Machine Learning Techniques Towards Efficient Occupancy Detection for Industrial Urban Spaces

The occupant’s behavior and randomness produce erroneous findings in respect to usage of resources. There are situations when the correct figures for building occupancy are estimated incorrectly. These differences are only due to erroneous occupancy estimates in buildings. The level of accuracy of occupancy detection can be improved in order to reduce the error rate between reality and prediction. Various research investigations on occupancy have been undertaken, according to a large volume of literature. There are still a lot of differences between the techniques used for detection to justify what has been identified till now in various scientific documents. In occupancy domain, several different approaches and scientific reports have been released. Various analytical and data-dependent machine-learning strategies for predicting occupancy prediction are presented in the current study work. Furthermore, this review study provides theoretical direction for building design and occupancy detection.