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

Table 22

Model summary for a DOE large 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	0.579^b	0.335	0.335	12518489.662	0.335	1024.355	10	20,339	0.000	0.678
New model^c	0.458^c	0.210	0.209	6510053.800	0.210	159.315	8	4798	0.000	1.227
Limited model^d	0.462^d	0.214	0.213	178702667.526	0.214	789.878	7	20,342	0.000	0.684

^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, and horizontal infrared radiation; ^cpredictors: (constant), precipitable water, direct normal radiation, total sky cover, AOD, atmospheric station pressure, dew point temperatures, dry bulb temperatures, and horizontal infrared radiation; ^dpredictors: (constant), total sky cover, wind speed, dry bulb temperatures, global horizontal radiation, relative humidity, atmospheric station pressure, and dew point temperatures.