Who should I trust? A Visual Analytics Approach for Comparing Net Load Forecasting Models
This work addresses the problem of model comparison for energy planning experts, offering an incremental tool to improve trust in forecasting models.
The paper tackles the challenge of evaluating net load forecasting models by introducing a visual analytics application that compares deep-learning-based models with others across various conditions, demonstrating its effectiveness in aiding decision-making and enhancing trust through a case study.
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.