Abstract
AbstractThe different thermal comfort indices such as Predictive Mean Vote (PMV), Standard Effective Temperature (SET), and Thermal Sensations (TS) have been used to predict occupants’ thermal comfort in a building. The advances in the machine learning approach help overcome the challenges of predicting current traditional thermal indices in a real-time environment. The different indices have different types of data samples (continuous/labelled). Therefore, while considering the machine learning technique in developing the models of the predictive thermal indices, it is essential to select the vital features, the proper learning type, the algorithm, and the evaluation method to establish the models of the predictive thermal comfort. The main focus of this paper is on the development of the ML model and the evaluation technique that helps in selecting the best model in predicting the thermal indices. This work proposes the new neighbourhood-component-analysis Bayesian-optimization-algorithm-based artificial-neural-network to develop a predictive model for the thermal indices. Here, we have proposed a regression-based model to predict PMV, SET and a classification-based model to predict 7-point TS. The statistical-testing results specify that the ANN model's performance is highly accurate and more reliable in predicting the thermal perception in a real-time environment. The performance of the selected model is validated using subjective measures. This prediction leads to the pre-emptive control of the setpoint temperature of the air-conditioning unit, hence resulting in energy efficiency and comfort.
Highlights
While considering the energy consumption of a building, maintaining good thermal comfort in an indoor environment plays a significant role (Petidis et al 2018)
The main highlights of this research are (1) considered the NCA feature selection method to find the best suitable features automatically in developing the comfort models (2) considering the Bayesian optimization algorithm (BOA) in optimizing the hyper-parameters, which helps in selecting the best parameters to improve the model performance (3) proper evaluation method to estimate the performance (e) estimate in a real-time environment to predict the thermal comfort of the group of occupants present in an indoor environment iv) decide at the proper setpoint selection with this prediction
This section delineates the development of the Machinelearning model, including data collection, data preprocessing, feature selection, Bayesian hyper-parameter optimization-based model development, and statistical evaluation method for both the regression (PMV, Standard Effective Temperature (SET)) and the classification (TS) models to select the best model for the prediction of the thermal comfort for the occupants
Summary
While considering the energy consumption of a building, maintaining good thermal comfort in an indoor environment plays a significant role (Petidis et al 2018). The feature selection and the hyper-parameter optimization technique are getting crucial attention in developing the machine-learning model due to their ability to reduce the complexity and improve the model's performance (Probst et al 2019). The main highlights of this research are (1) considered the NCA feature selection method to find the best suitable features automatically in developing the comfort models (2) considering the BOA in optimizing the hyper-parameters, which helps in selecting the best parameters to improve the model performance (3) proper evaluation method to estimate the performance (e) estimate in a real-time environment to predict the thermal comfort of the group of occupants present in an indoor environment iv) decide at the proper setpoint selection with this prediction
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