Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
A hybrid deep transfer learning strategy for thermal comfort prediction in buildings
https://orcid.org/0000-0002-3699-900X
https://orcid.org/0000-0002-5199-7352
https://orcid.org/0000-0002-2330-9321
Abstract Since the thermal condition of living spaces affects the occupants' productivity and their quality of life, it is important to design effective heating, ventilation and air conditioning (HVAC) control strategies for better energy efficiency and thermal comfort. An essential step in HVAC control and energy optimization is thermal comfort modeling. Recently, data-driven thermal comfort models have been preferred over the Fanger's Predicted Mean Vote (PMV) model due to higher accuracy and ease of use. However, the unavailability of comprehensive labelled thermal comfort data from the occupants poses a significant modeling challenge. This paper addresses data inadequacy issues by adopting ‘transfer learning’ to leverage well learned knowledge from source domain (same climate zones) to target domain (different climate zone) where modeling data is sparse. Specifically, a Transfer Learning based Convolutional Neural Networks-Long Short Term Memory neural networks (TL CNN-LSTM) is designed for effective thermal comfort modeling that exploits the spatio-temporal relations in the thermal comfort data. The significant modeling parameters for TL CNN-LSTM are identified using the Chi-squared test. Further, the lack of sufficient samples across all thermal conditions in the available thermal comfort datasets was handled by Synthetic Minority Oversampling Technique (SMOTE). Experiments with two source (ASHRAE RP-884 and Scales Project) and one target (Medium US office) datasets demonstrate the ability of TL CNN-LSTM in achieving an accuracy of >55% with limited data in target buildings. The limitation of TL CNN-LSTM is its continued dependence on intrusive parameters and the challenges in assessing its adaptability to different climate zones.
Highlights Transfer learning based CNN-LSTM model is presented for thermal comfort modeling. It provides accurate thermal comfort prediction for buildings with limited data. Significant thermal comfort parameters are identified using Chi-square statistics. SMOTE oversampling technique is used to address the class imbalance problem. Impact of different thermal comfort parameters on the accuracy of model is studied.
A hybrid deep transfer learning strategy for thermal comfort prediction in buildings
https://orcid.org/0000-0002-3699-900X
https://orcid.org/0000-0002-5199-7352
https://orcid.org/0000-0002-2330-9321
Abstract Since the thermal condition of living spaces affects the occupants' productivity and their quality of life, it is important to design effective heating, ventilation and air conditioning (HVAC) control strategies for better energy efficiency and thermal comfort. An essential step in HVAC control and energy optimization is thermal comfort modeling. Recently, data-driven thermal comfort models have been preferred over the Fanger's Predicted Mean Vote (PMV) model due to higher accuracy and ease of use. However, the unavailability of comprehensive labelled thermal comfort data from the occupants poses a significant modeling challenge. This paper addresses data inadequacy issues by adopting ‘transfer learning’ to leverage well learned knowledge from source domain (same climate zones) to target domain (different climate zone) where modeling data is sparse. Specifically, a Transfer Learning based Convolutional Neural Networks-Long Short Term Memory neural networks (TL CNN-LSTM) is designed for effective thermal comfort modeling that exploits the spatio-temporal relations in the thermal comfort data. The significant modeling parameters for TL CNN-LSTM are identified using the Chi-squared test. Further, the lack of sufficient samples across all thermal conditions in the available thermal comfort datasets was handled by Synthetic Minority Oversampling Technique (SMOTE). Experiments with two source (ASHRAE RP-884 and Scales Project) and one target (Medium US office) datasets demonstrate the ability of TL CNN-LSTM in achieving an accuracy of >55% with limited data in target buildings. The limitation of TL CNN-LSTM is its continued dependence on intrusive parameters and the challenges in assessing its adaptability to different climate zones.
Highlights Transfer learning based CNN-LSTM model is presented for thermal comfort modeling. It provides accurate thermal comfort prediction for buildings with limited data. Significant thermal comfort parameters are identified using Chi-square statistics. SMOTE oversampling technique is used to address the class imbalance problem. Impact of different thermal comfort parameters on the accuracy of model is studied.
