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

Table 15

ANOVA for a DOE medium office building depending on different models.

ANOVA^a
Model		Sum of squares	df	Mean square	F	Sig.
Modified model^b	Regression	1548679524700758530.000	10	154867952470075808.000	970.983	0.000^b
	Residual	3243991240918024700.000	20,339	159496103098383.620	—	—
	Total	4792670765618783200.000	20,349	—	—	—
New model^c	Regression	54015103645277112.000	8	6751887955659638.000	159.315	0.000^c
	Residual	203343080683039488.000	4798	42380800475831.500	—	—
	Total	257358184328316608.000	4806	—	—	—
Limited model^d	Regression	1128275925296639100.000	7	161182275042376992.000	894.764	0.000^d
	Residual	3664394840322144300.000	20,342	180139358977590.400	—	—
	Total	4792670765618783200.000	20,349	—	—	—

^aDependent variable: heating load of a DOE small office building; ^bpredictors: (constant), visibility, diffuse radiation, atmospheric station pressure, wind speed, total sky cover, dew point 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.