Multi-objective optimization of building energy performance and indoor thermal comfort by combining artificial neural networks and metaheuristic algorithms

Chegari, Badr; Tabaa, Mohamed; Simeu, Emmanuel; Moutaouakkil, Fouad; Medromi, Hicham

doi:10.1016/j.enbuild.2021.110839

Cited by 119 publications

(44 citation statements)

References 53 publications

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“…The simulation-based optimization was popular in previous studies reported in the literature due to the complexity of parameter interactions. Simulation tools, which are generally employed to evaluate the optimization results, provide efficient resolutions to define the thermodynamic interaction process with dynamic indoor and outdoor environment [25]. The most commonly used software, such as DOE-2, EnergyPlus TM , DeST, and TRNSYS, has been well-developed for both commercial and academic use [26].…”

Section: Literature Review 21 Methods For Multi-objective Building Designmentioning

confidence: 99%

A GP-Based Hierarchical Objectives Decision-Making Method for Building Energy Efficiency Optimization

Liu

Sun

2022

Buildings

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Building energy efficiency, which is critical in reducing environmental impact, has become one of the most important objectives of building designs. In order to precisely express the goals of building designs, and help decision makers estimate the ultimate performance of design schemes in advance when searching for the optimal building design, the Goal Programming Model (GPM) is introduced in this study to provide a solution for explicit design objective delivery and multi-stakeholder involved decision-making support. In this proposed method, EnergyPlusTM works as a simulation engine to search for the relationship between design parameter combinations and building energy consumption. Simultaneously, Genetic Algorithm (GA) is used to improve the efficiency of overall building energy performance optimization by processing multiple iterations. A case study with five possible design scenarios was dedicated in this study to implement the proposed optimization method, and the optimization results verified the capacity of the established GP-based optimization method to satisfy various design requirements for decision makers and/or stakeholders, especially in facing the hierarchical objectives with different priorities. In this case, the envelope-related variables, including the exterior wall and window, serve as optimization objectives. The optimization is carried out under the ideal air conditioning system, considering different energy usage patterns. Meanwhile, comparing with the vague and restricted expression of objectives in multi-objective optimization, the proposed GP-based optimization method provides explicit trade-off relationships among various objectives for designers, which improves the practical value of the optimized designs, so as to ensure the project success and facilitate the development of green buildings.

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Section: Literature Review 21 Methods For Multi-objective Building Designmentioning

confidence: 99%

A GP-Based Hierarchical Objectives Decision-Making Method for Building Energy Efficiency Optimization

Liu

Sun

2022

Buildings

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“…They summarized that the outdoor climate directly affects indoor thermal comfort, and its factors should be employed to control occupants' satisfaction. Chegari et al [25] found that outdoor environmental factors and indoor thermal comforts are highly associated. They analyzed outdoor environments related to building design, such as opaque and glazed walls, shading devices, and thermal ventilation.…”

Section: Related Work Based On Iot and MLmentioning

confidence: 99%

Design and Development of Internet of Things-Driven Fault Detection of Indoor Thermal Comfort: HVAC System Problems Case Study

Sahoh

Kliangkhlao

Kittiphattanabawon

2022

Sensors

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Controlling thermal comfort in the indoor environment demands research because it is fundamental to indicating occupants’ health, wellbeing, and performance in working productivity. A suitable thermal comfort must monitor and balance complex factors from heating, ventilation, air-conditioning systems (HVAC Systems) and outdoor and indoor environments based on advanced technology. It needs engineers and technicians to observe relevant factors on a physical site and to detect problems using their experience to fix them early and prevent them from worsening. However, it is a labor-intensive and time-consuming task, while experts are short on diagnosing and producing proactive plans and actions. This research addresses the limitations by proposing a new Internet of Things (IoT)-driven fault detection system for indoor thermal comfort. We focus on the well-known problem caused by an HVAC system that cannot transfer heat from the indoor to outdoor and needs engineers to diagnose such concerns. The IoT device is developed to observe perceptual information from the physical site as a system input. The prior knowledge from existing research and experts is encoded to help systems detect problems in the manner of human-like intelligence. Three standard categories of machine learning (ML) based on geometry, probability, and logical expression are applied to the system for learning HVAC system problems. The results report that the MLs could improve overall performance based on prior knowledge around 10% compared to perceptual information. Well-designed IoT devices with prior knowledge reduced false positives and false negatives in the predictive process that aids the system to reach satisfactory performance.

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“…Many previous studies tried to develop multi-objective tools which are able to improve simultaneously the energy (cooling and electricity demand), comfort (thermoigrometric, visual and acoustic), environmental (considering all phases of an LCA) and economic (costs and return time) aspects [42][43][44]. As for example, D'Amico et al [42] proposed a simple and reliable tool which simultaneously solves the energy and environmental balance of buildings.…”

Section: State Of the Art In Artificial Neural Network And Fuzzy Logic Toolsmentioning

confidence: 99%

“…The fuzzy logic system was used to control the execution routine of the CFD process and to reduce the computational cost by up to 40. In another study [43], a building performance optimization technique was well applied in order to design energy-efficient buildings, i.e., a residential building located in the Marrakech region, Morocco. The aim was to minimize its energy demand, especially for heating and cooling, as well as to maximize the indoor thermal comfort of the two most important targets for building designers.…”

Section: State Of the Art In Artificial Neural Network And Fuzzy Logic Toolsmentioning

confidence: 99%

Development of a Decisional Procedure Based on Fuzzy Logic for the Energy Retrofitting of Buildings

et al. 2021

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This paper concerns the development of an automatic tool, based on Fuzzy Logic, which is able to identify the proper solutions for the energy retrofitting of existing buildings. Regarding winter heating, opaque and glazing surfaces are considered in order to reduce building heat dispersions. Starting from energy diagnosis, it is possible to formulate retrofitting proposals and to evaluate the effectiveness of the intervention considering several aspects (energy savings, costs, intervention typology). The innovation of this work is represented by the application of a fuzzy logic expert system to obtain an indication about the proper interventions for building energy retrofitting, providing as inputs only few parameters, with a strong reduction in time and effort with respect to the software tools and methodologies currently applied by experts. The novelty of the paper is the easy handling properties of the developed tool, which requires only a few data about the buildings: not many such methods were developed in the last years. The energy requirements for winter heating before and after particular interventions were evaluated for a consistent set of buildings in order to produce the required knowledge base for the tool’s development. The identified appropriate inputs and outputs, their domains of discretization, the membership functions associated to each fuzzy set, and the linguistic rules were deduced on the basis of the knowledge determined in this was. Therefore, the system was successfully validated with reference to further buildings characterized by different design and architecture features, showing a good agreement with the intervention opportunities evaluated.

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Multi-objective optimization of building energy performance and indoor thermal comfort by combining artificial neural networks and metaheuristic algorithms

Cited by 119 publications

References 53 publications

A GP-Based Hierarchical Objectives Decision-Making Method for Building Energy Efficiency Optimization

A GP-Based Hierarchical Objectives Decision-Making Method for Building Energy Efficiency Optimization

Design and Development of Internet of Things-Driven Fault Detection of Indoor Thermal Comfort: HVAC System Problems Case Study

Development of a Decisional Procedure Based on Fuzzy Logic for the Energy Retrofitting of Buildings

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