Cost effective indoor HVAC energy efficiency monitoring based on intelligent decision support system under fermatean fuzzy framework.

The Heating, Ventilation, and Air Conditioning (HVAC) control system is in charge of the building's energy efficiency. Indoor energy consumption trends can be intelligently monitored and minimized. Occupancy data is essential for saving a significant amount of energy. This energy footprint can play an important part in modern smart buildings to improve indoor green environments while lowering costs. Traditional energy monitoring and control systems can be enhanced by installing an occupancy monitoring system, which consists of a network of sensors and cameras. In this paper, we offer a novel and innovative Convolutional Neural Network (CNN) based on real-time camera occupancy detection and recognition algorithms across various types of sensors, which provides realistic low-cost energy-saving solutions with robust Graphical Processing Units (GPUs). Decisionmaking tools can be used to select the appropriate occupancy detection and recognition alternative for indoor environment and energy monitoring and management. In this research work, we develop the\Fermatean Fuzzy Prioritized Weighted Average (FFPWA) operator and Fermatean Fuzzy Prioritized Weighted Geometric (FFPWG) operator". In the end, we give an algorithm for an Intelligent Decision Support System (IDSS) using proposed Aggregation Operators (AOs) to compare our CNN based method with other existing sensors techniques. [ABSTRACT FROM AUTHOR]