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

Table 24

Residual statistics for a DOE large office building depending on different models.

Residual statistics^a
		Minimum	Maximum	Mean	Std. Deviation	N
Modified model^b	Predicted value	−47685040.00	48544072.00	5299326.20	8881896.171	20,350
	Residual	−35572376.000	136128544.000	0.000	12515413.336	20,350
	Std. predicted value	−5.965	4.869	0.000	1.000	20,350
	Std. residual	−2.842	10.874	0.000	1.000	20,350
New model^c	Predicted value	−5426438.00	19135408.00	1919966.69	3352476.356	4807
	Residual	−19135408.000	73064632.000	0.000	6504633.271	4807
	Std. predicted value	−2.191	5.135	0.000	1.000	4807
	Std. residual	−2.939	11.223	0.000	0.999	4807
Limited model^d	Predicted value	−198986816.00	346896704.00	63958680.44	93151196.759	20,350
	Residual	−280478208.000	2066232704.000	0.000	178671928.270	20,350
	Std. predicted value	−2.823	3.037	0.000	1.000	20,350
	Std. residual	−1.570	11.562	0.000	1.000	20,350

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