In the contemporary era, a substantial portion of global energy consumption is allocated to residential and office buildings. Regrettably, a considerable amount of this energy is squandered due to inefficient utilization of electrical systems. One of the recognized approaches to curbing this wastage involves the detection, learning, and prediction of user presence within buildings, enabling proactive measures based on these forecasts.

Occupancy Prediction in Buildings: State of the Art and Future Directions

Irfanullah Khan;Emilio Greco;Antonio Guerrieri;Giandomenico Spezzano
2023

Abstract

In the contemporary era, a substantial portion of global energy consumption is allocated to residential and office buildings. Regrettably, a considerable amount of this energy is squandered due to inefficient utilization of electrical systems. One of the recognized approaches to curbing this wastage involves the detection, learning, and prediction of user presence within buildings, enabling proactive measures based on these forecasts.
2023
Istituto di Calcolo e Reti ad Alte Prestazioni - ICAR
978-3-031-42194-5
Internet of Things, Smart buildings, Smart environments, Occupancy prediction, Machine learning, Deep learning, Energy efficiency, Federated learning, Sensors, Radars
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/492101
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