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.

翻译：准确的净负荷预测对于能源规划至关重要，有助于制定交易和负荷分配决策。然而，评估预测模型在不同输入变量（如温度和湿度）下的性能仍然具有挑战性，特别是在激发对模型结果的高度信任方面。在此背景下，日益需要数据驱动的技术干预手段，以帮助科学家理解模型如何对含噪和清洁的输入变量做出反应，从而揭示复杂行为并增强对结果的信心。本文提出了Forte——一个基于可视分析的应用，用于探索深度概率净负荷预测模型在不同输入变量下的表现，并理解不同场景的误差率。通过精心设计的视觉干预手段，该基于Web的界面使科学家能够通过模拟多种场景来获取模型性能的见解，从而促进知情决策过程。我们讨论了使用Forte进行的观察结果，并展示了可视化技术在揭示天气输入与净负荷预测之间相关性方面的有效性，最终通过提升对预测模型的信任来增强电网能力。