Deep Learning-Based Socio-Demographic Information Identification From Smart Meter Data
Smart meters provide large amounts of data and the value of this data is getting increased attention because a better understanding of the characteristics of consumers helps utilities and retailers implement more effective demand response programs and more personalized services. This paper investiga...
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Veröffentlicht in: | IEEE transactions on smart grid 2019-05, Vol.10 (3), p.2593-2602 |
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creator | Wang, Yi Chen, Qixin Gan, Dahua Yang, Jingwei Kirschen, Daniel S. Kang, Chongqing |
description | Smart meters provide large amounts of data and the value of this data is getting increased attention because a better understanding of the characteristics of consumers helps utilities and retailers implement more effective demand response programs and more personalized services. This paper investigates how such characteristics can be inferred from fine-grained smart meter data. A deep convolutional neural network (CNN) first automatically extracts features from massive load profiles. A support vector machine then identifies the characteristics of the consumers. Comprehensive comparisons with state-of-the-art and advanced machine learning techniques are conducted. Case studies on an Irish dataset demonstrate the effectiveness of the proposed deep CNN-based method, which achieves higher accuracy in identifying the socio-demographic information about the consumers. |
doi_str_mv | 10.1109/TSG.2018.2805723 |
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subjects | Artificial neural networks big data classification Consumers Convolutional neural network (CNN) Data mining Data visualization Deep learning Demographics Encoding Feature extraction Machine learning Measuring instruments Neural networks smart meter Smart meters socio-demographic information Sociodemographics support vector machine (SVM) Support vector machines Training Utilities |
title | Deep Learning-Based Socio-Demographic Information Identification From Smart Meter Data |
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