Autoregressive model for individual consumption data - Sparsity recovery and significance test

Pan Li, Baosen Zhang, Yang Weng, Ram Rajagopal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Understanding consumer flexibility and behavior patterns is becoming increasingly vital to the design of robust and efficient energy saving programs. Accurate prediction of consumption is a key part to this understanding. Existing prediction methods usually have high relative errors that can be larger than 30%. In this paper, we explore sparsity in users' past data and relationship between different users to increase prediction accuracy. We show that using LASSO and Granger Causality techniques, prediction accuracy can be significantly improved in comparison to existing algorithms. We use mean absolute percentage error (MAPE) as the criteria.

Original languageEnglish (US)
Title of host publication2016 IEEE Power and Energy Society General Meeting, PESGM 2016
PublisherIEEE Computer Society
Volume2016-November
ISBN (Electronic)9781509041688
DOIs
StatePublished - Nov 10 2016
Externally publishedYes
Event2016 IEEE Power and Energy Society General Meeting, PESGM 2016 - Boston, United States
Duration: Jul 17 2016Jul 21 2016

Other

Other2016 IEEE Power and Energy Society General Meeting, PESGM 2016
CountryUnited States
CityBoston
Period7/17/167/21/16

Keywords

  • Granger causality
  • LASSO
  • Load forecasting
  • Prediction of consumption
  • Sparsity

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Nuclear Energy and Engineering
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering

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  • Cite this

    Li, P., Zhang, B., Weng, Y., & Rajagopal, R. (2016). Autoregressive model for individual consumption data - Sparsity recovery and significance test. In 2016 IEEE Power and Energy Society General Meeting, PESGM 2016 (Vol. 2016-November). [7741126] IEEE Computer Society. https://doi.org/10.1109/PESGM.2016.7741126