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

Energies

Nipon Theera-Umpon¹  MoonKeun Kim²  Sanghyuk Lee³  Jaehoon Cha³  VanHuy Pham⁴  Eunmi Lee⁵ 

[1] Biomedical Engineering Institute, Chiang Mai University, Chiang Mai 50200, Thailand;Department of Architecture, Xi’an Jiatong-Liverpool University, Suzhou 215123, China;Department of Electrical and Electronic Engineering, Xi’an Jiatong-Liverpool University, Suzhou 215123, China;Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam;Social Science Research Institute, Yonsei University, Seoul 03722, Korea;

关键词: energy management;    building modelling;    Bayesian regularization neural network;    simplified model;    mean impact value;   

DOI  :  10.3390/en12071201

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown