2022, Applied Energy.

[24]  W. Na,et al.  A Bayesian Approach with Urban-Scale Energy Model to Calibrate Building Energy Consumption for Space Heating: A Case Study of Application in Beijing , 2022, SSRN Electronic Journal.

[25]  J. Widén,et al.  Evaluation of simplified building energy models for urban-scale energy analysis of buildings , 2022, Building and Environment.

[26]  S. Ikeda,et al.  Automated computational design method for energy systems in buildings using capacity and operation optimization , 2022, Applied Energy.

[27]  Y. Yamaguchi,et al.  Building stock energy modeling considering building system composition and long-term change for climate change mitigation of commercial building stocks , 2022, Applied Energy.

[28]  D. Müller,et al.  Towards an integrated design of heat pump systems: Application of process intensification using two-stage optimization , 2021, Energy Conversion and Management.

[29]  Dirk Müller,et al.  Simulation-based design optimization of heat pump systems considering building variations , 2021 .

[30]  J. Teller,et al.  An energy consumption model for the Algerian residential building’s stock, based on a triangular approach: Geographic Information System (GIS), regression analysis and hierarchical cluster analysis. , 2021 .

[31]  Chao Ren,et al.  Modelling building energy use at urban scale: A review on their account for the urban environment , 2021 .

[32]  U. Eicker,et al.  A new workflow for detailed urban scale building energy modeling using spatial joining of attributes for archetype selection , 2021, Journal of Building Engineering.

[33]  C. Treeck,et al.  Urban Energy Simulations using Open CityGML Models: A Comparative Analysis , 2021, Energy and Buildings.

[34]  G. Gharehpetian,et al.  Optimal scheduling of residential building energy system under B2G, G2B and B2B operation modes , 2021, International Journal of Energy and Environmental Engineering.

[35]  Bratislav D. Blagojević,et al.  Energy performance of air-conditioned buildings based on short-term weather forecast , 2021, E3S Web of Conferences.

[36]  Yingru Zhao,et al.  Prioritizing urban planning factors on community energy performance based on GIS-informed building energy modeling , 2021 .

[37]  Sen Huang,et al.  Open-source Modelica models for the control performance simulation of chiller plants with water-side economizer , 2021 .

[38]  Melissa M. Bilec,et al.  Urban building energy model: Database development, validation, and application for commercial building stock , 2021 .

[39]  Yiqun Pan,et al.  Evaluating the impact of shading from surrounding buildings on heating/ cooling energy demands of different community forms , 2021, Building and Environment.

[40]  Usman Ali,et al.  Review of urban building energy modeling (UBEM) approaches, methods and tools using qualitative and quantitative analysis , 2021 .

[41]  Xing Jin,et al.  A systematic method to develop three dimensional geometry models of buildings for urban building energy modeling , 2021, Sustainable Cities and Society.

[42]  A. Malkawi,et al.  Early-Phase Performance-Driven Design using Generative Models , 2021, ArXiv.

[43]  C. Reinhart,et al.  Using Urban Building Energy Modelling (UBEM) to support the new European Union’s Green Deal: Case study of Dublin Ireland , 2021 .

[44]  Shengwei Wang,et al.  A hierarchical optimal control strategy for continuous demand response of building HVAC systems to provide frequency regulation service to smart power grids , 2021 .

[45]  Achim Benjamin Spaeth,et al.  Decision-making pathways to daylight efficiency for office buildings with balconies in the tropics , 2021 .

[46]  Francesco Pirotti,et al.  EUReCA: An open-source urban building energy modelling tool for the efficient evaluation of cities energy demand , 2021 .

[47]  Runming Yao,et al.  Modelling heating and cooling energy demand for building stock using a hybrid approach , 2021 .

[48]  Adrian Chong,et al.  Data science for building energy efficiency: A comprehensive text-mining driven review of scientific literature , 2021, Energy and Buildings.

