A hybrid deep transfer learning strategy for thermal comfort prediction in buildings
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Krithi Ramamritham | Anirudh Sriram | Anupama Kowli | Nivethitha Somu | K. Ramamritham | A. Kowli | Anirudh Sriram | Nivethitha Somu | Anupama Kowli
[1] P. O. Fanger,et al. Thermal comfort: analysis and applications in environmental engineering, , 1972 .
[2] C. Waelbroeck,et al. Consistently dated Atlantic sediment cores over the last 40 thousand years , 2019, Scientific Data.
[3] Aske Plaat,et al. On the Impact of Data Set Size in Transfer Learning Using Deep Neural Networks , 2016, IDA.
[4] Athanasios Tzempelikos,et al. Inference of thermal preference profiles for personalized thermal environments with actual building occupants , 2019, Building and Environment.
[5] Flora D. Salim,et al. Transfer Learning for Thermal Comfort Prediction in Multiple Cities , 2020, Building and Environment.
[6] Frederik Auffenberg,et al. A Personalised Thermal Comfort Model Using a Bayesian Network , 2015, IJCAI.
[7] B. Moghtaderi,et al. The Impact of the Thermal Comfort Models on the Prediction of Building Energy Consumption , 2018, Sustainability.
[8] Lihua Xie,et al. Random forest based thermal comfort prediction from gender-specific physiological parameters using wearable sensing technology , 2018 .
[9] Y. Zhai,et al. Using machine learning algorithms to predict occupants’ thermal comfort in naturally ventilated residential buildings , 2020, Energy and Buildings.
[10] P. Fanger. Moderate Thermal Environments Determination of the PMV and PPD Indices and Specification of the Conditions for Thermal Comfort , 1984 .
[11] Harimi Djamila,et al. Indoor thermal comfort predictions: Selected issues and trends , 2017 .
[12] W. H. Engelmann,et al. The National Human Activity Pattern Survey (NHAPS): a resource for assessing exposure to environmental pollutants , 2001, Journal of Exposure Analysis and Environmental Epidemiology.
[13] Fang Liu,et al. Improved thermal comfort modeling for smart buildings: A data analytics study , 2018 .
[14] Joyce Kim,et al. Personal comfort models: Predicting individuals' thermal preference using occupant heating and cooling behavior and machine learning , 2018 .
[15] Zhixing Li,et al. Intelligent Thermal Comfort Controlling System for Buildings Based on IoT and AI , 2020, Future Internet.
[16] Siliang Lu,et al. Data-driven simulation of a thermal comfort-based temperature set-point control with ASHRAE RP884 , 2019, Building and Environment.
[17] Roger Zimmermann,et al. Conversational transfer learning for emotion recognition , 2021, Inf. Fusion.
[18] William W. Braham,et al. Measuring the right factors: A review of variables and models for thermal comfort and indoor air quality , 2021 .
[19] Lihua Xie,et al. A feedforward neural network based indoor-climate control framework for thermal comfort and energy saving in buildings , 2019, Applied Energy.
[20] Zoltán Nagy,et al. Comprehensive analysis of the relationship between thermal comfort and building control research - A data-driven literature review , 2018 .
[21] L. Wong,et al. Investigation of thermal comfort in sleeping environment and its association with sleep quality , 2021 .
[22] Carol C. Menassa,et al. Personalized human comfort in indoor building environments under diverse conditioning modes , 2017 .
[23] P. Gurian,et al. Tracking the human-building interaction: A longitudinal field study of occupant behavior in air-conditioned offices , 2015 .
[24] Yanfeng Liu,et al. A holistic approach to the evaluation of the indoor temperature based on thermal comfort and learning performance , 2021 .
[25] Jingsi Zhang,et al. Comparing machine learning algorithms in predicting thermal sensation using ASHRAE Comfort Database II , 2020, Energy and Buildings.
[26] Xu Zhang,et al. Data-driven thermal comfort model via support vector machine algorithms: Insights from ASHRAE RP-884 database , 2020 .
[27] Nitesh V. Chawla,et al. SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..
[28] D. Lai,et al. A machine learning approach to predict outdoor thermal comfort using local skin temperatures , 2020 .
[29] E. W. Shaw. Thermal Comfort: analysis and applications in environmental engineering, by P. O. Fanger. 244 pp. DANISH TECHNICAL PRESS. Copenhagen, Denmark, 1970. Danish Kr. 76, 50 , 1972 .
[30] Paola Boarin,et al. Post-occupancy evaluation of a historic primary school in Spain: Comparing PMV, TSV and PD for teachers' and pupils' thermal comfort , 2017 .
[31] David S. Moore,et al. Chi-Square Tests. , 1976 .
[32] Luc Van Gool,et al. NIDL: A pilot study of contactless measurement of skin temperature for intelligent building , 2019, Energy and Buildings.
[33] Prashant Kumar,et al. Natural ventilation in warm climates: The challenges of thermal comfort, heatwave resilience and indoor air quality , 2021 .
[34] Deepak Ranjan Nayak,et al. COVID-19 classification by CCSHNet with deep fusion using transfer learning and discriminant correlation analysis , 2020, Information Fusion.
[35] Burcin Becerik-Gerber,et al. Infrared thermography of human face for monitoring thermoregulation performance and estimating personal thermal comfort , 2016 .
[36] Xiwang Li,et al. Using an ensemble machine learning methodology-Bagging to predict occupants’ thermal comfort in buildings , 2018, Energy and Buildings.
[37] Yonggang Wen,et al. DeepComfort: Energy-Efficient Thermal Comfort Control in Buildings Via Reinforcement Learning , 2020, IEEE Internet of Things Journal.
[38] Yong Luo,et al. Heterogeneous Transfer Learning for Thermal Comfort Modeling , 2019, BuildSys@SenSys.
[39] Italo Meroni,et al. Integrated Method for Personal Thermal Comfort Assessment and Optimization through Users’ Feedback, IoT and Machine Learning: A Case Study , 2018, Sensors.
[40] Mostafa Rafaie,et al. An IoT Framework for Modeling and Controlling Thermal Comfort in Buildings , 2020, Frontiers in Built Environment.
[41] Jyotirmay Mathur,et al. The Scales Project, a cross-national dataset on the interpretation of thermal perception scales , 2019, Scientific Data.
[42] Farrokh Jazizadeh,et al. Towards integration of doppler radar sensors into personalized thermoregulation-based control of HVAC , 2017, BuildSys@SenSys.
[43] Angela Sanguinetti,et al. Upscaling participatory thermal sensing: Lessons from an interdisciplinary case study at University of California for improving campus efficiency and comfort , 2017 .
[44] Bo Peng,et al. Data-Driven Thermal Comfort Prediction With Support Vector Machine , 2017 .
[45] Lihua Xie,et al. Machine learning based prediction of thermal comfort in buildings of equatorial Singapore , 2017, 2017 IEEE International Conference on Smart Grid and Smart Cities (ICSGSC).
[46] Krishna R. Pattipati,et al. Predicting individual thermal comfort using machine learning algorithms , 2015, 2015 IEEE International Conference on Automation Science and Engineering (CASE).
[47] Geun Young Yun,et al. The use of artificial intelligence (AI) methods in the prediction of thermal comfort in buildings: energy implications of AI-based thermal comfort controls , 2020 .
[48] Rahul Simha,et al. Machine learning method for real-time non-invasive prediction of individual thermal preference in transient conditions , 2019, Building and Environment.
[49] Carol C. Menassa,et al. Non-intrusive interpretation of human thermal comfort through analysis of facial infrared thermography , 2018, Energy and Buildings.