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Open AccessArticle

Towards Characterization of Indoor Environment in Smart Buildings: Modelling PMV Index Using Neural Network with One Hidden Layer

Department of Traction and Traffic Control, Faculty of Electrical and Computer Engineering, Cracow University of Technology, 31-155 Cracow, Poland
Sustainability 2020, 12(17), 6749; https://doi.org/10.3390/su12176749
Received: 15 July 2020 / Revised: 12 August 2020 / Accepted: 14 August 2020 / Published: 20 August 2020
(This article belongs to the Special Issue IEIE Buildings (Integration of Energy and Indoor Envirornent))
Modelling of comfort with the use of neural networks in modern times has become extremely popular. In recent years, scientists have been using these methods because of their satisfactory accuracy. The article proposes a method of modelling feedforward neural networks, thanks to which it is possible to obtain the most efficient network with one hidden layer in terms of a given quality criterion. The article also presents the methodology for modelling a PMV index, on the basis of which it can be demonstrated whether the network will work properly not only on paper but in reality as well. The objective of this work is to develop a performance model allowing the effective improvement of all electrical and mechanical devices affecting the energy efficiency and indoor environment in smart buildings. To achieve this, several attributes of indoor environment are included, namely: air leakage as a connection to the outdoor environment, but also as uncontrolled component of energy, ventilation as delivery and distribution of fresh air in the building space, individual ventilation on demand indoor air quality (IAQ) in the dwelling or as a personal IAQ control, source control of pollutants in the building, thermal comfort, temperature, air movement and humidity control (humidity modifiers, i.e., buffers different from the air conditioning radiation from cold and hot surfaces bringing forward a question about the strategy of the process control. One may either develop a series of control models to be synthesized later or one can use one over-arching characteristic and use its components for operating the control system. The paper addresses the second strategy and uses the concept of PMV for a criterion of broadly defined thermal comfort (including ventilation and air quality). View Full-Text
Keywords: PMV index; feedforward neural network; intelligent construction; intelligent building; thermal comfort modelling PMV index; feedforward neural network; intelligent construction; intelligent building; thermal comfort modelling
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MDPI and ACS Style

Dudzik, M. Towards Characterization of Indoor Environment in Smart Buildings: Modelling PMV Index Using Neural Network with One Hidden Layer. Sustainability 2020, 12, 6749. https://doi.org/10.3390/su12176749

AMA Style

Dudzik M. Towards Characterization of Indoor Environment in Smart Buildings: Modelling PMV Index Using Neural Network with One Hidden Layer. Sustainability. 2020; 12(17):6749. https://doi.org/10.3390/su12176749

Chicago/Turabian Style

Dudzik, Marek. 2020. "Towards Characterization of Indoor Environment in Smart Buildings: Modelling PMV Index Using Neural Network with One Hidden Layer" Sustainability 12, no. 17: 6749. https://doi.org/10.3390/su12176749

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