Research on the identification of temperature in intelligent building based on feed forward neural network and particle swarm optimization algorithm

Chen, Liang; Fang, Qiansheng; Zhang, Zhen-ya

doi:10.1109/icnc.2010.5584480

Cited by 8 publications

(5 citation statements)

References 4 publications

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“…If direct coupling between TRNSYS and GA was used, it would need 10 year to finish the task [27]. Chen et al [26] used a feed forwards neural network with one hidden layer for the identification of temperature in intelligent buildings and then optimize by the PSO. Eisenhower et al [23] used the Support Vector Machines method to generate several metamodels of a 30-zone EnergyPlus building model and then performed sensitivity analysis to select the most influential variables for optimization.…”

Section: Optimization Of Computationally Expensive Modelsmentioning

confidence: 99%

A review on simulation-based optimization methods applied to building performance analysis

2014

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Recent progress in computer science and stringent requirements of the design of "greener" buildings put forwards the research and applications of simulation-based optimization methods in the building sector. This paper provides an overview on this subject, aiming at clarifying recent advances and outlining potential challenges and obstacles in building design optimization. Key discussions are focused on handling discontinuous multi-modal building optimization problems, the performance and selection of optimization algorithms, multi-objective optimization, the application of surrogate models, optimization under uncertainty and the propagation of optimization techniques into real-world design challenges. This paper also gives bibliographic information on the issues of simulation programs, optimization tools, efficiency of optimization methods, and trends in optimization studies. The review indicates that future researches should be oriented towards improving the efficiency of search techniques and approximation methods (surrogate models) for largescale building optimization problems; and reducing time and effort for such activities. Further effort is also required to quantify the robustness in optimal solutions so as to improve building performance stability.

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Section: Optimization Of Computationally Expensive Modelsmentioning

confidence: 99%

A review on simulation-based optimization methods applied to building performance analysis

2014

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“…Neural networks have been widely used for function approximation [29,53] and RBF N is one of the possible strategies [33]. Micchelli [42] in an early work proposed a mathematical formulation of RBF N .…”

Section: Radial Basis Function Network (Rbfn)mentioning

confidence: 99%

On the performance of meta-models in building design optimization

2018

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“…Among other AI techniques frequently discussed in the literature came Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). While Wang et al (2010) using PSO to optimize building cooling, heating and power consumption, Chen et al (2010) presented a combination of PSO and feed-forward ANN for temperature identification in smart buildings [27,28]. In 2012, on the other hand, Yuan et al developed an ACO module to optimize building energy performance [29].…”

Section: Introduction 11 General Backgroundmentioning

confidence: 99%

Sustainable Design Optimization Tool for Prototypical Buildings in Hot Arid Climate Using Analytical Hierarchy Process

Mahmoud

Dorra

Nassar

et al. 2021

Journal of Al-Azhar University Engineering Sector

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New Building Information Modelling (BIM)enabled performance metrics are gaining importance in Building Performance Simulation (BPS). However, scarce work was published featuring holistic metrics for prototypical designs in hot arid climate. Thus, this research presents a BIM-enabled sustainable early-design decision support tool, whose optimization variables are building orientation and Window-to-Wall Ratio (WWR). The mode's objective is reaching optimum building Sustainability Combined Score (SCS); the holistic value of performance parameters considered by the user. To demonstrate the methodology, the daylighting and thermal comfort metrics are considered: (1) Day-Lit Area (DLA); (2) Daylight Autonomy (DA); (3) Mean Daylight Factor (µDF); (4) Useful Daylight Index (UDI); (5) Temperature Discomfort Hours (TDH); and (6) Humidity Discomfort Hours (HDH). BIM simulation is carried out for various combinations of building orientations and WWR, metrics are converted into percentages then into criteria scores based upon expert-determined criteria rating scales. Next, using the Analytical Hierarchy Process (AHP), Criteria Relative Weights (CRW's) are determined, using a Pairwise Comparison Matrix (PCM). SCS's are computed for each scenario, as a function of criteria scores and CRW's. The verification of the AHP Module is done arithmetically using consistency indices. After screening all valid scenarios considering site constraints, accessibility and design codes, the scenario with optimum SCS is obtained. Three cases of prototypical designs, comprising a school building, a civil defense facility and a congregational building are presented. All cases are located in the hot desert climate of Qatar. The validation of the SCS Module output is carried out using expert survey.

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Research on the identification of temperature in intelligent building based on feed forward neural network and particle swarm optimization algorithm

Cited by 8 publications

References 4 publications

A review on simulation-based optimization methods applied to building performance analysis

A review on simulation-based optimization methods applied to building performance analysis

On the performance of meta-models in building design optimization

Sustainable Design Optimization Tool for Prototypical Buildings in Hot Arid Climate Using Analytical Hierarchy Process

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