Influences on the predictive performance of thermal sensation indices

References

• Andersen, R. K., Fabi, V., & Corgnati, S. P. (2016). Predicted and actual indoor environmental quality: Verification of occupants’ behaviour models in residential buildings. Energy and Buildings, 127, 105–115. doi: 10.1016/j.enbuild.2016.05.074
   Web of Science ®Google Scholar
• ASHRAE. (2013). Standard 55-2013. Thermal environmental conditions for human occupancy. American Society of Heating, Refrigerating and Air-Conditioning Engineering, Atlanta, USA.
   Google Scholar
• Brager, G. S., Paliaga, G., & de Dear, R. J. (2004). Operable windows, personal control, and occupant comfort. ASHRAE Transactions, 110(Part 2), 17–35. Retrieved from https://escholarship.org/uc/item/4×57v1pf#page-1
   Google Scholar
• CEN 15251. (2012, August). Indoor environmental input parameters for design and assessment of energy performance of buildings addressing indoor air quality, thermal environment, lighting and acoustics; German version EN 15251:2012.
   Google Scholar
• Chappells, H., & Shove, E. (2005). Debating the future of comfort: Environmental sustainability, energy consumption and the indoor environment. Building Research & Information, 33(1), 32–40. doi: 10.1080/0961321042000322762
   Web of Science ®Google Scholar
• de Dear, R. J., Brager, G. S., & Cooper, D. (1997). Developing an adaptive model of thermal comfort and preference. In Final Report on ASHRAE Research Project 884. Macquarie University Sydney.
   Google Scholar
• Fabi, V., Sugliano, M., Andersen, R. K., & Corgnati, S. P. (2015). Validation of occupants’ behaviour models for indoor quality parameter and energy consumption prediction. Procedia Engineering, 121, 1805–1811. doi: 10.1016/j.proeng.2015.09.160
   Google Scholar
• Fanger, P. O. (1970). Thermal comfort analysis and applications in environmental engineering. New York: McGraw-Hill.
   Google Scholar
• Fanger, P. O., & Toftum, J. (2002). Extension of the PMV model to non-air-conditioned buildings in warm climates. Energy and Buildings, 34, 533–536. doi:10.1016/S0378-7788(02)00003-8
   Web of Science ®Google Scholar
• Gagge, A. P., Fobelets, A. P., & Berglund, L. G. (1986). A standard predictive index of human response to the thermal environment. ASHRAE Transactions, 92(2B), 709–731.
   Google Scholar
• Gagge, A. P., Nishi, Y., & Gonzalez, R. R. (1972). Standard effective temperature – A single temperature index of temperature sensation and thermal discomfort. Proceedings of the CIB Commission W45 (human requirements) symposium, Watford, UK: Building Research Station, pp. 229–250.
   Google Scholar
• Gao, J., Wang, Y., & Wargocki, P. (2015). Comparative analysis of modified PMV models and SET models to predict human thermal sensation in naturally ventilated buildings. Building and Environment, 92, 200–208. doi:10.1016/j.buildenv.2015.04.030
   Web of Science ®Google Scholar
• Hawighorst, M., Schweiker, M., & Wagner, A. (2015). The psychology of thermal comfort: Influences of thermo-specific self-efficacy and climate sensitiveness. In M. Loomans & M. te Kulve (Eds.), Proceedings of the healthy buildings Europe, 1–9. Eindhoven.
   Google Scholar
• Hellwig, R. T. (2015). Perceived control in indoor environments: A conceptual approach. Building Research & Information, 43(3), 302–315. doi: 10.1080/09613218.2015.1004150
   Web of Science ®Google Scholar
• Humphreys, M. A., & Nicol, F. (1998). Understanding the adaptive approach to thermal comfort. ASHRAE Transactions, 104(1), 991–1004.
   Google Scholar
• Humphreys, M. A., & Nicol, F. (2002). The validity of ISO-PMV for predicting comfort votes in every-day thermal environments. Energy and Buildings, 34(6), 667–684. doi:10.1016/S0378-7788(02)00018-X
   Web of Science ®Google Scholar
• ISO 7730. (2005). 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.
