Development of a Python-based algorithm for adaptive thermal comfort: Theoretical formulation and application cases

¹ Department of Electrical Engineering, University of Carlos III, Madrid, Spain, dsanchez7@us.es
² Department of Building Construction, University of Granada, Granada, Spain, dbienvenido@ugr.es
³ Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan, jpulido@g.ecc.u-tokyo.ac.jp
⁴ Department of Building Construction II, University of Sevilla, Sevilla, Spain, carlosrubio@us.es

Abstract

Adaptive thermal comfort has gained momentum within the scientific community as a cost effective and affordable way of maintaining acceptable levels of comfort in dwellings while abating energy expenditure. At the moment two international standards, namely the European EN16798-1 and the American ASHRAE55-2010 shape the understanding of adaptive comfort around the world. However, in recent years concerns have raised about whether they can accurately represent comfort conditions considering the cultural and societal background of different countries, and whether adaptive thermal comfort will be still feasible in future scenarios of climate change. Considering these challenges, this study presents an algorithm which can model different adaptive comfort models; additionally, it can be implemented into energy simulation engines and therefore used to predict energy consumption under different climates, building typologies, and dynamic comfort conditions. This contribution presents the development of the aforementioned algorithm, called ACCIS (Adaptive-Comfort-Control-Implementation Script), originally written in EnergyPlus Runtime Language (ERL) and later nested in a Python package called ACCIM (Adaptive-Comfort-Control-Implemented Model)”, its main characteristics, and also the implementation into two cases studies: The thermal comfort in social dwellings in Spain and Japan considering present and future climate change scenarios namely Representative Concentration Pathways (RCP) 2.6, 4.5 and 8.5 for years 2050, 2080 and 2100. The results show that the predicted energy consumption of low-income families is strongly influenced by the adaptive comfort model chosen to model their thermal routine and suggest that international standards should be put under revision to consider the local particularities of dwellers in subsidized housing projects. The results of this research can be useful to devise public policies aimed at abating energy cost for low-income dwellers that benefit from social housing programs, particularly in the light of the increment of energy costs for heating and cooling associated with climate change..