[49]  Carol C. Menassa,et al.  From BIM to digital twins: a systematic review of the evolution of intelligent building representations in the AEC-FM industry , 2021, J. Inf. Technol. Constr..

[50]  David Griffin,et al.  Utilizing Industry 4.0 on the Construction Site: Challenges and Opportunities , 2021, IEEE Transactions on Industrial Informatics.

[51]  Shengwei Wang,et al.  A risk-based robust optimal chiller sequencing control strategy for energy-efficient operation considering measurement uncertainties , 2020 .

[52]  Pei Huang,et al.  Global sensitivity analysis for key parameters identification of net-zero energy buildings for grid interaction optimization , 2020 .

[53]  Zachary M. Berzolla,et al.  From concept to application: A review of use cases in urban building energy modeling , 2020, Applied Energy.

[54]  Jianshun Zhang,et al.  Green Design Studio: A modular-based approach for high-performance building design , 2020, Building Simulation.

[55]  Jin Zhou,et al.  Simulation-aided occupant-centric building design: A critical review of tools, methods, and applications , 2020, Energy and Buildings.

[56]  Ye Xu,et al.  Operation optimization of a solar hybrid CCHP system for adaptation to climate change , 2020 .

[57]  Lian Zhao,et al.  Mitigation Strategies for Overheating and High Carbon Dioxide Concentration within Institutional Buildings: A Case Study in Toronto, Canada , 2020, Buildings.

[58]  Xu Zhang,et al.  A virtual sensor based self-adjusting control for HVAC fast demand response in commercial buildings towards smart grid applications , 2020 .

[59]  Nsilulu Tresor Mbungu,et al.  An overview of renewable energy resources and grid integration for commercial building applications , 2020 .

[60]  Shunian Qiu,et al.  Model-free optimal chiller loading method based on Q-learning , 2020 .

[61]  Jun Ma,et al.  Simulation optimisation towards energy efficient green buildings: Current status and future trends , 2020 .

[62]  Zihao Wang,et al.  A review of data mining technologies in building energy systems: Load prediction, pattern identification, fault detection and diagnosis , 2020 .

[63]  Shuai Lu,et al.  Investigation on the potential of improving daylight efficiency of office buildings by curved facade optimization , 2020 .

[64]  E. Williams,et al.  Residential building stock model for evaluating energy retrofit programs in Saudi Arabia , 2020 .

[65]  Mahdi Mahdikhani,et al.  Energy performance optimization of existing buildings: A literature review , 2020 .

[66]  Rui Ma,et al.  Heuristic optimization for grid-interactive net-zero energy building design through the glowworm swarm algorithm , 2020 .

[67]  Nashwan Dawood,et al.  On-demand monitoring of construction projects through a game-like hybrid application of BIM and machine learning , 2020, Automation in Construction.

[68]  Wei Xu,et al.  Research on a Systematical Design Method for Nearly Zero-Energy Buildings , 2019, Sustainability.

[69]  Shengwei Wang,et al.  Optimal design of data center cooling systems concerning multi-chiller system configuration and component selection for energy-efficient operation and maximized free-cooling , 2019 .

[70]  Arno Schlueter,et al.  Coupled simulation of thermally active building systems to support a digital twin , 2019, Energy and Buildings.

[71]  Soolyeon Cho,et al.  Design optimization of building geometry and fenestration for daylighting and energy performance , 2019, Solar Energy.

[72]  Xiaoxia Qi,et al.  A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network , 2019, Applied Energy.

[73]  Kim Bjarne Wittchen,et al.  A hybrid modelling method for improving estimates of the average energy-saving potential of a building stock , 2019, Energy and Buildings.

[74]  Y. Liu,et al.  A Building Information Model (BIM) and Artificial Neural Network (ANN) Based System for Personal Thermal Comfort Evaluation and Energy Efficient Design of Interior Space , 2019, Sustainability.

[75]  Mehdi Seyedmahmoudian,et al.  Short-term PV power forecasting using hybrid GASVM technique , 2019, Renewable Energy.

