Application of neural network to building environmental prediction and control

Li, Ruocheng; Zhang, Xiangpeng; Liu, Le; Li, Yifan; Xu, Qingyang

doi:10.1177/0143624419838362

Cited by 8 publications

(5 citation statements)

References 36 publications

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“…The most dominant IB found in the intelligent building literature is concerned with either “ energy efficiency behavior ” [ 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 ] or “energy saving behavior ” [ 100 , 102 , 103 , 105 , 106 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 ]. Ding et al [ 100 ], for example, identify research trends in building energy saving using a text mining methodology.…”

Section: Resultsmentioning

confidence: 99%

Characterizing Smart Environments as Interactive and Collective Platforms: A Review of the Key Behaviors of Responsive Architecture

Lee

Ostwald

Kim

2021

Sensors

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Since architect Nicholas Negroponte first proposed a vision of responsive architecture smart environments have been widely investigated, especially in the fields of computer science and engineering. Despite growing interest in the topic, a comprehensive review of research about smart environments from the architectural perspective is largely missing. In order to provide a formal understanding of smart environments in architecture, this paper conducts a systematic literature review of scholarly sources over the last decade, focusing on four related subjects: (1) responsive architecture, (2) kinetic architecture, (3) adaptive architecture and (4) intelligent buildings. Through this review, the paper identifies and examines interactive and collective behaviors in smart environments, thereby contributing to defining the properties of creative, smart spaces in the contemporary digital ecosystem. In addition, this research offers a means of systematically characterizing and constructing smart environments as interactive and collective platforms, enabling occupants to sense, experience and understand smart spaces.

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Section: Resultsmentioning

confidence: 99%

Characterizing Smart Environments as Interactive and Collective Platforms: A Review of the Key Behaviors of Responsive Architecture

Lee

Ostwald

Kim

2021

Sensors

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“…With intelligent and continuous learning methods, this approach can learn sophisticated nonlinear relationships between several parameters, allowing real identification of the systems. Several parametric system identification algorithms have been designed using Neural Networks to overcome standard identification limitations [14,27], such as the nonlinearity of building systems.…”

Section: System Identification By Neural Network Approachmentioning

confidence: 99%

Optimal Neural Network PID Approach for Building Thermal Management

et al. 2023

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The process of thermal control and regulation in buildings is considered complex. Its complexity lies in the various internal and external physical phenomena impacting its control, and also in the increasingly important requirements of occupant comfort, energy optimization and efficiency, and optimization of measuring and monitoring equipment. Recently, the establishment of technical rules for optimal building thermal control has gained interest in academia and industry. These rules have focused mainly on three aspects: the use of renewable energy, optimal management, and the use of equipment and materials allowing the optimization of energy. However, optimal control has not been addressed enough. In this article, we present a PID controller based on a Neural Network approach for thermal building management and control. The proposed approach is based on two processes: an optimal identification process dedicated to the thermal building behavior prediction impacted by variable and invariable elements, measured and unmeasured factors, and a control process to ensure the desired performance with optimal energy control. The results obtained show the advantages of the adopted system in terms of energy optimization, with an important energy gain of 8% to 11%, along with better regulation and control performance, and in terms of occupant comfort with minimal temperature variations.

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“…A wavelet neural network approach was proposed by [61] for improving PID controller performance. PID stands for (proportional + integral + derivative) controller.…”

Section: Wavelet Neural Networkmentioning

confidence: 99%

The Role of Machine Learning and the Internet of Things in Smart Buildings for Energy Efficiency

et al. 2022

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Machine learning can be used to automate a wide range of tasks. Smart buildings, which use the Internet of Things (IoT) to connect building operations, enable activities, such as monitoring temperature, safety, and maintenance, for easier controlling via mobile devices and computers. Smart buildings are becoming core aspects in larger system integrations as the IoT is becoming increasingly widespread. The IoT plays an important role in smart buildings and provides facilities that improve human security by using effective technology-based life-saving strategies. This review highlights the role of IoT devices in smart buildings. The IoT devices platform and its components are highlighted in this review. Furthermore, this review provides security challenges regarding IoT and smart buildings. The main factors pertaining to smart buildings are described and the different methods of machine learning in combination with IoT technologies are also described to improve the effectiveness of smart buildings to make them energy efficient.

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Application of neural network to building environmental prediction and control

Cited by 8 publications

References 36 publications

Characterizing Smart Environments as Interactive and Collective Platforms: A Review of the Key Behaviors of Responsive Architecture

Characterizing Smart Environments as Interactive and Collective Platforms: A Review of the Key Behaviors of Responsive Architecture

Optimal Neural Network PID Approach for Building Thermal Management

The Role of Machine Learning and the Internet of Things in Smart Buildings for Energy Efficiency

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