Forte: An Interactive Visual Analytic Tool for Trust-Augmented Net Load Forecasting
This work addresses the problem of trust in forecasting models for energy planners and scientists, but it is incremental as it applies existing visualization techniques to a specific domain.
The paper tackles the challenge of assessing and building trust in net load forecasting models by introducing Forte, an interactive visual analytic tool that helps scientists explore model performance across various input variables and scenarios, demonstrating its effectiveness in providing insights into weather correlations and improving trust.
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.