Thermal comfort indices analysis using multiple linear regression and neural network
Само за регистроване кориснике
2023
Конференцијски прилог (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
Compared to methodology provided by standards concerning thermal comfort, by using
models based on various approximation methods or artificial intelligence, it may be possible
to ensure more time efficient and accurate calculation of thermal comfort indices. The aim of
this study is to compare Predicted Mean Vote (PMV) computation model established by using
multiple linear regression and trained artificial neural network, from the standpoint of
accuracy. Both models are established on the basis of the same dataset which consists of 400
combinations of 4 thermal comfort parameters. These parameters are the air temperature,
mean radiant temperature, relative humidity and clothing resistance, while activity level and
air velocity are adopted as 1.1 met (office typing activity) and 0.05 m/s, respectively, and are
considered constant values for selected type of indoor environment. Clothing resistance is
adopted as 0.5 clo for summer period and 1.0 clo for winter period, while the ...air temperature,
mean radiant temperature and relative humidity are values which are randomly generated
within appropriately selected ranges. Taking into account that coefficients of determination
which correspond to it are over 95%, resulting first degree polynomial relation obtained by
using multiple linear regression can be considered a satisfactory approximation of PMV
model as it is given in ASHRAE Standard 55-2020. Furthermore, there are certain input value
combinations for which PMV values obtained by using this model coincide with the ones
calculated by using algorithm which is provided by standard. However, results obtained by
using trained neural network with one hidden layer coincide with PMV values calculated on
the basis of ASHRAE Standard 55-2020 for each input value combination. Therefore, from
the standpoint of accuracy, it is concluded that neural network provides significantly better
approximation of PMV model.
Кључне речи:
Thermal comfort / Predicted Mean Vote / linear regression / neural network / artificial intelligence / indoor environmental qualityИзвор:
18th conference on sustainable development of energy, water and environment systems, 2023Издавач:
- Faculty of Mechanical Engineering and Naval Architecture, Zagreb
Финансирање / пројекти:
- Интелигентни системи управљања климатизације у циљу постизања енергетски ефикасних режима у сложеним условима експлоатације (RS-MESTD-Technological Development (TD or TR)-33047)
- Иновативни приступ у примени интелигентних технолошких система за производњу делова од лима заснован на еколошким принципима (RS-MESTD-Technological Development (TD or TR)-35004)
Колекције
Институција/група
Mašinski fakultetTY - CONF AU - Kerčov, Anton AU - Jovanović, Radiša AU - Bajc, Tamara PY - 2023 UR - https://machinery.mas.bg.ac.rs/handle/123456789/7076 AB - Compared to methodology provided by standards concerning thermal comfort, by using models based on various approximation methods or artificial intelligence, it may be possible to ensure more time efficient and accurate calculation of thermal comfort indices. The aim of this study is to compare Predicted Mean Vote (PMV) computation model established by using multiple linear regression and trained artificial neural network, from the standpoint of accuracy. Both models are established on the basis of the same dataset which consists of 400 combinations of 4 thermal comfort parameters. These parameters are the air temperature, mean radiant temperature, relative humidity and clothing resistance, while activity level and air velocity are adopted as 1.1 met (office typing activity) and 0.05 m/s, respectively, and are considered constant values for selected type of indoor environment. Clothing resistance is adopted as 0.5 clo for summer period and 1.0 clo for winter period, while the air temperature, mean radiant temperature and relative humidity are values which are randomly generated within appropriately selected ranges. Taking into account that coefficients of determination which correspond to it are over 95%, resulting first degree polynomial relation obtained by using multiple linear regression can be considered a satisfactory approximation of PMV model as it is given in ASHRAE Standard 55-2020. Furthermore, there are certain input value combinations for which PMV values obtained by using this model coincide with the ones calculated by using algorithm which is provided by standard. However, results obtained by using trained neural network with one hidden layer coincide with PMV values calculated on the basis of ASHRAE Standard 55-2020 for each input value combination. Therefore, from the standpoint of accuracy, it is concluded that neural network provides significantly better approximation of PMV model. PB - Faculty of Mechanical Engineering and Naval Architecture, Zagreb C3 - 18th conference on sustainable development of energy, water and environment systems T1 - Thermal comfort indices analysis using multiple linear regression and neural network UR - https://hdl.handle.net/21.15107/rcub_machinery_7076 ER -
@conference{ author = "Kerčov, Anton and Jovanović, Radiša and Bajc, Tamara", year = "2023", abstract = "Compared to methodology provided by standards concerning thermal comfort, by using models based on various approximation methods or artificial intelligence, it may be possible to ensure more time efficient and accurate calculation of thermal comfort indices. The aim of this study is to compare Predicted Mean Vote (PMV) computation model established by using multiple linear regression and trained artificial neural network, from the standpoint of accuracy. Both models are established on the basis of the same dataset which consists of 400 combinations of 4 thermal comfort parameters. These parameters are the air temperature, mean radiant temperature, relative humidity and clothing resistance, while activity level and air velocity are adopted as 1.1 met (office typing activity) and 0.05 m/s, respectively, and are considered constant values for selected type of indoor environment. Clothing resistance is adopted as 0.5 clo for summer period and 1.0 clo for winter period, while the air temperature, mean radiant temperature and relative humidity are values which are randomly generated within appropriately selected ranges. Taking into account that coefficients of determination which correspond to it are over 95%, resulting first degree polynomial relation obtained by using multiple linear regression can be considered a satisfactory approximation of PMV model as it is given in ASHRAE Standard 55-2020. Furthermore, there are certain input value combinations for which PMV values obtained by using this model coincide with the ones calculated by using algorithm which is provided by standard. However, results obtained by using trained neural network with one hidden layer coincide with PMV values calculated on the basis of ASHRAE Standard 55-2020 for each input value combination. Therefore, from the standpoint of accuracy, it is concluded that neural network provides significantly better approximation of PMV model.", publisher = "Faculty of Mechanical Engineering and Naval Architecture, Zagreb", journal = "18th conference on sustainable development of energy, water and environment systems", title = "Thermal comfort indices analysis using multiple linear regression and neural network", url = "https://hdl.handle.net/21.15107/rcub_machinery_7076" }
Kerčov, A., Jovanović, R.,& Bajc, T.. (2023). Thermal comfort indices analysis using multiple linear regression and neural network. in 18th conference on sustainable development of energy, water and environment systems Faculty of Mechanical Engineering and Naval Architecture, Zagreb.. https://hdl.handle.net/21.15107/rcub_machinery_7076
Kerčov A, Jovanović R, Bajc T. Thermal comfort indices analysis using multiple linear regression and neural network. in 18th conference on sustainable development of energy, water and environment systems. 2023;. https://hdl.handle.net/21.15107/rcub_machinery_7076 .
Kerčov, Anton, Jovanović, Radiša, Bajc, Tamara, "Thermal comfort indices analysis using multiple linear regression and neural network" in 18th conference on sustainable development of energy, water and environment systems (2023), https://hdl.handle.net/21.15107/rcub_machinery_7076 .