A hybrid ensemble learning approach for indoor thermal comfort predictions utilizing the ASHRAE RP-884 database

Traditional Heating, Ventilation and Air-conditioning systems operate on a fixed schedule, regardless of occupancy or external temperature. With the rise of smart buildings, building managers and owners are seeking ways to reduce energy consumption while maintaining occupant comfort. There are various environmental and personal factors that impact thermal comfort levels.

In this paper, we aim to develop a machine learning-based approach for precisely forecasting the thermal comfort levels of building occupants using the readily available ASHRAE RP-884 database. The dataset was first pre-processed using a k Nearest Neighbor (kNN)-based imputation method. Then, the RRelifF algorithm was used to select highly relevant and practically retrievable input features. Based on the selected features, four ensemble models were created to assess the relationship between input features and thermal comfort levels. A decision-making rule was employed to determine the credibility of each predictor, with only credible outputs selected. The credible outputs were aggregated to produce the final predicted mean vote (PMV) using Genetic Algorithm (GA) optimized coefficients. The testing results were promising, exhibiting high accuracy and a low Root Mean Squared Error (RMSE) value of 0.157 in the prediction of PMV.