Exploratory comparison of control algorithms and machine learning as regulators for a personalized climatization system

Metzmacher, Henning; Syndicus, Marc; Warthmann, Alexander; van, Christoph Treeck

doi:10.1016/j.enbuild.2021.111653

Cited by 13 publications

(2 citation statements)

References 38 publications

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“…Preliminary studies using the system have been conducted using two local actuators. 57,58 It was shown that the system is able to compensate cold and warm ambient conditions and increase the subject’s thermal comfort within a defined temperature range. Since only two actuators were tested, the system ability to compensate for cold ambient temperatures was limited.…”

Section: Discussionmentioning

confidence: 99%

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Modular personalized climatization testing infrastructure with smartphone-based user feedback

Metzmacher

Syndicus

Warthmann

et al. 2022

Building Services Engineering Research and Technology

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Heating and ventilation in buildings and vehicles are responsible for over a third of global final energy usage and the resulting emissions. Local climatization can help to save energy while at the same time enable more individualized and adapted micro-climates around people. For the domain of local comfort, integrative test environments for sensors, actuators, and control software are scarce, and oftentimes tailored to a specific set of components. Here, a server-based modular testing infrastructure which allows integration and evaluation of sensors, actuators, and control strategies is presented. Currently, the system is able to integrate, monitor, and log data of thermal imaging, motion sensing, environmental sensor such as temperature or air velocity and to forward signals to actuators such as fans, infrared- or contact-heaters. The generation of control signals is model-based and relies on user feedback provided via the system’s smartphone app. Lastly, learning algorithms can be trained and compared during user studies. Practical Application: Although developed in a laboratory based research context, the proposed system is based on open standards and protocols. It therefore can be applied by practitioners, developers, and manufacturers in order to test stand-alone components as well as ensembles of sensors and actuators for personalized climatization in an integrative and replicable manner.

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Section: Discussionmentioning

confidence: 99%

“…user feedback on current thermal sensation, can be collected via the user app described in section User Integration and Feedback App. A full description of an exploratory comparison of control strategies can be found in Metzmacher et al, 57 and will only be briefly described here.…”

Section: Model Based Controlmentioning

confidence: 99%

Modular personalized climatization testing infrastructure with smartphone-based user feedback

Metzmacher

Syndicus

Warthmann

et al. 2022

Building Services Engineering Research and Technology

Self Cite

View full text Add to dashboard Cite

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Personalized Ventilation Systems

Yang

Liu

Zhu

et al. 2023

Indoor Environment and Sustainable Building

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Machine Learning Applications for Smart Building Energy Utilization: A Survey

Huotari,

Malhi,

Främling

2024

Arch Computat Methods Eng

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The United Nations launched sustainable development goals in 2015 that include goals for sustainable energy. From global energy consumption, households consume 20–30% of energy in Europe, North America and Asia; furthermore, the overall global energy consumption has steadily increased in the recent decades. Consequently, to meet the increased energy demand and to promote efficient energy consumption, there is a persistent need to develop applications enhancing utilization of energy in buildings. However, despite the potential significance of AI in this area, few surveys have systematically categorized these applications. Therefore, this paper presents a systematic review of the literature, and then creates a novel taxonomy for applications of smart building energy utilization. The contributions of this paper are (a) a systematic review of applications and machine learning methods for smart building energy utilization, (b) a novel taxonomy for the applications, (c) detailed analysis of these solutions and techniques used for the applications (electric grid, smart building energy management and control, maintenance and security, and personalization), and, finally, (d) a discussion on open issues and developments in the field.

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Exploratory comparison of control algorithms and machine learning as regulators for a personalized climatization system

Cited by 13 publications

References 38 publications

Modular personalized climatization testing infrastructure with smartphone-based user feedback

Modular personalized climatization testing infrastructure with smartphone-based user feedback

Personalized Ventilation Systems

Machine Learning Applications for Smart Building Energy Utilization: A Survey

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