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

Table 4

ANOVA for a DOE small building depending on time-lag.

ANOVA^a
Model		Sum of squares	df	Mean square	F	Sig.
Time-lag0	Regression	452150586586186300.000	11	41104598780562392.000	737.844	0.000^b
	Residual	1133122624135237120.000	20,340	55709076899470.850	—	—
	Total	1585273210721423360.000	20,351	—	—	—
Time-lag1	Regression	466270598690733180.000	11	42388236244612096.000	770.449	0.000^b
	Residual	1119001925868610430.000	20,339	55017548840582.650	—	—
	Total	1585272524559343620.000	20,350	—	—	—
Time-lag2	Regression	555346313497669060.000	11	50486028499788096.000	996.953	0.000^b
	Residual	1029922597401856130.000	20,338	50640308653842.860	—	—
	Total	1585268910899525120.000	20,349	—	—	—

^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.