Data Driven PMV-Comfort and Energy Consumption Control in Common Buildings

Rassadin, Yury; Shushko, Nikita

doi:10.1088/1742-6596/2701/1/012148

J. Phys.: Conf. Ser.

2024

DOI: 10.1088/1742-6596/2701/1/012148

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Data Driven PMV-Comfort and Energy Consumption Control in Common Buildings

Yury Rassadin,

Nikita Shushko

Abstract: HVAC systems are essential in the energy management of commercial buildings. The main goal for HVAC system is to improve productivity of the inhabitants by providing comfortable indoor environment. The most common tool for evaluating comfort is PMV index, non-linear combination of indoor environment state variables. The Paper considers optimization of energy consumption of the building by combining state space expansion and microgrid approach.

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Personalized and humanized design of building interior environment using IoT technology

Qiao,

Lv,

et al. 2024

Applied Mathematics and Nonlinear Sciences

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The application of Internet of Things (IoT) technology has become more and more widespread, and the smart home based on IoT technology has penetrated thousands of households and the design of the internal environment of the building is also gradually intelligentized and digitized. This study realizes the personalized and humanized design of IoT technology in the internal environment of buildings through the design of a personalized smart home system based on IoT, which focuses on the use of the Apriori algorithm to mine the association rules of the user’s preferences for the internal environment of the building. Then, it uses the GA-BP neural network algorithm to predict the audience’s preferences and the PMV indexes so as to achieve the personalized and humanized design of the smart home system. And human-centered design. The system is evaluated for its performance and practical application, and the design effect of the building’s interior environment is examined. The network bandwidth bottleneck of the system is 2M, and the absolute error of the GA-BP prediction model is within (−0.04, 0.19), with an average accuracy of 99.245% and an improvement of 20.38% over the BP prediction model. The importance and satisfaction of all the scenario models of this system are within (3,5), the scenario model settings are more reasonable, the user satisfaction is high, and it can meet the user’s personalized and humanized needs. The results of this research can guide the personalized and humanized design of the internal environment of the building and provide certain theoretical references for the systematic study of the design of the internal environment of the building.

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Personalized and humanized design of building interior environment using IoT technology

Qiao,

Lv,

et al. 2024

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

show abstract

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Data Driven PMV-Comfort and Energy Consumption Control in Common Buildings

Cited by 1 publication

References 7 publications

Personalized and humanized design of building interior environment using IoT technology

Personalized and humanized design of building interior environment using IoT technology

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