[76]  Binghui Si,et al.  An application of Bayesian Network approach for selecting energy efficient HVAC systems , 2019, Journal of Building Engineering.

[77]  Divya T. Vedullapalli,et al.  Combined HVAC and Battery Scheduling for Demand Response in a Building , 2019, IEEE Transactions on Industry Applications.

[78]  Narjes Abbasabadi,et al.  Urban energy use modeling methods and tools: A review and an outlook , 2019, Building and Environment.

[79]  Mumine Gercek,et al.  Energy and environmental performance based decision support process for early design stages of residential buildings under climate change , 2019, Sustainable Cities and Society.

[80]  Wangda Zuo,et al.  A methodology to create prototypical building energy models for existing buildings: A case study on U.S. religious worship buildings , 2019, Energy and Buildings.

[81]  Jan Carmeliet,et al.  Co-simulation and optimization of building geometry and multi-energy systems: Interdependencies in energy supply, energy demand and solar potentials , 2019, Applied Energy.

[82]  Constantine E. Kontokosta,et al.  Rethinking HVAC temperature setpoints in commercial buildings: The potential for zero-cost energy savings and comfort improvement in different climates , 2019, Building and Environment.

[83]  Pardis Pishdad-Bozorgi,et al.  A review of building information modeling (BIM) and the internet of things (IoT) devices integration: Present status and future trends , 2019, Automation in Construction.

[84]  Federico Milano,et al.  Demand response algorithms for smart-grid ready residential buildings using machine learning models , 2019, Applied Energy.

[85]  Miguel Molina-Solana,et al.  A Probabilistic Algorithm for Predictive Control With Full-Complexity Models in Non-Residential Buildings , 2019, IEEE Access.

[86]  Antonio Vicino,et al.  An integrated model predictive control approach for optimal HVAC and energy storage operation in large-scale buildings , 2019, Applied Energy.

[87]  Pierluigi Mancarella,et al.  Building-to-grid flexibility: Modelling and assessment metrics for residential demand response from heat pump aggregations , 2019, Applied Energy.

[88]  Yongjun Sun,et al.  A collaborative control optimization of grid-connected net zero energy buildings for performance improvements at building group level , 2018, Energy.

[89]  Ruijun Chen,et al.  Application of CFD in building performance simulation for airflow analysis and architectural design: a cast study , 2018, 2018 International Conference on Cloud Computing, Big Data and Blockchain (ICCBB).

[90]  F. Nocera,et al.  Daylight Performance of Classrooms in a Mediterranean School Heritage Building , 2018, Sustainability.

[91]  W. Feng,et al.  Scenarios of energy efficiency and CO2 emissions reduction potential in the buildings sector in China to year 2050 , 2018, Nature Energy.

[92]  Daniela Pasini,et al.  Connecting BIM and IoT for addressing user awareness toward energy savings , 2018, Journal of Structural Integrity and Maintenance.

[93]  Peng Xu,et al.  Measures to improve energy demand flexibility in buildings for demand response (DR): A review , 2018, Energy and Buildings.

[94]  Nicolas Réhault,et al.  Energy performance optimization in buildings: A review on semantic interoperability, fault detection, and predictive control , 2018, Applied Physics Reviews.

[95]  Himanshu Sharma,et al.  Modeling and Optimisation of a Solar Energy Harvesting System for Wireless Sensor Network Nodes , 2018, J. Sens. Actuator Networks.

[96]  I-Chen Wu,et al.  A BIM-based visualization and warning system for fire rescue , 2018, Adv. Eng. Informatics.

[97]  Shuqin Chen,et al.  The methodology of standard building selection for residential buildings in hot summer and cold winter zone of China based on architectural typology , 2018, Journal of Building Engineering.

[98]  Juha Jokisalo,et al.  A novel cost-optimizing demand response control for a heat pump heated residential building , 2018 .

[99]  Nikolaos Gatsis,et al.  Occupancy-based buildings-to-grid integration framework for smart and connected communities , 2018, Applied Energy.

