Development of Regression Models considering Time-Lag and Aerosols for Predicting Heating Loads in Buildings

Table 3

DOE small office model summary considering time-lag.

Model summary^a
Model	R	R square	Adjusted R square	Std. error of the estimate	Change statistics					Durbin–Watson
					R square change	F change	df1	df2	Sig. F change
Time-lag0	0.534^b	0.285	0.285	7463851.345	0.285	737.844	11	20,340	0.000	0.799
Time-lag1	0.542^b	0.294	0.294	7417381.535	0.294	770.449	11	20,339	0.000	0.834
Time-lag2	0.592^b	0.350	0.350	7116200.437	0.350	996.953	11	20,338	0.000	0.778

^aDependent variable: heating load of a DOE small office building; ^bpredictors: (constant), visibility, diffuse radiation, atmospheric station pressure, wind speed, total sky cover, dry bulb temperatures, direct normal radiation, relative humidity, global horizontal radiation, horizontal infrared radiation, and dew point temperatures.