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

Table 13

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

Residual statistics^a
		Minimum	Maximum	Mean	Std. deviation	N
Time-lag0^b	Predicted value	−59638412.00	34621280.00	5299548.66	8247327.016	20,352
	Residual	−31375230.000	142983696.000	0.000	12941519.557	20,352
	Std. predicted value	−7.874	3.555	0.000	1.000	20,352
	Std. residual	−2.424	11.045	0.000	1.000	20,352
Time-lag1^b	Predicted value	−53244212.00	40414716.00	5299348.31	8244198.466	20,351
	Residual	−28412308.000	145224064.000	0.000	12943928.250	20,351
	Std. predicted value	−7.101	4.259	0.000	1.000	20,351
	Std. residual	−2.194	11.216	0.000	1.000	20,351
Time-lag2^b	Predicted value	−47442988.00	48511468.00	5299326.20	8882662.010	20,350
	Residual	−35604016.000	136248016.000	0.000	12514869.803	20,350
	Std. predicted value	−5.938	4.865	0.000	1.000	20,350
	Std. residual	−2.844	10.884	0.000	1.000	20,350

^aDependent variable: heating load of a 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.