[100]  Tianzhen Hong,et al.  Building simulation: Ten challenges , 2018, Building Simulation.

[101]  Muhammad Aslam Uqaili,et al.  Simulation tools application for artificial lighting in buildings , 2018 .

[102]  A. Selvakumar,et al.  Assessment of SVM, empirical and ANN based solar radiation prediction models with most influencing input parameters , 2017, Renewable Energy.

[103]  Facundo Bre,et al.  A computational multi-objective optimization method to improve energy efficiency and thermal comfort in dwellings , 2017 .

[104]  Ilaria Ballarini,et al.  Data analytics for occupancy pattern learning to reduce the energy consumption of HVAC systems in office buildings , 2017 .

[105]  Zhen Liu,et al.  A Systematic Method of Integrating BIM and Sensor Technology for Sustainable Construction Design , 2017 .

[106]  Mahdi Shahbakhti,et al.  Building-to-grid predictive power flow control for demand response and demand flexibility programs , 2017 .

[107]  Sergio Gil-Lopez,et al.  An energy-efficient predictive control for HVAC systems applied to tertiary buildings based on regression techniques , 2017 .

[108]  Talal Rahwan,et al.  Automatic HVAC Control with Real-time Occupancy Recognition and Simulation-guided Model Predictive Control in Low-cost Embedded System , 2017, ArXiv.

[109]  Enrico Benetto,et al.  Global sensitivity analysis as a support for the generation of simplified building stock energy models , 2017 .

[110]  Christopher Tull,et al.  A data-driven predictive model of city-scale energy use in buildings , 2017 .

[111]  Amphawan Julsereewong,et al.  Temperature variation modeling for improving building HVAC control using built-in temperature sensors in smartphones , 2017, 2017 14th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON).

[112]  Gerardo Maria Mauro,et al.  A new comprehensive approach for cost-optimal building design integrated with the multi-objective model predictive control of HVAC systems , 2017 .

[113]  Aurélie Chabaud,et al.  A rule-based strategy to the predictive management of a grid-connected residential building in southern France , 2017 .

[114]  Yitao Liu,et al.  Deep learning based ensemble approach for probabilistic wind power forecasting , 2017 .

[115]  Daniel Uribe,et al.  Optimization of a fixed exterior complex fenestration system considering visual comfort and energy performance criteria , 2017 .

[116]  Jingjing Wang,et al.  Comparison of evaluation standards for green building in China, Britain, United States , 2017 .

[117]  Paul Ruyssevelt,et al.  An exergy-based multi-objective optimisation model for energy retrofit strategies in non-domestic buildings , 2016 .

[118]  Michael D. Sohn,et al.  A regression-based approach to estimating retrofit savings using the Building Performance Database , 2016 .

[119]  Zhiqiang Zhai,et al.  Advances in building simulation and computational techniques: A review between 1987 and 2014 , 2016 .

[120]  Benjamin C. M. Fung,et al.  Advances and challenges in building engineering and data mining applications for energy-efficient communities , 2016 .

[121]  Rasmus Lund Jensen,et al.  Building simulations supporting decision making in early design – A review , 2016 .

[122]  Arun Kumar,et al.  A review on modeling and simulation of building energy systems , 2016 .

[123]  Ian F. C. Smith,et al.  Optimal Sensor Placement for Time-Dependent Systems: Application to Wind Studies around Buildings , 2016, J. Comput. Civ. Eng..

[124]  Kyle Konis,et al.  Passive performance and building form: An optimization framework for early-stage design support , 2016 .

[125]  Yongjun Sun,et al.  A robust demand response control of commercial buildings for smart grid under load prediction uncertainty , 2015 .

[126]  Julien Eynard,et al.  Predictive control of multizone heating, ventilation and air-conditioning systems in non-residential buildings , 2015, Appl. Soft Comput..

[127]  Ertunga C. Özelkan,et al.  Bi-objective optimization of building enclosure design for thermal and lighting performance , 2015 .

