Considering the fact that customers of large commercial buildings have the characteristics of the higher density and randomness, this paper presented an air-conditioning cooling load prediction method based on weather forecast and internal occupancy density. The multiple linear feedback regression model was applied to predict, with precision, the air conditioning cooling load. Case analysis showed that the largest mean relative error of hourly and the daily predicting cooling load maximum were 18.1% and 5.14%, respectively.
. [J]. Frontiers in Energy, 2016, 10(4): 459-465.
Junbao JIA,Jincheng XING,Jihong LING,Ren PENG. A method to predict cooling load of large commercial buildings based on weather forecast and internal occupancy. Front. Energy, 2016, 10(4): 459-465.
The value is greater with the nearer of the distance from city center
[1,10]
Commercial intensive index S
*
*
Tab.1
Fig.1
Building envelope
Material
Heat transfer coefficient/(W•m–2•K–1)
External wall
20 mm thick cement mortar 240 mm thick clay hollow block 20 mm thick cement mortar
1.88
Roof
10 mm thick quarry tile 25 mm thick cement mortar 1.5 mm thick polyurethane waterproof coating 20 mm cement mortar screed-coat 200 mm cement vermiculite stone thermal insulation layer 20 mm cement mortar screed-coat 120 mm thick reinforced concrete
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