Input modeling and uncertainty quantification for improving volatile residential load forecasting

Guangrui Xie, Xi Chen and Yang Weng

Energy, 2020, vol. 211, issue C

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.

Keywords: Load forecasting; Gaussian processes; Deep neural networks; Input modeling; Uncertainty quantification; Sensitivity analysis; Renewable integration (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:211:y:2020:i:c:s0360544220321149

DOI: 10.1016/j.energy.2020.119007

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