# Input Modeling and Uncertainty Quantification for Improving Volatile Residential Load Forecasting

## TL;DR

This paper introduces a two-stage approach incorporating input modeling and uncertainty quantification to enhance residential load forecasting accuracy and reliability, addressing input errors often neglected by existing methods.

## Contribution

It proposes a novel two-stage framework that models input errors and propagates their impact into load forecasts, improving accuracy and interval coverage.

## Key findings

- Outperforms existing methods in predictive accuracy
- Provides better interval coverage for load forecasts
- Uses global sensitivity analysis for efficiency

## Abstract

Load forecasting has long been recognized as an important building block for all utility operational planning efforts. Over the recent years, it has become ever more challenging to make accurate forecasts due to the proliferation of distributed energy resources, despite the abundance of existing load forecasting methods. In this paper, we identify one drawback suffered by most load forecasting methods: neglect to thoroughly address the impact of input errors on load forecasts. As a potential solution, we propose to incorporate input modeling and uncertainty quantification to improve load forecasting performance via a two-stage approach. The proposed two-stage approach has the following merits. (1) It provides input modeling and quantifies the impact of input errors, rather than neglecting or mitigating 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 to improve 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 in terms of both point predictive accuracy and coverage ability of the predictive intervals.