Simplified Neural Network Model Design with Sensitivity Analysis and Electricity Consumption Prediction in a Commercial Building

Moon Keun Kim, Jaehoon Cha, Eunmi Lee, Pham Van Huy, Sanghyuk Lee and Nipon Theera-Umpon
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Moon Keun Kim: Department of Architecture, Xi’an Jiatong-Liverpool University, Suzhou 215123, China
Jaehoon Cha: Department of Electrical and Electronic Engineering, Xi’an Jiatong-Liverpool University, Suzhou 215123, China
Eunmi Lee: Social Science Research Institute, Yonsei University, Seoul 03722, Korea
Pham Van Huy: Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam
Sanghyuk Lee: Department of Electrical and Electronic Engineering, Xi’an Jiatong-Liverpool University, Suzhou 215123, China
Nipon Theera-Umpon: Biomedical Engineering Institute, Chiang Mai University, Chiang Mai 50200, Thailand

Energies, 2019, vol. 12, issue 7, 1-13

Abstract: 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.

Keywords: energy management; building modelling; Bayesian regularization neural network; simplified model; mean impact value (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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