Thermal comfort is a fundamental goal of architecture aiming at protecting individuals from harsh weather conditions. In Nigeria’s savanna climate zone, such as Kaduna, poor indoor thermal comfort leads to over-reliance on air-conditioning systems. There is limited research on the application of passive design strategies in the Nigerian savanna climate, which creates a barrier to their widespread implementation in residential buildings. In response to the increased awareness of climate change and the need for sustainable design, this study explores the potential of passive design strategies, focusing on the combination of rooftop insulation and reflective materials with mechanical ventilation as a means of improving indoor thermal comfort solutions. This study conducted a 3-day field experiment of typical dwellings in Kaduna, a major city in the Nigerian savanna climate zone. The data collected from this experiment served as the basis for a simulation study using EnergyPlus software, which tested and evaluated 3 different strategies: passive design (roof insulation + reflective materials), mechanical ventilation, and a combination of passive design and mechanical ventilation. This study highlights the potential for passive design strategies to provide a more sustainable, cost-effective solution, reducing dependence on air conditioning while supporting indoor comfort. Additionally, the research methodology and insights gained offer a basis for developing future building codes in Nigeria that emphasize sustainable practices. Such codes would guide architects, builders, and policymakers in designing homes that respond to local climate needs and align with broader sustainability goals. Further research could explore additional passive measures, including advanced window technologies, shading, and natural ventilation, to maximize sustainable residential design potential in tropical savanna climates.

The thermal comfort evaluation of the urban environment arouses widespread concern among scholars, and research in this field is mostly based on thermal comfort evaluation indexes such as PMV, PET, SET, UTCI, etc. These thermal comfort index evaluation models are complex in the calculation process and poor in operability, which makes it difficult for people who lack a relevant knowledge background to understand, calculate, and apply them. The purpose of this study is to provide a simple, efficient, and easy-to-operate outdoor thermal comfort evaluation model for severe cold areas in China using a machine learning method. In this study, the physical environment parameters are obtained by field measurement, and individual information is obtained by a field questionnaire survey. The applicability of four machine learning models in outdoor thermal comfort evaluation is studied. A total of 320 questionnaires are collected. The results show that the correlation coefficients between predicted values and voting values of the extreme gradient lifting model, gradient lifting model, random forest model, and neural network model are 0.9313, 0.7148, 0.9115, and 0.5325, respectively. Further analysis of the extreme gradient model with the highest correlation coefficient shows that individual factors (such as residence time, distance between hometown and residence, clothing, age, height, and weight) and environmental factors (such as air humidity (RH), wind speed (v), air temperature (Ta), and black bulb temperature (Tg)) have different influences on thermal comfort evaluation. In summary, using a machine learning method to evaluate outdoor thermal comfort is simpler, more direct, and more efficient, and it can make up for the lack of consideration of complex individual factors in the evaluation method of thermal comfort index. The results have reference value and application value for the research of outdoor thermal comfort evaluation in severe cold areas of China.