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

Table 6

Model summary for a DOE small office building depending on different models.

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
Modified model^b	0.592^b	0.350	0.350	7116200.437	0.350	996.953	11	20,338	0.000	0.778
New model^c	0.422^c	0.178	0.176	3742573.281	0.178	130.924	8	4845	0.000	1.509
Limited model^d	0.513^d	0.264	0.263	7575212.077	0.264	1213.784	6	20,343	0.000	0.767

^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; ^cpredictors: (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; ^dpredictors: (constant), wind speed, dry bulb temperatures, relative humidity, global horizontal radiation, atmospheric station pressure, and dew point temperatures.