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

Table 7

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

ANOVA^a
Model		Sum of squares	df	Mean square	F	Sig.
Modified model^b	Regression	555346313497669060.000	11	50486028499788096.000	996.953	0.000^b
	Residual	1029922597401856130.000	20,338	50640308653842.860	—	—
	Total	1585268910899525120.000	20,349	—	—	—
New model^c	Regression	14670632319938900.000	8	1833829039992363.000	130.924	0.000^c
	Residual	67863211318381720.000	4845	14006854761275.896	—	—
	Total	82533843638320624.000	4853	—	—	—
Limited model^d	Regression	417909494247049020.000	6	69651582374508168.000	1213.784	0.000^d
	Residual	1167359416652476160.000	20,343	57383838010739.625	—	—
	Total	1585268910899525120.000	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, dry bulb temperatures, direct normal radiation, relative humidity, global horizontal radiation, 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 horizontal radiation, and horizontal infrared radiation; ^dpredictors: (constant), wind speed, dry bulb temperatures, relative humidity, global horizontal radiation, atmospheric station pressure, and dew point temperatures.