Multiobjective optimization of building energy consumption and thermal comfort based on integrated BIM framework with machine learning-NSGA II

Energy and Buildings, Год журнала: 2022, Номер 277, С. 112479 - 112479

Опубликована: Окт. 12, 2022

Detailed parametric analysis and measurements are required to reduce building energy usage while maintaining acceptable thermal conditions. This research suggested a system that combines Building Information Modeling (BIM), machine learning, the non-dominated sorting genetic algorithm-II (NSGA II) investigate impact of factors on find optimal design. A plugin is developed receive sensor data export all necessary information from BIM MSSQL Excel. The model was imported IDA Indoor Climate Energy (IDA ICE) execute an consumption simulation then pairwise test produce sample set. To study set develop prediction between usage, 11 learning algorithms used. best algorithm Group Least Square Support Vector Machine (GLSSVM), later employed in NSGA II as fitness function using Dynamo software. An multi-objective optimization designed optimize interior comfort (measured by predicted percentage dissatisfied (PPD)). Pareto front calculated, optimum point approach used combination envelope characteristics, HVAC setpoints, shading parameters, lighting, air infiltration. feasibility effectiveness framework demonstrated case upper secondary school Norway; results show that: (1) GLSSVM has unique capacity forecast use with high accuracy: R2 0.99, RMSE 1.2, MSE 1.44, MAE 0.89; (2) may be successfully improved GLSSVM-NSGA hybrid technique, which reduces 37.5% increases 33.5%, respectively.

Язык: Английский

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Multi-objective optimization of residential building energy consumption, daylighting, and thermal comfort based on BO-XGBoost-NSGA-II

Building and Environment, Год журнала: 2024, Номер 254, С. 111386 - 111386

Опубликована: Март 7, 2024

Язык: Английский

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A multi-objective optimization strategy for building carbon emission from the whole life cycle perspective

Energy, Год журнала: 2022, Номер 262, С. 125373 - 125373

Опубликована: Сен. 10, 2022

Язык: Английский

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Digital Twin of HVAC system (HVACDT) for multiobjective optimization of energy consumption and thermal comfort based on BIM framework with ANN-MOGA

Advances in Building Energy Research, Год журнала: 2022, Номер 17(2), С. 125 - 171

Опубликована: Окт. 26, 2022

This study proposes a novel Digital Twin framework of heating, ventilation, and air conditioning (HVACDT) system to reduce energy consumption while increasing thermal comfort. The is developed help the facility managers better understand building operation enhance HVAC function. based on Building Information Modelling (BIM) combined with newly created plug-in receive real-time sensor data as well comfort optimization process through Matlab programming. In order determine if suggested practical, were collected from Norwegian office between August 2019 October 2021 used test framework. An artificial neural network (ANN) in Simulink model multiobjective genetic algorithm (MOGA) are then improve system. comprised distributors, cooling units, heating pressure regulators, valves, gates, fans, among other components. this context, several characteristics, such temperatures, pressure, airflow, control, factors considered decision variables. objective functions, predicted percentage dissatisfied (PPD) usage both calculated. As result, ANN's variables function correlated well. Furthermore, MOGA presents different design that can be obtain best possible solution terms usage. results show average savings for four days summer roughly 13.2%, 10.8% three months (June, July, August), keeping PPD under 10%. Finally, compared traditional approaches, HVACDT displays higher level automation management.

Язык: Английский

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Data-driven prediction and optimization of residential building performance in Singapore considering the impact of climate change

Building and Environment, Год журнала: 2022, Номер 226, С. 109735 - 109735

Опубликована: Окт. 28, 2022

Язык: Английский

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Multi-objective optimization of the solar orientation of two residential multifamily buildings in south Brazil

Energy and Buildings, Год журнала: 2023, Номер 285, С. 112838 - 112838

Опубликована: Фев. 8, 2023

The shape and orientation of a building influence the energy demand, therefore optimal decisions should only be made rigorously supported by evaluation programs, which allow for measuring demand more precisely. main purpose this research is to evaluate massive residential social housing multifamily buildings find best solar positioning minimize cooling heating demands simultaneously in bioclimatic zone 2 (Cfa) southern region Brazil. To do this, study utilizes multi-objective optimization with genetic algorithm (NSGA-II) simulating thermal behavior EnergyPlus performing Python language programming code, totalizing 80,000 simulations. results showed that could reduce total 4% "H" 22% linear isolated scenario. For condominium condition, reduction 2% typology 8% shape. presented can help engineers architects design energy-efficient address energetic vulnerability same building. Moreover, future work carried out improve constructive pattern replicated all over country, improving surroundings.

Язык: Английский

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Multi-objective architecture for strategic integration of distributed energy resources and battery storage system in microgrids

Journal of Energy Storage, Год журнала: 2023, Номер 72, С. 108276 - 108276

Опубликована: Июль 20, 2023

Язык: Английский

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Multi-objective optimization designs of phase change material-enhanced building using the integration of the Stacking model and NSGA-III algorithm

Journal of Energy Storage, Год журнала: 2023, Номер 68, С. 107807 - 107807

Опубликована: Июнь 1, 2023

Язык: Английский

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Coordinated optimization design of buildings and regional integrated energy systems based on load prediction in future climate conditions

Applied Thermal Engineering, Год журнала: 2024, Номер 241, С. 122338 - 122338

Опубликована: Янв. 6, 2024

Язык: Английский

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The artificial intelligence reformation of sustainable building design approach: A systematic review on building design optimization methods using surrogate models

Energy and Buildings, Год журнала: 2024, Номер unknown, С. 114769 - 114769

Опубликована: Сен. 1, 2024

Язык: Английский

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