Thermal design of air-conditioned building for tropical climate using admittance method and genetic algorithm

Sahu, Mridu; Bhattacharjee, Bishwajit; Kaushik, S.C.

doi:10.1016/j.enbuild.2012.06.003

Cited by 46 publications

(14 citation statements)

References 9 publications

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“…Sahu et al [117] proposed a strategy consisting to couple electrical analogy model with a genetic algorithm for improving design building parameters to reduce plant load. More specifically, they identified the orientation, the shape, the roof and walls materials and window properties.…”

Section: Examplesmentioning

confidence: 99%

State of the art in building modelling and energy performances prediction: A review

Foucquier

Robert

Suard

et al. 2013

Renewable and Sustainable Energy Reviews

702

338

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In the European Union, the building sector is one of the largest consumer of energy with about 40% of the final energy consumption. Reducing consumption is, at the same time, a sociological, technological and scientific matter. New methods have to be devised in order to support building professionals in their effort to optimize designs and to enhance energy performances. Indeed, the research field related to building modelling and energy performances prediction is very productive, involving various scientific domains. Among them, one can distinguish physics-related fields, focusing on the resolution of equations simulating building thermal behaviour and mathematics-related ones, consisting in the implementation of prediction model thanks to machine learning techniques. This paper proposes a detailed review of these works. First, the approaches based on physical ("white box") models are reviewed according three-category classification. Then, we present the main machine learning ("black box") tools used for prediction of energy consumption, heating/cooling demand, indoor temperature. Eventually, a third approach called hybrid ("grey box") method is introduced, which uses both physical and statistical techniques. The paper covers a wide range of research works, giving the base principles of each technique and numerous illustrative examples.

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Section: Examplesmentioning

confidence: 99%

State of the art in building modelling and energy performances prediction: A review

Foucquier

Robert

Suard

et al. 2013

Renewable and Sustainable Energy Reviews

702

338

View full text Add to dashboard Cite

show abstract

“…The probability of an individual to be selected for next generation depends on its fitness value and the selection process. Fitness proportionate selection, also known as roulette wheel selection, is a commonly used selection technique that involves the following steps (Sahu et al 2012): (1) Normalization of each individual's fitness value; (2) Sorting of the population by descending fitness values; (3) Computation of the accumulated normalized fitness values; (4) Generation of a random number between 0 and 1; and (5) Selection of the first individual whose accumulated normalized value is greater than the generated random number.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Computational intelligence techniques for HVAC systems: A review

et al. 2016

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Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO 2 emissions. The drive to reduce energy use and associated greenhouse gas emissions from buildings has acted as a catalyst in the development of advanced computational methods for energy efficient design, management and control of buildings and systems. Heating, ventilation and air-conditioning (HVAC) systems are the major source of energy consumption in buildings and ideal candidates for substantial reductions in energy demand. Significant advances have been made in the past decades on the application of computational intelligence (CI) techniques for HVAC design, control, management, optimization, and fault detection and diagnosis. This article presents a comprehensive and critical review on the theory and applications of CI techniques for prediction, optimization, control and diagnosis of HVAC systems. The analysis of trends reveals that the minimisation of energy consumption was the key optimization objective in the reviewed research, closely followed by the optimization of thermal comfort, indoor air quality and occupant preferences.Hardcoded Matlab program was the most widely used simulation tool, followed by TRNSYS, EnergyPlus, DOE-2, HVACSim+ and ESP-r. Metaheuristic algorithms were the preferred CI method for solving HVAC related problems and in particular genetic algorithms were applied in most of the studies. Despite the low number of studies focussing on multi-agent systems (MAS), as compared to the other CI techniques, interest in the technique is increasing due to their ability of dividing and conquering an HVAC optimization problem with enhanced overall performance. The paper also identifies prospective future advancements and research directions.

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“…It manipulates not just one potential solution of a problem but also a collection of potential solutions. Each potential solution is called individual (Sahu et al 2012). The usual method of applying a GA to a real-parameter problem is to encode each parameter as a bit string.…”

Section: Gamentioning

confidence: 99%

Inverse design of aircraft cabin environment by coupling artificial neural network and genetic algorithm

Zhang

You

2014

HVAC&R Research

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An inverse design method is proposed to achieve the pre-set control objectives of the aircraft cabin environment. The method combines the artificial neural network and the genetic algorithm, and the training and testing data of the artificial neural network is obtained by computational fluid dynamics analysis. Both of the thermal comfort and energy consumption are considered in the inverse design. The artificial neural network is used to identify the relationship between the thermal comfort and the air supply parameters (inlet velocity magnitude and angle, inlet air temperature). The genetic algorithm coupled with the well-trained artificial neural network is used to design the aircraft cabin environment. Numerical results show that the Bayesian regularization algorithm is proved to have better generalization capability than the other training algorithms for the artificial neural network. The increase of training data quantity improves the generalization capability of the artificial neural network, while it spends more simulation time. A computational fluid dynamics database with 60 datasets is shown to be suitable to the present inverse design, and the testing error of the artificial neural network is below 8.2%. Several groups of optimal air supply parameters are found with different trade-offs between the thermal comfort and energy consumption. The best solution of thermal comfort, i.e., the percentage of zone with |PMV | >0.5 in all cabin control domains, is less than 7.8%.

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Thermal design of air-conditioned building for tropical climate using admittance method and genetic algorithm

Cited by 46 publications

References 9 publications

State of the art in building modelling and energy performances prediction: A review

State of the art in building modelling and energy performances prediction: A review

Computational intelligence techniques for HVAC systems: A review

Inverse design of aircraft cabin environment by coupling artificial neural network and genetic algorithm

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