Electric Load Data Compression and Classification Based on Deep Stacked Auto-Encoders

Xiaoyao Huang, Tianbin Hu, Chengjin Ye, Guanhua Xu, Xiaojian Wang and Liangjin Chen
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Xiaoyao Huang: State Grid Zhejiang Electric Power Corporation, Hangzhou 310007, China
Tianbin Hu: School of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China
Chengjin Ye: College of electric engineering, Zhejiang University, Hangzhou 310027, China
Guanhua Xu: College of electric engineering, Zhejiang University, Hangzhou 310027, China
Xiaojian Wang: State Grid Zhejiang Electric Power Corporation, Hangzhou 310007, China
Liangjin Chen: State Grid Zhejiang Electric Power Corporation, Hangzhou 310007, China

Energies, 2019, vol. 12, issue 4, 1-17

Abstract: With the development of advanced metering infrastructure (AMI), electrical data are collected frequently by smart meters. Consequently, the load data volume and length increase dramatically, which aggravates the data storage and transmission burdens in smart grids. On the other hand, for event detection or market-based demand response applications, load service entities (LSEs) want smart meter readings to be classified in specific and meaningful types. Considering these challenges, a stacked auto-encoder (SAE)-based load data mining approach is proposed. First, an innovative framework for smart meter data flow is established. On the user side, the SAEs are utilized to compress load data in a distributed way. Then, centralized classification is adopted at remote data center by softmax classifier. Through the layer-wise feature extracting of SAE, the sparse and lengthy raw data are expressed in compact forms and then classified based on features. A global fine-tuning strategy based on a well-defined labeled subset is embedded to improve the extracted features and the classification accuracy. Case studies in China and Ireland demonstrate that the proposed method is more capable to achieve the minimum of error and satisfactory compression ratios (CR) than benchmark compressors. It also significantly improves the classification accuracy on both appliance and house level datasets.

Keywords: big data; smart meter; compression; classification; deep learning; Stacked Auto-Encoder (SAE) (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 (8)

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