# Who should I trust? A Visual Analytics Approach for Comparing Net Load Forecasting Models

arXiv: https://arxiv.org/abs/2407.21299 · HTML: https://mlsanity.com/paper/2407.21299/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 31 Jul 2024.

## Builds on (most cited first, 4 of 4)

- [The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations](https://mlsanity.com/paper/2212.11737/) (2022)
- [KPF-AE-LSTM: A Deep Probabilistic Model for Net-Load Forecasting in High Solar Scenarios](https://mlsanity.com/paper/2203.04401/) (2022)
- [Forte: An Interactive Visual Analytic Tool for Trust-Augmented Net Load Forecasting](https://mlsanity.com/paper/2311.06413/) (2023)
- [Clustering Interval Load with Weather to Create Scenarios of Behind-the-Meter Solar Penetration](https://mlsanity.com/paper/2403.05009/) (2024)

## Abstract (arXiv)

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.