Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/65575
Title: Simplified neural network model design with sensitivity analysis and electricity consumption prediction in a commercial building
Authors: Moon Keun Kim
Jaehoon Cha
Eunmi Lee
Van Huy Pham
Sanghyuk Lee
Nipon Theera-Umpon
Keywords: Energy
Engineering
Mathematics
Issue Date: 28-Mar-2019
Abstract: © 2019 by the authors. With growing urbanization, it has become necessary to manage this growth smartly. Specifically, increased electrical energy consumption has become a rapid urbanization trend in China. A building model based on a neural network was proposed to overcome the difficulties of analytical modelling. However, increased amounts of data, repetitive computation, and training time become a limitation of this approach. A simplified model can be used instead of the full order model if the performance is acceptable. In order to select effective data, Mean Impact Value (MIV) has been applied to select meaningful data. To verify this neural network method, we used real electricity consumption data of a shopping mall in China as a case study. In this paper, a Bayesian Regularization Neural Network (BRNN) is utilized to avoid overfitting due to the small amount of data. With the simplified data set, the building model showed reasonable performance. The mean of Root Mean Square Error achieved is around 10% with respect to the actual consumption and the standard deviation is low, which reflects the model’s reliability. We also compare the results with our previous approach using the Levenberg–Marquardt back propagation (LM-BP) method. The main difference is the output reliability of the two methods. LM-BP shows higher error than BRNN due to overfitting. BRNN shows reliable prediction results when the simplified neural network model is applied.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85065539954&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/65575
ISSN: 19961073
Appears in Collections:CMUL: Journal Articles

Files in This Item:
There are no files associated with this item.


Items in CMUIR are protected by copyright, with all rights reserved, unless otherwise indicated.