   Google Scholar
• Kingma, B. R., Schweiker, M., Wagner, A., & van Marken Lichtenbelt, W. D. (2017). Exploring internal body heat balance to understand thermal sensation. Building Research & Information. Forthcoming.
   Web of Science ®Google Scholar
• Leaman, A., & Bordass, B. (2007). Are users more tolerant of ‘green’ buildings? Building Research & Information, 35(6), 662–673. doi:10.1080/09613210701529518
   Web of Science ®Google Scholar
• McIntyre, D. A. (1980). Indoor climate. London: Applied Science Publisher.
   Google Scholar
• R Development Core Team. (2012). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing, Vienna, Austria. Retrieved from http://www.r-project.org
   Google Scholar
• Schiavon, S., Hoyt, T., & Piccioli, A. (2014). Web application for thermal comfort visualization and calculation according to ASHRAE Standard 55. Building Simulation, 7(4), 321–334. doi: 10.1007/s12273-013-0162-3
   Web of Science ®Google Scholar
• Schweiker, M., Fuchs, X., Becker, S., Shukuya, M., Dovjak, M., Hawighorst, M., & Kolarik, J. (2016). Challenging the assumptions for thermal sensation scales. Building Research & Information. http://doi.org/10.1080/09613218.2016.1183185
   Web of Science ®Google Scholar
• Schweiker, M., Haldi, F., Shukuya, M., & Robinson, D. (2012). Verification of stochastic models of window opening behaviour for residential buildings. Journal of Building Performance Simulation, 5(January 2015), 55–74. doi:10.1080/19401493.2011.567422
   Web of Science ®Google Scholar
• Schweiker, M., Mueller, S., Kleber, M., Kingma, B. R., & Shukuya, M. (2016). comf: Functions for thermal comfort research. Retrieved from https://cran.r-project.org/package=comf
   Google Scholar
• Schweiker, M., & Wagner, A. (2015). A framework for an adaptive thermal heat balance model (ATHB). Building and Environment, 94(P1), 252–262. doi:10.1016/j.buildenv.2015.08.018
   Web of Science ®Google Scholar
• Schweiker, M., & Wagner, A. (2016a). Exploring potentials and limitations of the adaptive thermal heat balance framework. Proceedings of 9th Windsor conference: Making comfort relevant, Windsor, UK.
   Google Scholar
• Schweiker, M., & Wagner, A. (2016b). The effect of occupancy on perceived control, neutral temperature, and behavioral patterns. Energy and Buildings, 117, 246–259. doi:10.1016/j.enbuild.2015.10.051
   Web of Science ®Google Scholar
• Shipworth, D., Huebner, G., Schweiker, M., & Kingma, B. R. (2016). Diversity in thermal sensation: Drivers of variance and methodological artefacts. Proceedings of 9th Windsor conference: Making comfort relevant, Windsor, UK, Conference: Making Comfort Relevant (pp. 1–17). Windsor, UK: NCEUB.
   Google Scholar
• Shove, E., Chappells, H., Lutzenhiser, L., & Hackett, B. (2008). Comfort in a lower carbon society. Building Research & Information, 36(4), 307–311. doi: 10.1080/09613210802079322
   Web of Science ®Google Scholar
• Van Hoof, J. (2008). Forty years of Fanger’s model of thermal comfort: Comfort for all? Indoor Air, 18(3), 182–201. JOUR. doi: 10.1111/j.1600-0668.2007.00516.x
   PubMed Web of Science ®Google Scholar
• Wolf, S., Schweiker, M., Wagner, A., & van Treeck, C. (2015). Revisiting validation methods of occupant behaviour models. In M. Loomans & M. te Kulve (Eds.), Proceedings of the healthy buildings Europe, 1–8. Eindhoven.
   Google Scholar
• Yao, R., Li, B., & Liu, J. (2009). A theoretical adaptive model of thermal comfort: Adaptive predicted mean vote (aPMV). Building and Environment, 44, 2089–2096. doi:10.1016/j.buildenv.2009.02.014
   Web of Science ®Google Scholar