Improving sustainable office building operation by using historical data and linear models to predict energy usage

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• 作者：Majeed Safa^a ; ^{Majeed.Safa@lincoln.ac.nz} ; Mahdi Safa^b ; Jeremy Allen^c ; Arash Shahi^d ; Carl T. Haas^e
• 关键词：Energy modelling ; Energy auditing ; Office buildings ; Energy saving ; Artificial neural network ; Linear regression model
• 刊名：Sustainable Cities and Society
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：29
• 期：Complete
• 页码：107-117
• 全文大小：3364 K
• 卷排序：29

文摘

The paper proposes a method that applies multiple linear regression (MLR) and artificial neural network (ANN) models to predict energy usage based on weather conditions and occupancy; thus enabling a comparison of the use of these two types of modelling methods. The models were developed based on the monthly outside temperatures and the number of full-time employees (FTEs). A comparison of the actual and predicted energy consumption revealed that the models can predict energy usage within an acceptable error range. The results also demonstrated that each building should be investigated as an individual unit.