  Net load forecasting is crucial for energy planning and facilitating informed
decision-making regarding trade and load distributions. However, evaluating
forecasting models' performance against benchmark models remains challenging,
thereby impeding experts' trust in the model's performance. In this context,
there is a demand for technological interventions that allow scientists to
compare models across various timeframes and solar penetration levels. This
paper introduces a visual analytics-based application designed to compare the
performance of deep-learning-based net load forecasting models with other
models for probabilistic net load forecasting. This application employs
carefully selected visual analytic interventions, enabling users to discern
differences in model performance across different solar penetration levels,
dataset resolutions, and hours of the day over multiple months. We also present
observations made using our application through a case study, demonstrating the
effectiveness of visualizations in aiding scientists in making informed
decisions and enhancing trust in net load forecasting models.
