Predicting the clothing insulation through machine learning algorithms: A comparative analysis and a practical approach

Abstract

Since indoor clothing insulation is a key element in thermal comfort models, the aim of the present study is proposing an approach for predicting it, which could assist the occupants of a building in terms of recommendations regarding their ensemble. For that, a systematic analysis of input variables is exposed, and 13 regression and 12 classification machine learning algorithms were developed and compared. The results are based on data from 3352 questionnaires and 21 input variables from a field study in mixed-mode office buildings in Spain. Outdoor temperature at 6 a.m., indoor air temperature, indoor relative humidity, comfort temperature and gender were the most relevant features for predicting clothing insulation. When comparing machine learning algorithms, decision tree-based algorithms with Boosting techniques achieved the best performance. The proposed model provides an efficient method for forecasting the clothing insulation level and its application would entail optimising thermal comfort and energy efficiency.

Skills of Statistical Learning Algorithms in Thermal Stress Assessment Compared with the Expert Judgement Inherent to the Universal Thermal Climate Index (UTCI)

“…Our approach was to apply selected SL algorithms deeming representative for recent applications [5][6][7][8][9][10][11][35][36][37][38][39][40][41] to the data simulated by the UTCI-Fiala model [27] at stage 2 of the UTCI development (Figure 1).…”

“…Statistical or machine learning (SL) is central to artificial intelligence (AI) applications [1][2][3] with potential relevance to environmental risk assessment, especially in settings with high dimensional input as for thermal stress indices [4]. There, they may assist or even attempt replacing the biometeorological expert judgement, as indicated by the increasing number of recent application studies in this field [5][6][7][8][9][10][11].…”