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Load forecasting has long been recognized as an important building block forall utility operational planning efforts. Over the recent years, it has becomeever more challenging to make accurate forecasts due to the proliferation ofdistributed energy resources, despite the abundance of existing loadforecasting methods. In this paper, we identify one drawback suffered by mostload forecasting methods: neglect to thoroughly address the impact of inputerrors on load forecasts. As a potential solution, we propose to incorporateinput modeling and uncertainty quantification to improve load forecastingperformance via a two-stage approach. The proposed two-stage approach has thefollowing merits. (1) It provides input modeling and quantifies the impact ofinput errors, rather than neglecting or mitigating the impact, a prevalentpractice of existing methods. (2) It propagates the impact of input errors intothe ultimate point and interval predictions for the target customer's load toimprove predictive performance. (3) A variance-based global sensitivityanalysis method is further proposed for input-space dimensionality reduction inboth stages to enhance the computational efficiency. Numerical experiments showthat the proposed two-stage approach outperforms competing load forecastingmethods in terms of both point predictive accuracy and coverage ability of thepredictive intervals.

Input Modeling and Uncertainty Quantification for Improving Volatile Residential Load Forecasting