  Accurate net load forecasting is vital for energy planning, aiding decisions
on trade and load distribution. However, assessing the performance of
forecasting models across diverse input variables, like temperature and
humidity, remains challenging, particularly for eliciting a high degree of
trust in the model outcomes. In this context, there is a growing need for
data-driven technological interventions to aid scientists in comprehending how
models react to both noisy and clean input variables, thus shedding light on
complex behaviors and fostering confidence in the outcomes. In this paper, we
present Forte, a visual analytics-based application to explore deep
probabilistic net load forecasting models across various input variables and
understand the error rates for different scenarios. With carefully designed
visual interventions, this web-based interface empowers scientists to derive
insights about model performance by simulating diverse scenarios, facilitating
an informed decision-making process. We discuss observations made using Forte
and demonstrate the effectiveness of visualization techniques to provide
valuable insights into the correlation between weather inputs and net load
forecasts, ultimately advancing grid capabilities by improving trust in
forecasting models.
