Analytical Modelling of HVAC-IoT Systems with the Aid of UVGI and Solar Energy Harvesting
Shafeeq Ahmad, Md Toufique Alam, Mohammad Bilal, Osama Khan, Mohd Zaheen Khan
Abstract
The current research provides a novel arrangement of HVAC-IoT, taking into account the difficulty and variety of building’s internal situations during COVID-19. To monitor and identify indoor air pollutant indicators, smart systems fitted with the Internet of Things, sensors, and solar energy are used. Furthermore, this investigation uncovers the concept of anticipatory maintenance in a building configuration that catches potential ventilation-based breakdown caused due to atmospheric changes. This study investigates a unique methodology that takes into account numerous datasets based on variable environmental circumstances while simultaneously offering suggestions for preventive analytics in buildings throughout the COVID-19 phase, hence creating a straightforward and effective manufacturing technique. With the help of sophisticated hybrid prediction approaches like RSM, the heat burden and particle removal parameters can be validated. The model efficiency for heat load and ventilation was 96% and 98%. The input factors studied are air conditions, and the desired output result is the heat burden and pollution particle removal efficiency. For both production systems, the minimum error rate was determined for trapezoidal membership functions.