Cognitive intelligent duct systems for providing cooling comfort as per the climate

The presented research is intended to provide cognitive intelligence to the duct system of a building so that it can cool the building as per the recent temperature of the atmosphere. Providence of intelligence by a feature selection algorithm to select the criteria for increasing or decreasing the temperature, to find the optimal temperature by a gradient descent algorithm, is being proposed. Traditional HVAC (heating, ventilation, and air conditioning) systems often rely on a one-size-fits-all approach to temperature control, lack the granularity to optimize comfort and energy efficiency in buildings with varying thermal demands. This paper proposes an investigation into intelligent duct systems (IDS) that utilize environmental sensors and machine learning algorithms to dynamically manage temperature within a building on a zone-by-zone basis. The research will explore the potential benefits of such a system, including improved energy efficiency, personalized comfort control, and reduced environmental impact. By analyzing data from sensors and adjusting airflow through controllable dampers, these systems offer the potential for improved thermal comfort and reduced energy consumption. We examine the components, operation, and benefits of IDS.