Thermostat/Hygrometer vs ANN-Based Predictive/Adaptive Environmental Control Strategies

This study tested the feasibility of employing artificial neural network (ANN)-based predictive and adaptive control logics to improve thermal comfort and energy efficiency through a decrease in over- and under-shooting of control variables. Three control logics were developed: (1) conventional temperature/humidity control logic, (2) ANN-based temperature/humidity control logic, and (3) ANN-based Predicted Mean Vote (PMV) control logic.

Assessing Adaptive Thermal Comfort Using Artificial Neural Networks in Naturally-Ventilated Buildings

This paper presents a method for predicting occupants’ indoor thermal sensation in naturally-ventilated environments, based on real thermal sensation samples, using a GA-BP neural network model. This method improves the traditional back propagation neural network by incorporating an integrated genetic algorithm into the BP neutral network which aims to optimise the connection weight or threshold of the parameters in the input layer of the GA-BP neutral network model, which represent the factors affecting adaptive thermal comfort.