Energy calibration of HVAC sub-system model using sensitivity analysis and meta-heuristic optimization

Martin, Gilbert Larochelle; Monfet, Danielle; Nouanegue, Herve Frank; Lavigne, Karine; Sansregret, Simon

doi:10.1016/j.enbuild.2019.109382

Cited by 10 publications

(2 citation statements)

References 28 publications

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“…Such algorithms have been extensively employed by energy experts for fine-tuning the performance of HVAC systems [42,43]. Furthermore, metaheuristic techniques can assist in the training of more accurate ML models to mitigate the computational drawbacks associated with ML algorithms.…”

Section: Introductionmentioning

confidence: 99%

New Heuristic Methods for Sustainable Energy Performance Analysis of HVAC Systems

et al. 2022

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Energy-efficient buildings have attracted vast attention as a key component of sustainable development. Thermal load analysis is a pivotal step for the proper design of heating, ventilation, and air conditioning (HVAC) systems for increasing thermal comfort in energy-efficient buildings. In this work, novel a methodology is proposed to predict the cooling load (LC) of residential buildings based on their geometrical characteristics. Multi-layer perceptron (MLP) neural network was coupled with metaheuristic algorithms to attain its optimum hyperparameter values. According to the results, the LC pattern can be promisingly captured and predicted by all developed hybrid models. Nevertheless, the comparison analysis revealed that the electrostatic discharge algorithm (ESDA) achieved the most powerful MLP model. Hence, utilizing the proposed methodology would give new insights into the thermal load analysis method and bridge the existing gap between the most recently developed computational intelligence techniques and energy performance analysis in the sustainable design of energy-efficient residential buildings.

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Section: Introductionmentioning

confidence: 99%

New Heuristic Methods for Sustainable Energy Performance Analysis of HVAC Systems

et al. 2022

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“…Katebi et al [43] attained the optimal condition of a wavelet-based linear quadratic regulator using metaheuristic methods. Martin et al [44] used a metaheuristic technique along with sensitivity analysis for parameter adjustment in order to calibrate the HVAC sub-system component. Likewise, Bamdad Masouleh [45] employed two types of ant colony optimization (for continuous and mixed variables) for building energy optimization.…”

Section: Introductionmentioning

confidence: 99%

A TLBO-Tuned Neural Processor for Predicting Heating Load in Residential Buildings

et al. 2022

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Recent studies have witnessed remarkable merits of metaheuristic algorithms in optimization problems. Due to the significance of the early analysis of the thermal load in energy-efficient buildings, this work introduces and compares four novel optimizer techniques—the firefly algorithm (FA), optics-inspired optimization (OIO), shuffled complex evolution (SCE), and teaching–learning-based optimization (TLBO)—for an accurate prediction of the heating load (HL). The models are applied to a multilayer perceptron (MLP) neural network to surmount its computational shortcomings. The models are fed by a literature-based dataset obtained for residential buildings. The results revealed that all models used are capable of properly analyzing and predicting the HL pattern. A comparison between them, however, showed that the TLBO-MLP with the coefficients of determination 0.9610 vs. 0.9438, 0.9373, and 0.9556 (respectively, for FA-MLP, OIO-MLP, and SCE-MLP) and the root mean square error of 2.1103 vs. 2.5456, 2.7099, and 2.2774 presents the most reliable approximation of the HL. It also surpassed several methods used in previous studies. Thus, the developed TLBO-MLP can be a beneficial model for subsequent practical applications.

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Two neural-metaheuristic techniques based on vortex search and backtracking search algorithms for predicting the heating load of residential buildings

Foong

Lyu

2020

Engineering with Computers

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Energy calibration of HVAC sub-system model using sensitivity analysis and meta-heuristic optimization

Cited by 10 publications

References 28 publications

New Heuristic Methods for Sustainable Energy Performance Analysis of HVAC Systems

New Heuristic Methods for Sustainable Energy Performance Analysis of HVAC Systems

A TLBO-Tuned Neural Processor for Predicting Heating Load in Residential Buildings

Two neural-metaheuristic techniques based on vortex search and backtracking search algorithms for predicting the heating load of residential buildings

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