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Enhancing Thermal Comfort in Commerc...

Arida, Maya Ahmad,

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Enhancing Thermal Comfort in Commercial Buildings: Contributions of Machine Learning Algorithms for Optimizing Predictive Thermal Comfort Indices /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Enhancing Thermal Comfort in Commercial Buildings: Contributions of Machine Learning Algorithms for Optimizing Predictive Thermal Comfort Indices // Maya Ahmad Arida.
作者:	Arida, Maya Ahmad,
面頁冊數:	1 electronic resource (190 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-02, Section: B.
Contained By:	Dissertations Abstracts International86-02B.
標題:	Computer science. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31144170
ISBN:	9798383700013

Enhancing Thermal Comfort in Commercial Buildings: Contributions of Machine Learning Algorithms for Optimizing Predictive Thermal Comfort Indices /
Arida, Maya Ahmad,

Enhancing Thermal Comfort in Commercial Buildings: Contributions of Machine Learning Algorithms for Optimizing Predictive Thermal Comfort Indices /Maya Ahmad Arida. - 1 electronic resource (190 pages)

Source: Dissertations Abstracts International, Volume: 86-02, Section: B.

In recent years, there has been a dramatic increase in the number of energy-efficient structures built in the United States. Optimizing thermal comfort and energy efficiency represents a critical challenge and opportunity within the field of energy-efficient building design. Modern control systems based on thermal comfort models, such as the Predicted Mean Vote (PMV), offer a promising avenue for improving occupant comfort while simultaneously reducing energy consumption. Leveraging machine learning for predictive modeling further advances this field, enabling more precise control and forecasting of indoor environmental conditions. Focusing on these innovations, this research examines the efficacy of PMV-based comfort controls compared to traditional thermostatic controls in commercial buildings through detailed energy modeling. A sensitivity analysis, grounded in the Design of Experiments methodology and augmented by real-world energy consumption data, assesses how variations in building envelope characteristics influence the balance between indoor thermal comfort and energy efficiency. The findings reveal that PMV-based systems significantly improve this balance, outshining conventional temperature-based controls under a variety of conditions.Furthermore, the investigation extends to the predictive modeling of thermal comfort indices, specifically PMV and PPD, employing advanced machine learning techniques such as Artificial Neural Networks (ANN) and Support Vector Machines (SVM). Through the careful collection and preprocessing of data on environmental variables, clothing insulation, metabolic rates, and personal preferences, this research fine-tunes the predictive accuracy of these models. The comparative analysis underscores the superior performance of ANN and SVM in forecasting thermal comfort, highlighting the transformative potential of machine learning in the domain of building environmental comfort.Contributing to both theoretical and practical dimensions, this research explains the advantages of integrating comfort-based control systems with machine learning predictive models. It supports a concept shift towards more sustainable and occupant-centric building management practices, offering valuable insights for the design of energy-efficient, responsive indoor environments. Through this dual approach, the work sets a new benchmark for achieving optimal thermal comfort and in the built environment.

English

ISBN: 9798383700013Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Thermal comfort

Enhancing Thermal Comfort in Commercial Buildings: Contributions of Machine Learning Algorithms for Optimizing Predictive Thermal Comfort Indices /
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520 $a In recent years, there has been a dramatic increase in the number of energy-efficient structures built in the United States. Optimizing thermal comfort and energy efficiency represents a critical challenge and opportunity within the field of energy-efficient building design. Modern control systems based on thermal comfort models, such as the Predicted Mean Vote (PMV), offer a promising avenue for improving occupant comfort while simultaneously reducing energy consumption. Leveraging machine learning for predictive modeling further advances this field, enabling more precise control and forecasting of indoor environmental conditions. Focusing on these innovations, this research examines the efficacy of PMV-based comfort controls compared to traditional thermostatic controls in commercial buildings through detailed energy modeling. A sensitivity analysis, grounded in the Design of Experiments methodology and augmented by real-world energy consumption data, assesses how variations in building envelope characteristics influence the balance between indoor thermal comfort and energy efficiency. The findings reveal that PMV-based systems significantly improve this balance, outshining conventional temperature-based controls under a variety of conditions.Furthermore, the investigation extends to the predictive modeling of thermal comfort indices, specifically PMV and PPD, employing advanced machine learning techniques such as Artificial Neural Networks (ANN) and Support Vector Machines (SVM). Through the careful collection and preprocessing of data on environmental variables, clothing insulation, metabolic rates, and personal preferences, this research fine-tunes the predictive accuracy of these models. The comparative analysis underscores the superior performance of ANN and SVM in forecasting thermal comfort, highlighting the transformative potential of machine learning in the domain of building environmental comfort.Contributing to both theoretical and practical dimensions, this research explains the advantages of integrating comfort-based control systems with machine learning predictive models. It supports a concept shift towards more sustainable and occupant-centric building management practices, offering valuable insights for the design of energy-efficient, responsive indoor environments. Through this dual approach, the work sets a new benchmark for achieving optimal thermal comfort and in the built environment.
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653 $a Machine learning
653 $a Energy efficiency
653 $a Support Vector Machines
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