Modeling of Artificial Intelligence-Based Automated Climate Control with Energy Consumption Using Optimal Ensemble Learning on a Pixel Non-Uniformity Metro System

Abstract

Climate control in a pixel non-uniformity metro system includes regulating the air, humidity, and temperature quality within metro trains and stations to ensure passenger comfort and safety. The climate control system in a PNU metro system combines intelligent algorithms, energy-efficient practices, and advanced technologies to make a healthy and comfortable environment for passengers while reducing energy consumption. The proposed an automated climate control using an improved salp swarm algorithm with an optimal ensemble learning technique examines the underlying factors, including indoor air temperature, wind direction, indoor air relative humidity, light sensor 1 (wavelength), return air relative humidity, supply air temperature, wind speed, supply air relative humidity, airflow rate, and return air temperature. Moreover, this new proposed technique applies ISSA to elect an optimal set of features. Then, the climate control process takes place using an ensemble learning approach comprising long short-term memory, gated recurrent unit, and recurrent neural network. Lastly, the Harris hawks optimization algorithm can be employed to adjust the hyperparameters related to the ensemble learning models. The extensive results demonstrated the supremacy of the proposed algorithms over other approaches to the climate control process on PNU metro systems.