A method to predict cooling load of large commercial buildings based on weather forecast and internal occupancy

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...

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Bibliographic Details
Published inFrontiers in Energy Vol. 10; no. 4; pp. 459 - 465
Main Authors JIA, Junbao, XING, Jincheng, LING, Jihong, PENG, Ren
Format Journal Article
LanguageEnglish
Published Beijing Higher Education Press 01.12.2016
Springer Nature B.V
Subjects
Accuracy
Air conditioning
Buildings
Climatology
commercial building
Commercial buildings
Cooling
Cooling loads
Cooling systems
Demand
Density
Electricity
Energy
Energy consumption
Energy storage
Energy Systems
Facilities
Feedback
Humidity
Load
load prediction
Mathematical models
multiple linear regression
Radiation
Regression
Regression analysis
Research Article
Studies
Time series
Weather
Weather forecasting
制冷负荷
占用
商业建筑
多元线性回归
天气预报
平均相对误差
模型预测
负荷预测
China
commercial building
multiple linear regression
load prediction
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Summary: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.
Bibliography:commercial building, load prediction, multi-ple linear regression
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.
11-6017/TK
Document received on :2015-08-23
load prediction
Document accepted on :2015-11-06
commercial building
multiple linear regression
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:2095-1701
2095-1698
DOI:10.1007/s11708-016-0424-8