NDLI: Application of ANN for the prediction of building energy consumption at different climate zones with HDD and CDD

Content Provider	IEEE Xplore Digital Library
Author	Yan Cheng-wen Yao Jian
Copyright Year	2010
Description	Author affiliation: Faculty of Architectural, Civil Engineering and Environment, Ningbo University, Ningbo, China (Yan Cheng-wen; Yao Jian)
Abstract	In order to find a fast and effective method to predict building energy consumption at different climate zones, this paper used artificial neural network (ANN) for this prediction with 20 input parameters, including 18 building envelope performance parameters, heating degree day (HDD) and cooling degree day (CDD). A backpropagation neural network has been preferred and the data have been presented to network by being normalized. Application studies of seven cases were carried out with ANN and conventional methods. Results show that ANN predicts building energy consumption easily and quickly, which gives satisfactory results with successful prediction rate of over 96%, compared with conventional methods, and the application of this ANN is extended largely to be used in other climate zones with the consideration of HDD and CDD as parts of input parameters.
File Size	543133
File Format	PDF
ISBN	9781424458219
e-ISBN	9781424458240
DOI	10.1109/ICFCC.2010.5497626
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-05-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Backpropagation Energy consumption Solid modeling Enery consumption Cooling Buildings Artificial neural networks Predictive models Windows Electronic mail Building envelope Artificial Neural Network (ANN) Climate zone Civil engineering
Content Type	Text
Resource Type	Article

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