Data consistency method of heterogeneous power IOT based on hybrid model. (November 2021)

• Record Type: Journal Article
• Authors: Jiang, Haoyu; Chen, Kai; Ge, Quanbo; Xu, Jinqiang; Fu, Yingying; Li, Chunxi
• Is Part Of: ISA transactions. Volume 117(2021)
• Page Start: 172
• Page End: 179
• Publication Date: 2021-11
• Subjects: Power IOT system -- Hybrid model -- Heterogeneous data consistency -- Machine learning combination method; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2021.01.056
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: The data of the power Internet of Things (IOT) system is transferred from the IaaS layer to the SaaS layer. The general data preprocessing method mainly solves the problem of big data anomalies and missing at the PaaS layer, but it still lacks the ability to judge the high error data that meets the timing characteristics, making it difficult to deal with heterogeneous power inconsistent issues. This paper shows this phenomenon and its physical mechanism, showing the difficulty of building a quantitative model forward. A data-driven method is needed to form a hybrid model to correct the data. The research object is the electricity meter data on both sides of a commercial building transformer, which comes from different power IOT systems. The low-voltage side was revised based on the high-voltage side. Compared with the correction method based on purely using neural networks, the combined method, Linear Regression (LS) + Differential Evolution (DE) + Extreme Learning Machine (ELM), further reduces the deviation from approximately 4% to 1%. Highlights: The heterogeneous electrical data from complex IOT system is inconsistent. The positive mechanism of inconsistency has been revealed, but not easy to correct. Reverse data correction used a combination of simple methods and the effect is better.