[128]  Kristoffer Negendahl,et al.  Building performance simulation in the early design stage: An introduction to integrated dynamic models , 2015 .

[129]  Ccjm Constant Hak,et al.  Acoustic modelling of sports halls, two case studies , 2015 .

[130]  Mohamed Marzouk,et al.  Monitoring thermal comfort in subways using building information modeling , 2014 .

[131]  Paul Raftery,et al.  A review of methods to match building energy simulation models to measured data , 2014 .

[132]  Wenjie Yang,et al.  Performance-driven architectural design and optimization technique from a perspective of architects , 2013 .

[133]  Bijan Samali,et al.  Energy-efficient HVAC systems: Simulationتempirical modelling and gradient optimization , 2013 .

[134]  Paul Cooper,et al.  Existing building retrofits: Methodology and state-of-the-art , 2012 .

[135]  Jan L.M. Hensen,et al.  State of the art in lighting simulation for building science: a literature review , 2012 .

[136]  Marc A. Rosen,et al.  District heating and cooling: Review of technology and potential enhancements , 2012 .

[137]  Stéphane Grieu,et al.  Methodology for the design of energy production and storage systems in buildings: Minimization of the energy impact on the electricity grid , 2012 .

[138]  Bangyin Liu,et al.  Online 24-h solar power forecasting based on weather type classification using artificial neural network , 2011 .

[139]  C. Coimbra,et al.  Forecasting of global and direct solar irradiance using stochastic learning methods, ground experiments and the NWS database , 2011 .

[140]  Balaji Rajagopalan,et al.  Model-predictive control of mixed-mode buildings with rule extraction , 2011 .

[141]  Ala Hasan,et al.  Minimisation of life cycle cost of a detached house using combined simulation and optimisation , 2008 .

[142]  Christophe Cruz,et al.  IFC and building lifecycle management , 2008 .

[143]  F. Haghighat,et al.  Zonal Modeling for Simulating Indoor Environment of Buildings: Review, Recent Developments, and Applications , 2007 .

[144]  Hüsamettin Bulut,et al.  Analysis of variable-base heating and cooling degree-days for Turkey , 2001 .

[145]  Yongbao Chen,et al.  Physical energy and data-driven models in building energy prediction: A review , 2022, Energy Reports.

[146]  A. Palombo,et al.  Assessing energy demands of building stock in railway infrastructures: a novel approach based on bottom-up modelling and dynamic simulation , 2022, Energy Reports.

[147]  Millie Pant,et al.  A Comprehensive Review on NSGA-II for Multi-Objective Combinatorial Optimization Problems , 2021, IEEE Access.

[148]  Tuule Mall Kull,et al.  Gray Box Time Variant Clogging behaviour and Pressure Drop Prediction of the Air Filter in the HVAC System , 2021 .

[149]  Jianming Lian,et al.  Simulation-based performance evaluation of model predictive control for building energy systems , 2021, Applied Energy.

[150]  Mohamed Tabaa,et al.  Towards a Digital Twin model for Building Energy Management: Case of Morocco , 2021, ANT/EDI40.

[151]  Fu Xiao,et al.  Mining big building operational data for improving building energy efficiency: A case study , 2018 .

[152]  Sen Huang,et al.  Improved cooling tower control of legacy chiller plants by optimizing the condenser water set point , 2017 .

[153]  Timur Dogan,et al.  TOWARDS STANDARIZED BUILDING PROPERTIES TEMPLATE FILES FOR EARLY DESIGN ENERGY MODEL GENERATION , 2014 .

[154]  Philippe Rigo,et al.  A review on simulation-based optimization methods applied to building performance analysis , 2014 .

[155]  Inhan Kim,et al.  PROCESS-DRIVEN BIM-BASED OPTIMAL DESIGN USING INTEGRATION OF ENERGYPLUS, GENETIC ALGORITHM, AND PARETO OPTIMALITY , 2011 .

[156]  Sandip Deshmukh,et al.  Modeling of hybrid renewable energy systems , 2008 .