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

Table 18

Model summary for a DOE large office building depending on 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	0.514^b	0.264	0.264	172903173.811	0.264	663.260	11	20,340	0.000	0.732
Time-lag1	0.516^b	0.266	0.266	172668699.008	0.266	670.178	11	20,339	0.000	0.761
Time-lag2	0.579^b	0.335	0.335	12518253.739	0.335	931.428	11	20,338	0.000	0.677

^aDependent variable: heating load of a DOE large 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.