Input modeling and uncertainty quantification for improving volatile residential load forecasting

Guangrui Xie, Xi Chen, Yang Weng

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Residential load forecasting has been playing an increasingly important role in operation and planning of power systems. Over the recent years, accurate forecasts of individual loads have become ever more challenging due to the proliferation of distributed energy resources. This paper identifies and verifies the opportunity of improving load forecasting performance by incorporating suitable input modeling and uncertainty quantification, and proposes a two-stage approach that enjoys the following features. (1) It provides input modeling and quantifies the impact of input errors, rather than neglect or mitigate the impact—a prevalent practice of existing methods. (2) It propagates the impact of input errors into the ultimate point and interval predictions for the target customer's load for improved predictive performance. (3) A variance-based global sensitivity analysis method is further proposed for input-space dimensionality reduction in both stages to enhance the computational efficiency. Numerical experiments show that the proposed two-stage approach outperforms competing load forecasting methods with respect to both point predictive accuracy and coverage ability of the predictive intervals achieved.

Original languageEnglish (US)
Article number119007
JournalEnergy
Volume211
DOIs
StatePublished - Nov 15 2020

Keywords

  • Deep neural networks
  • Gaussian processes
  • Input modeling
  • Load forecasting
  • Renewable integration
  • Sensitivity analysis
  • Uncertainty quantification

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Pollution
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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