Title: Design of building energy consumption monitoring model based on parallel cloud computing

Authors: Xiaoju Sun

Addresses: ZhengZhou Shengda University of Economics, Business & Management, Zhengzhou, Henan, China

Abstract: Traditional single-threaded energy consumption monitoring methods for buildings is poor in anti-interference, resulting in relatively high monitoring error rate and low accuracy and hindering practical application of them, so a design scheme of building energy consumption monitoring model based on parallel cloud computing is proposed. In this method, building energy consumption data is collected in parallel cloud computing mode, and the big data mining and characteristic extraction methods are adopted to reconstruct the building energy consumption data characteristics and fuse the parallel collected data; correlation analysis is performed to samples of the fused data, and relevant building energy consumption data is processed with linear fitting, and then the results are output. The simulation results show that when this model is adopted for building energy consumption monitoring, the output bit error converges to 0 if the input signal-to-noise ratio is 6 dB, indicating that the proposed method can provide relatively high accuracy and performs well in anti-interference, so it has certain practical application value.

Keywords: building; energy consumption monitoring; parallel cloud computing mode.

DOI: 10.1504/IJGEI.2020.111174

International Journal of Global Energy Issues, 2020 Vol.42 No.5/6, pp.457 - 469

Received: 01 Apr 2019
Accepted: 23 Feb 2020
Published online: 12 Nov 2020 *

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