Two approaches for synthesizing scalable residential energy consumption data

Xiufeng Liu*, Nadeem Iftikhar, Huan Huo, Rongling Li, Per Sieverts Nielsen

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Many fields require scalable and detailed energy consumption data for different study purposes. However, due to privacy issues, it is often difficult to obtain sufficiently large datasets. This paper proposes two different methods for synthesizing fine-grained energy consumption data for residential households, namely a regression-based method and a probability-based method. They each use a supervised machine learning method, which trains models with a relatively small real-world dataset and then generates large-scale time series based on the models. This paper describes the two methods in details, including data generation process, optimization techniques, and parallel data generation. This paper evaluates the performance of the two methods, which compare the resulting consumption profiles with real-world data, including patterns, statistics, and parallel data generation in the cluster. The results demonstrate the effectiveness of the proposed methods and their efficiency in generating large-scale datasets.
Original languageEnglish
JournalFuture Generation Computer Systems
Pages (from-to)586-600
Publication statusPublished - 2019


  • Energy consumption
  • Time series
  • Synthesize
  • Simulation
  • Data generation


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