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

TL;DR

This paper presents a visual analytics tool that helps energy experts compare and evaluate different net load forecasting models across various conditions, improving trust and decision-making.

Contribution

It introduces a novel visual analytics application for comparing deep-learning and other models in probabilistic net load forecasting, addressing evaluation challenges.

Findings

Visualizations aid in discerning model performance differences.

The tool enhances trust in forecasting models.

Case study demonstrates decision-making improvements.

Abstract

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.