A Sparse Linear Model and Significance Test for Individual Consumption Prediction

Pan Li, Baosen Zhang, Yang Weng, Ram Rajagopal

Research output: Contribution to journalArticlepeer-review

38 Scopus citations


Accurate prediction of user consumption is a key part not only in understanding consumer flexibility and behavior patterns, but in the design of robust and efficient energy saving programs as well. Existing prediction methods usually have high relative errors that can be larger than 30% and have difficulties accounting for heterogeneity between individual users. In this paper, we propose a method to improve prediction accuracy of individual users by adaptively exploring sparsity in historical data and leveraging predictive relationship between different users. Sparsity is captured by popular least absolute shrinkage and selection estimator, while user selection is formulated as an optimal hypothesis testing problem and solved via a covariance test. Using real-world data from PG&E, we provide extensive simulation validation of the proposed method against well-known techniques such as support vector machine, principle component analysis combined with linear regression, and random forest. The results demonstrate that our proposed methods are operationally efficient because of linear nature, and achieve optimal prediction performance.

Original languageEnglish (US)
Article number7873345
Pages (from-to)4489-4500
Number of pages12
JournalIEEE Transactions on Power Systems
Issue number6
StatePublished - Nov 2017
Externally publishedYes


  • Load forecasting
  • least absolute shrinkage and selection
  • significance test
  • sparse autoregressive model

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering


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