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

Table 11

Residual summary for a DOE medium office building depending on time-lag.

ANOVA^a
Model		Sum of squares	df	Mean square	F	Sig.
Time-lag0	Regression	1384242517670826240.000	11	125840228879166016.000	750.955	0.000^b
	Residual	3408445076552884200.000	20,340	167573504255304.030	—	—
	Total	4792687594223710200.000	20,351	—	—	—
Time-lag1	Regression	1383124549966582780.000	11	125738595451507520.000	750.070	0.000^b
	Residual	3409546418166475800.000	20,339	167635892529941.300	—	—
	Total	4792670968133058600.000	20,350	—	—	—
Time-lag2	Regression	1605570375500446210.000	11	145960943227313184.000	931.428	0.000^b
	Residual	3187100390118337000.000	20,338	156706676670190.620	—	—
	Total	4792670765618783200.000	20,349	—	—	—

^aDependent variable: heating load of DOE medium 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.