Abatement of the Increases in Cooling Energy Use during a Period of Intense Heat by a Network-Based Adaptive Controller

Ahn, Jonghoon

doi:10.3390/su13031353

Cited by 5 publications

(4 citation statements)

References 29 publications

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“…It can be assumed that these relatively small differences in energy consumed were derived from its precise control by optimizing the overshooting value generated when the Trm reached the Tset. In the simulation tests conducted with similar methodologies and models, thermal comfort and energy use show the control efficiency by 6~14% and 2~39%, respectively [14,17,31]. However, since this model tests an adaptive model using additional modules to maintain the quality of comfort in existing control systems, the energy use savings of about 5% can be sufficiently significant.…”

Section: Heating Energy By the Control Modelsmentioning

confidence: 93%

“…As a principle, the ANN algorithm includes a large class of several structures, and the optimized selections of a nonlinear mapping function x with a network are required as described in Fig. 2 [30,31]. In this research, the inner structure in function approximation consists of two input layers, 1ten hidden layers, and one output layer for the amount of air and its temperature, respectively.…”

Section: Control Modelsmentioning

confidence: 99%

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Building Space Thermal Control Model Responding to Sharp Changes in Outdoor Temperature

2023

Teh. vjesn.

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As computing and data-driven technologies have improved, the precision of the building thermal control models has been gradually improved, but the use of energy resources to operate them has been also increased. It is imperative to investigate the optimized point of energy use and human comfort for their thermal control strategies. The aim of this research is to find an energy-efficient thermal control model to maintain the constancy of thermal comfort and suppress the increase of energy use in association with precise environmental controls. Based on a cooling and heating air supply model in a simplified building model, a comprehensive energy use pattern is confirmed by adding an adaptive control model that allows indoor thermal comfort to be maintained at a setting level. The adaptive control model utilizing the artificial neural network and the adjustment process of initial settings is proposed to examine its performance in controlling the amount of thermal supply air and its temperature. For the clear comparison between a baseline model and a proposed model, the statistical indices of each thermal dissatisfaction value and the weekly heating energy use are utilized. The results of this research show that the thermal dissatisfaction fluctuation is alleviated by about 22.0~41.0% and the energy efficiency is improved by about 5.1%, respectively. The results provide the effectiveness of the proposed model which can improve both the energy use and thermal comfort in a building space. This advantage can help old thermal systems to improve their usability without replacing any major components.

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Section: Heating Energy By the Control Modelsmentioning

confidence: 93%

Section: Control Modelsmentioning

confidence: 99%

Building Space Thermal Control Model Responding to Sharp Changes in Outdoor Temperature

2023

Teh. vjesn.

View full text Add to dashboard Cite

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“…Then, the algorithm interpreted the signals to turn off for the amount of air and to increase the air temperature; thus, by these two control outputs, the ANN model was trained. As a principle, the ANN algorithm includes a large class of several structures, and the optimized selections of a nonlinear mapping function x with a network are required [30,31]. In this research, the ANN algorithm in function approximation was a multi-layer perceptron, which consisted of 2 input layers, 10 hidden layers, and an output layer for the amount of air and its temperature, respectively.…”

Section: Control Rulementioning

confidence: 99%

A Network-Based Strategy to Increase the Sustainability of Building Supply Air Systems Responding to Unexpected Temperature Patterns

Ahn

2022

Sustainability

Self Cite

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As real-time indoor thermal data became available, the precision of the building thermal control systems has improved, but the use of resources has also increased. Therefore, it is imperative to examine the optimized point of energy use and thermal dissatisfaction for their efficient control. The aim of this research is to find an energy-efficient thermal control strategy to suppress the increase in thermal dissatisfaction. An adaptive control model utilizing the artificial neural network and the adjustment process of initial settings is proposed to examine its performance in controlling thermal supply air in terms of indoor thermal dissatisfaction and energy use. For a clear comparison, the standard deviation of each thermal dissatisfaction value and the weekly heating energy transfer are used. The proposed model successfully performs in reducing the indoor thermal dissatisfaction level and energy use. In comparison with two deterministic models, the performance is improved in terms of the constancy of suppressing thermal dissatisfaction levels by 72.1% and the improvement in energy efficiency by 18.8%, respectively. The significance of this study Is that it is possible to improve control precision by adding only a few modules without replacing the entire existing system, and that the model’s sustainability is increased by reducing the possibility of hardware and software retrofitting in the future.

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“…The combination of machine learning, fuzzy control, and evolutionary algorithms culminated in the so-called Computational Intelligence (CI), which is currently being used in architecture. Some sophisticated control methods have integrated fuzzy adaptive control [12][13][14][15], optimum comfort control [16], and minimum-power comfort control [17] to address the nonlinear characteristic of comfort parameters, computation, time delay, and system uncertainty. A backpropagation algorithm-based direct neural network controller has been developed and effectively used in hydronic heating systems [18].…”

Section: Related Workmentioning

confidence: 99%

Indoor Air Quality Control Using Backpropagated Neural Networks

Uskenbayeva¹,

Altayeva²,

Gusmanova³

et al. 2022

Computers, Materials &Amp; Continua

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Providing comfortable indoor air quality control in residential construction is an exceedingly important issue. This is due to the structure of the fast response controller of air quality. The presented work shows the breakdown and creation of a mathematical model for an interactive, nonlinear system for the required comfortable air quality. Furthermore, the paper refers to designing traditional proportional integral derivative regulators and proportional, integral, derivative regulators with independent parameters based on a backpropagation neural network. In the end, we perform the experimental outputs of a suggested backpropagation neural network-based proportional, integral, derivative controller and analyze model results by applying the proposed system. The obtained results demonstrated that the proposed controller can provide the required level of clean air in the room. The proposed developed model takes into consideration international Heating, Refrigerating, and air conditioning standards as ASHRAE AND ISO. Based on the findings, we concluded that it is possible to implement a proposed system in homes and offer equivalent indoor air quality with continuous mechanical ventilation without a profuse amount of energy.

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Abatement of the Increases in Cooling Energy Use during a Period of Intense Heat by a Network-Based Adaptive Controller

Cited by 5 publications

References 29 publications

Building Space Thermal Control Model Responding to Sharp Changes in Outdoor Temperature

Building Space Thermal Control Model Responding to Sharp Changes in Outdoor Temperature

A Network-Based Strategy to Increase the Sustainability of Building Supply Air Systems Responding to Unexpected Temperature Patterns

Indoor Air Quality Control Using Backpropagated Neural Networks

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