Leveraging socioeconomic information and deep learning for residential load pattern prediction

Wen-Jun Tang, Xian-Long Lee, Hao Wang, Hong Tzer Yang

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

5 Citations (Scopus)


Advanced metering infrastructure systems record a high volume of residential load data, opening up an opportunity for utilities to understand consumer energy consumption behaviors. Existing studies have focused on load profiling and prediction, but neglected the role of socioeconomic characteristics of consumers in their energy consumption behaviors. In this paper, we develop a prediction model using deep neural networks to predict load patterns of consumers based on their socioeconomic information. We analyze load patterns using the K-means clustering method and use an entropy-based feature selection method to select the key socioeconomic characteristics that affect consumers' load patterns. Our prediction method with feature selection achieves a higher prediction accuracy compared with the benchmark schemes, e.g. 80% reduction in the prediction error.

Original languageEnglish
Title of host publicationProceedings of 2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe)
EditorsVirgil Dumbrava
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages5
ISBN (Electronic)9781538682180, 9781538682173
ISBN (Print)9781538682197
Publication statusPublished - 2019
Externally publishedYes
EventIEEE/PES Innovative Smart Grid Technologies Europe 2019 - Bucharest, Romania
Duration: 29 Sep 20192 Oct 2019
https://ieeexplore.ieee.org/xpl/conhome/8892271/proceeding (Proceedings)


ConferenceIEEE/PES Innovative Smart Grid Technologies Europe 2019
Abbreviated titleISGT Europe 2019
Internet address


  • Advanced Metering Infrastructure
  • Clustering
  • Deep Neural Network
  • Feature Selection
  • Load Pattern

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