Privacy-Preserving Sensing and Two-Stage Building Occupancy Prediction Using Random Forest Learning

Table 1

Taxonomy of sensing platforms and occupancy methods for space management.

Sensor type	Occupancy method	Experiment duration	Location type	Algorithm	Source
PIR	Presence prediction	50 hours	Several offices	Infinite hidden Markov model	[7]
CO2 sensors + others: light, PIR, acoustic	Occupancy detection	7 days	1 cubicle	Decision trees	[8]
Wi-Fi	Occupancy counting	1 week	2 lecture rooms	Newton Interp. + NN model	[9]
Distributed plug load power strip sensors	Occupancy detection	2 weeks	3 rooms	Bayesian inference, graphical lasso, influence model	[10]
PC23D stereo cameras	Occupancy counting	15 days	4 rooms	PLCount	[11]
PIR + infrared sensor	Occupancy counting	3 weeks	10 building areas	KNN	[12]
Temperature, humidity, light, CO2 and digital camera temperature, motion sensor, RFID tags	Occupancy detection	1 month	1 office	Random Forest, GBM, LDA, CART	[13]
	Occupancy prediction	61 days	5 homes	Mean of nearest past days	[14]
CO2 sensors	Occupancy counting	4 months	2 rooms office and theatre	Seasonal trend decomposition	[15]
Wi-Fi	Occupancy detection	n/a	1 conference room	Random Forests	[16]
PIR matrix	Occupancy detection activity recognition	n/a	Laboratory	Fuzzy background removal	[17]
PIR, CO2, power water, noise	Occupancy prediction	n/a	Office apartment multizone house	Bayesian networks	[18]