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Design and Implementation of Large-Scale Public Building Energy Consumption Monitoring Platform Based on BP Neural Network
Author(s) -
Xiancheng Liu,
Congxiang Tian
Publication year - 2021
Publication title -
security and communication networks
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.446
H-Index - 43
eISSN - 1939-0114
pISSN - 1939-0122
DOI - 10.1155/2021/6438909
Subject(s) - computer science , robustness (evolution) , energy consumption , consumption (sociology) , scale (ratio) , real time computing , artificial neural network , the internet , embedded system , computer security , reliability engineering , artificial intelligence , operating system , ecology , social science , biochemistry , chemistry , physics , quantum mechanics , sociology , gene , engineering , biology
With the rapid development of network technology, people are increasingly dependent on the internet. When BP neural network (BNN) performs simulation calculation, it has the advantages of fast training speed, high accuracy, and strong robustness and is widely used in large-scale public (LSP) building energy consumption (BEC) monitoring platforms (LPB). Therefore, the purpose of this paper to study the energy consumption monitoring platform of large public (LP) buildings is to better monitor the energy consumption of public buildings, so as to supplement or remedy at any time. This article mainly uses the data analysis method and the experimental method to carry on the relevant research and the system test to the BNN. The experimental results show that the monitoring system (MS) platform designed in this paper has real-time performance, and its time consumption is between 2 s and 3 s, and the data accords with theory and reality.

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