Development and comparison of adaptive data-driven models for thermal comfort assessment and control

Arnaiz-González, 2016, Instance selection for regression: Adapting DROP, Neurocomputing, 201, 66, 10.1016/j.neucom.2016.04.003 Arnaiz-González, 2016, Instance selection for regression by discretization, Expert Systems with Applications, 54, 340, 10.1016/j.eswa.2015.12.046 Brager, 1998, thermal adaptation in the built environment: a literature review, Energy and Buildings, 27, 83, 10.1016/S0378-7788(97)00053-4 Castilla, 2018, Affective evaluation of the luminous environment in university classrooms, Journal of Environmental Psychology, 58, 52, 10.1016/j.jenvp.2018.07.010 Chaudhuri, 2018, Thermal comfort prediction using normalized skin temperature in a uniform built environment, Energy and Buildings, 159, 426, 10.1016/j.enbuild.2017.10.098 Chaudhuri, 2019, A feedforward neural network based indoor-climate control framework for thermal comfort and energy saving in buildings, Applied Energy, 248, 44, 10.1016/j.apenergy.2019.04.065 Cheung, 2019, Analysis of the accuracy on PMV – PPD model using the ASHRAE global thermal comfort database II, Building and Environment, 153, 205, 10.1016/j.buildenv.2019.01.055 Cosma, 2019, Machine learning method for real-time non-invasive prediction of individual thermal preference in transient conditions, Building and Environment, 148, 372, 10.1016/j.buildenv.2018.11.017 Dai, 2017, Machine learning approaches to predict thermal demands using skin temperatures: Steady-state conditions, Building and Environment, 114, 1, 10.1016/j.buildenv.2016.12.005 de Dear, 1998, Global database of thermal comfort field experiments, ASHRAE Transactions, 104, 1141 Djamila, 2017, Indoor thermal comfort predictions: Selected issues and trends, Renewable and Sustainable Energy Reviews, 74, 569, 10.1016/j.rser.2017.02.076 Djongyang, 2010, Thermal comfort: A review paper, Renewable and Sustainable Energy Reviews, 14, 2626, 10.1016/j.rser.2010.07.040 Du, 2019, Quantification of personal thermal comfort with localized airflow system based on sensitivity analysis and classification tree model, Energy and Buildings, 194, 1, 10.1016/j.enbuild.2019.04.010 EN 16798-1, Energy performance of buildings - Ventilation for buildings - Part 1: Indoor environmental input parameters for design and assessment of energy performance of buildings addressing indoor air quality, thermal environment, lighting and acoustics, 2019. Enescu, 2017, A review of thermal comfort models and indicators for indoor environments, Renewable and Sustainable Energy Reviews, 79, 1353, 10.1016/j.rser.2017.05.175 Fanger, 1970 Fanger, 2002, Extension of the PMV model to non-air-conditioned buildings in warm climates, Spec. Issue Therm. Comf. Stand., 34, 533 Fantozzi, 2020, An extensive collection of evaluation indicators to assess occupants’ health and comfort in indoor environment, Atmosphere., 11, 90, 10.3390/atmos11010090 Farhan, 2015, IEEE int, Conf. Autom. Sci. Eng. CASE, 2015, 708 Földváry Ličina, 2018, Development of the ASHRAE global thermal comfort database II, Building and Environment, 142, 502, 10.1016/j.buildenv.2018.06.022 G. Gao, J. Li, Y. Wen, Energy-Efficient Thermal Comfort Control in Smart Buildings via Deep Reinforcement Learning, (2019). arXiv:1901.04693. Griffiths, 1990, Thermal comfort studies in buildings with passive solar features, Field studies. Hoerl, 1970, Ridge regression: Biased estimation for nonorthogonal problems, Technometrics, 12, 55, 10.1080/00401706.1970.10488634 M. Humphreys, F. Nicol, R. S., Adaptive Thermal Comfort: Foundations and Analysis, Routledge, London, 2016. https://doi.org/10.4324/9781315765815. Humphreys, 2002, The validity of ISO-PMV for predicting comfort votes in every-day thermal environments, Energy and Buildings, 34, 667, 10.1016/S0378-7788(02)00018-X Humphreys, 2013, Updating the adaptive relation between climate and comfort indoors; new insights and an extended database, Building and Environment, 63, 40, 10.1016/j.buildenv.2013.01.024 ISO 7726, Ergonomics of the thermal environment – Instruments for measuring physical quantities, 2001. ISO 7730, Ergonomics of the thermal environment - Analytical determination and interpretation of thermal comfort using calculation of the PMV and PPD indices and local thermal comfort criteria, 2006. Jiang, 2016, Modelling personal thermal sensations using c-Support vector classification (C-SVC) algorithm, Building and Environment, 99, 98, 10.1016/j.buildenv.2016.01.022 Kim, 2018, Personal comfort models: Predicting individuals’ thermal preference using occupant heating and cooling behavior and machine learning, Building and Environment, 129, 96, 10.1016/j.buildenv.2017.12.011 Kumar, 2022, Subject’s thermal adaptation in different built environments: an analysis of updated metadata-base of thermal comfort data in india, J. Build. Eng., 46 Lamberti, 2023, investigating the effects of climate on thermal adaptation: A comparative field study in naturally ventilated university classrooms, Energy and Buildings, 294, 10.1016/j.enbuild.2023.113227 Lamberti, 2020, IEEE int, Conf. EEEIC ICPS Eur., 2020, 1 Lee, 2017, A bayesian approach for probabilistic classification and inference of occupant thermal preferences in office buildings, Building and Environment, 118, 323, 10.1016/j.buildenv.2017.03.009 Li, 2017, Personalized human comfort in indoor building environments under diverse conditioning modes, Building and Environment, 126, 304, 10.1016/j.buildenv.2017.10.004 Lu, 2019, Data-driven simulation of a thermal comfort-based temperature set-point control with ASHRAE RP884, Building and Environment, 156, 137, 10.1016/j.buildenv.2019.03.010 Luo, 2020, Comparing machine learning algorithms in predicting thermal sensation using ASHRAE comfort database II, Energy and Buildings, 210, 10.1016/j.enbuild.2020.109776 McCartney, 2002, Developing an adaptive control algorithm for europe, Spec. Issue Therm. Comf. Stand., 34, 623 Nielsen, 2016, Introduction to HPC with MPI for data science, 195 Oliveri, 2016, IEEE 16th int, Conf. Environ. Electr. Eng. EEEIC, 2016, 1 Olvera-López, 2010, A review of instance selection methods, Artificial Intelligence Review, 34, 133, 10.1007/s10462-010-9165-y Pedregosa, 2011, Scikit-learn: Machine learning in python, Journal of Machine Learning Research, 12, 2825 Rana, 2013, Feasibility analysis of using humidex as an indoor thermal comfort predictor, Energy and Buildings, 64, 17, 10.1016/j.enbuild.2013.04.019 Rawal, 2022, Adaptive thermal comfort model based on field studies in five climate zones across india, Building and Environment, 219, 10.1016/j.buildenv.2022.109187 Rupp, 2019, thermal sensitivity of occupants in different building typologies: the griffiths constant is a variable, Energy and Buildings, 200, 11, 10.1016/j.enbuild.2019.07.048 Song, 2017, An efficient instance selection algorithm for k nearest neighbor regression, Neurocomputing, 251, 26, 10.1016/j.neucom.2017.04.018 Tartarini, 2020, Pythermalcomfort: A python package for thermal comfort research, SoftwareX., 12, 10.1016/j.softx.2020.100578 S. Taylor, Putting People First: The Healing Power of Indoor Air, (2020). https://www.ashrae.org/file%20library/technical%20resources/covid-19/seminar-21_presentation-1.pdf. Tibshirani, 1996, Regression shrinkage and selection via the lasso, J. R. Stat. Soc. Ser. B-Methodol., 58, 267 Wang, 2014, thermal adaptation and thermal environment in university classrooms and offices in harbin, Energy and Buildings, 77, 192, 10.1016/j.enbuild.2014.03.054 Wang, 2019, Predicting older people’s thermal sensation in building environment through a machine learning approach: Modelling, interpretation, and application, Building and Environment, 161, 10.1016/j.buildenv.2019.106231 Wu, 2018, Using an ensemble machine learning methodology-Bagging to predict occupants’ thermal comfort in buildings, Energy and Buildings, 173, 117, 10.1016/j.enbuild.2018.05.031 Xie, 2020, Review on occupant-centric thermal comfort sensing, predicting, and controlling, Energy and Buildings, 226, 10.1016/j.enbuild.2020.110392 Zhang, 2019, Effects of moderate thermal environments on cognitive performance: A multidisciplinary review, Applied Energy, 236, 760, 10.1016/j.apenergy.2018.12.005 Zhao, 2014, A data-driven method to describe the personalized dynamic thermal comfort in ordinary office environment: from model to application, Building and Environment, 72, 309, 10.1016/j.buildenv.2013.11.008 Zhou, 2020, Data-driven thermal comfort model via support vector machine algorithms: Insights from ASHRAE RP-884 database, Energy and Buildings, 211, 10.1016/j.enbuild.2020.109795 Zinzi, 2021, On the Built-Environment quality in nearly Zero-Energy renovated schools: Assessment and impact of passive strategies, Energies, 14, 10.3390/en14102799 Zou, 2005, Regularization and variable selection via the elastic net (vol B 67, pg 301, Journal of the Royal Statistical Society, Series B, 67, 768, 10.1111/j.1467-9868.2005.00527.x