A hybrid deep transfer learning strategy for thermal comfort prediction in buildings
https://orcid.org/0000-0002-3699-900X
https://orcid.org/0000-0002-5199-7352
https://orcid.org/0000-0002-2330-9321
Abstract Since the thermal condition of living spaces affects the occupants' productivity and their quality of life, it is important to design effective heating, ventilation and air conditioning (HVAC) control strategies for better energy efficiency and thermal comfort. An essential step in HVAC control and energy optimization is thermal comfort modeling. Recently, data-driven thermal comfort models have been preferred over the Fanger's Predicted Mean Vote (PMV) model due to higher accuracy and ease of use. However, the unavailability of comprehensive labelled thermal comfort data from the occupants poses a significant modeling challenge. This paper addresses data inadequacy issues by adopting ‘transfer learning’ to leverage well learned knowledge from source domain (same climate zones) to target domain (different climate zone) where modeling data is sparse. Specifically, a Transfer Learning based Convolutional Neural Networks-Long Short Term Memory neural networks (TL CNN-LSTM) is designed for effective thermal comfort modeling that exploits the spatio-temporal relations in the thermal comfort data. The significant modeling parameters for TL CNN-LSTM are identified using the Chi-squared test. Further, the lack of sufficient samples across all thermal conditions in the available thermal comfort datasets was handled by Synthetic Minority Oversampling Technique (SMOTE). Experiments with two source (ASHRAE RP-884 and Scales Project) and one target (Medium US office) datasets demonstrate the ability of TL CNN-LSTM in achieving an accuracy of >55% with limited data in target buildings. The limitation of TL CNN-LSTM is its continued dependence on intrusive parameters and the challenges in assessing its adaptability to different climate zones.
Highlights Transfer learning based CNN-LSTM model is presented for thermal comfort modeling. It provides accurate thermal comfort prediction for buildings with limited data. Significant thermal comfort parameters are identified using Chi-square statistics. SMOTE oversampling technique is used to address the class imbalance problem. Impact of different thermal comfort parameters on the accuracy of model is studied.
A hybrid deep transfer learning strategy for thermal comfort prediction in buildings
Somu, Nivethitha (Autor:in) / Sriram, Anirudh (Autor:in) / Kowli, Anupama (Autor:in) / Ramamritham, Krithi (Autor:in)
Building and Environment ; 204
07.07.2021
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Thermal comfort , HVACs , Energy efficiency , Buildings , Transfer learning , Deep learning , HVAC , heating, ventilation and air conditioning , PMV , predicted mean vote , TL , transfer learning , CNN , convolutional neural networks , LSTM , long short term neural networks , SMOTE , synthetic minority over sampling technique , MCC , Matthew's correlation coefficient , IEQ , indoor environment quality , ANN , artificial neural networks , SVM , support vector machine , RF , random forest , <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>k</mi> <mo>−</mo></mrow></math>NN , <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>k</mi></mrow></math> nearest neighbor , LR , logistic regression , GBM , gradient boosting machine , MLP , multi layer perceptron , TCP , thermal comfort parameters , AT , air temperature , ST , skin temperature , RH , relative humidity , PR , pulse rate , MR , metabolism rate , SC , skin conductance , CF , clothing factor , OS , oxygen saturation , CT , clock time , BA , body surface area , ID , indoor duration , E , energy consumption , WS , wind speed , AAT , average AT , OT , outdoor temperature , OAAT , outdoor AAT , AV , air velocity , OARH , outdoor average relative humidity , MRT , mean radiant temperature , AMR , average MR , TC , thermal comfort , AMRT , average MRT , OH , outdoor humidity , HSS , HVAC status and set point , AAS , average air speed , OI , outdoor illuminance , MST , mean surface temperature , IT , indoor temperature , TV , temperature variance , IH , indoor humidity , MSTG , mean surface temperature gradient , IMRT , indoor mean radiant temperature , MSTD , mean surface temperature difference , RT , radiant temperature , SS , skin surface , CO2 , CO2 concentration , CS , clothing surface , IL , illuminance level , IAT , indoor AT , FA , floor area , OAT , outdoor AT , We , weight , SR , solar radiation , He , height , thermal load , Pos , position , TSV , thermal sensation vote , heating/cooling intensity and location , AL , activity level , CO , chair occupancy , climate type , GT , global temperature , AC , adaptive control , TA , thermal acceptability , BOM , building operation mode , TP , thermal preference , ART , average radiant temperature , VAV , variable air volume control settings , HR , heart rate , TR , thermostat readings , IAV , indoor air velocity , WD , weather data , IAH , indoor air humidity , ORH , outdoor relative humidity , OAV , outdoor air velocity , A Turb , air turbulence
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