Sat, May 17, 2025
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School of Engineering & Technology, Duy Tan University
Abstract: (220 Views)
Comprehensive occupancy data from buildings may help energy management systems work better and save energy without sacrificing tenant comfort. Occupancy data is obtained by various methods, including high-precision instruments like thermal and optical cameras and specialized or environmental sensors like carbon dioxide (CO2) and passive infrared (PIR). While less accurate, the latter methods have drawn much interest since they are less expensive and invasive. This study's primary goal is to provide a unique prediction model that enhances the knowledge gleaned from sensor data by fusing evolutionary algorithms with machine learning (ML) models. To detect occupancy in an office setting, this study examines the effectiveness of four optimization algorithms: Multi-Verse Optimization (MVO), Teaching-Learning-Based Optimization (TLBO), Wind-Driven Optimization (WDO), and Whale Optimization Algorithm (WOA) when combined with Multilayer Perceptron (MLP). Among the noteworthy results is the strong performance of WDO-MLP, which shows remarkable AUC scores with little sensitivity to changes in swarm size. Furthermore, the best-performing approach is MVO combined with MLP, which shows better stability and discriminating ability at different swarm sizes. The practical implications of the research findings for implementing occupancy detection systems in smart buildings are noteworthy.
Comprehensive occupancy data from buildings may help energy management systems work better and save energy without sacrificing tenant comfort. Occupancy data is obtained by various methods, including high-precision instruments like thermal and optical cameras and specialized or environmental sensors like carbon dioxide (CO2) and passive infrared (PIR). While less accurate, the latter methods have drawn much interest since they are less expensive and invasive. This study's primary goal is to provide a unique prediction model that enhances the knowledge gleaned from sensor data by fusing evolutionary algorithms with machine learning (ML) models. To detect occupancy in an office setting, this study examines the effectiveness of four optimization algorithms: Multi-Verse Optimization (MVO), Teaching-Learning-Based Optimization (TLBO), Wind-Driven Optimization (WDO), and Whale Optimization Algorithm (WOA) when combined with Multilayer Perceptron (MLP). Among the noteworthy results is the strong performance of WDO-MLP, which shows remarkable AUC scores with little sensitivity to changes in swarm size. Furthermore, the best-performing approach is MVO combined with MLP, which shows better stability and discriminating ability at different swarm sizes. The practical implications of the research findings for implementing occupancy detection systems in smart buildings are noteworthy.
Comprehensive occupancy data from buildings may help energy management systems work better and save energy without sacrificing tenant comfort. Occupancy data is obtained by various methods, including high-precision instruments like thermal and optical cameras and specialized or environmental sensors like carbon dioxide (CO2) and passive infrared (PIR). While less accurate, the latter methods have drawn much interest since they are less expensive and invasive. This study's primary goal is to provide a unique prediction model that enhances the knowledge gleaned from sensor data by fusing evolutionary algorithms with machine learning (ML) models. To detect occupancy in an office setting, this study examines the effectiveness of four optimization algorithms: Multi-Verse Optimization (MVO), Teaching-Learning-Based Optimization (TLBO), Wind-Driven Optimization (WDO), and Whale Optimization Algorithm (WOA) when combined with Multilayer Perceptron (MLP). Among the noteworthy results is the strong performance of WDO-MLP, which shows remarkable AUC scores with little sensitivity to changes in swarm size. Furthermore, the best-performing approach is MVO combined with MLP, which shows better stability and discriminating ability at different swarm sizes. The practical implications of the research findings for implementing occupancy detection systems in smart buildings are noteworthy.
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