Room Occupancy Prediction Using ML

Abstract

Abstract: Now-a-days buildings are growing rapidly and uses large amount of energy. Most of the energy gets wasted on HVAC systems. Occupancy (presence and number of occupants) is one of the most important factors impacting energy efficiency of HVAC systems. Hence, knowing occupancy information is vital for demand driven HVAC controls, that directly impacts on energy-related building control systems. So, occupancy information without privacy issues (like using cameras) gained importance. Recent works are done on this problem. Existing solutions proposed are yes or no classification, estimating for low count etc using sensors data. Using yes or no classification, the energy consumption may not be saved to a great extent. In this work, we have trained a model which classifies the state of room based on number of occupants (empty, low, fair, high). For this we are using data collected by different sensors (co2, humidity, temperature). We are using classification algorithms to train the model. This information could be used to solve energy related problems in buildings

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