Real-Time Intelligent Thermal Comfort Prediction Model

Abstract

Real-time prediction model of indoor thermal comfort depending on Momentum Back Propagation (MBP) function is established by using Arduino hardware and mobile application. The air temperature indoor, air velocity, and relative humidity are gathered via temperature sensor and transferred via Bluetooth to the mobile application to predicate thermal comfort. A significant challenge in designing MBP is to decide the best architecture and parameters as the number of layers and nodes, and number of epochs for the network given the data for the AI issues. These parameters are usually selected on heuristic and fine-tuned manually, which could be as boring as the performance assessment may take hours to test the output of a single MBP parameterization. This paper tends to the issue of determining appropriate parameters for the MBP by applying chicken swarm optimization (CSO) algorithm. The CSO algorithm simulates the chicken swarm searching for the best parameter employs the Fitness function of these parameters which yielding minimum error and high accuracy. The proposed accuracy approximately equals 98.3% when using the best parameters obtained from Chicken Swarm Optimization (CSO). The proposed methodology performance is assessed on the collected dataset from weather archive and in the context of thermal comfort prediction, that mapping relations between the indoor features and thermal index.