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

Table 19

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

ANOVA^a
Model		Sum of squares	df	Mean square	F	Sig.
Time-lag0^b	Regression	218113322176116700000.000	11	19828483834192430000.000	663.260	0.000^b
	Residual	608074622833789500000.000	20,340	29895507513952288.000	—	—
	Total	826187945009906200000.000	20,351	—	—	—
Time-lag1^b	Regression	219791177256880500000.000	11	19981016114261864000.000	670.178	0.000^b
	Residual	606396700934593400000.000	20,339	29814479617217832.000	—	—
	Total	826187878191473900000.000	20,350	—	—	—
Time-lag2^b	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 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, horizontal infrared radiation, and dew point temperatures.