Electric load forecasting is an indispensable component of electric power
system planning and management. Inaccurate load forecasting may lead to the
threat of outages or a waste of energy. Accurate electric load forecasting is
challenging when there is limited data or even no data, such as load
forecasting in holiday, or under extreme weather conditions. As high-stakes
decision-making usually follows after load forecasting, model interpretability
is crucial for the adoption of forecasting models. In this paper, we propose an
interactive GAM which is not only interpretable but also can incorporate
specific domain knowledge in electric power industry for improved performance.
This boosting-based GAM leverages piecewise linear functions and can be learned
through our efficient algorithm. In both public benchmark and electricity
datasets, our interactive GAM outperforms current state-of-the-art methods and
demonstrates good generalization ability in the cases of extreme weather
events. We launched a user-friendly web-based tool based on interactive GAM and
already incorporated it into our eForecaster product, a unified AI platform for
electricity forecasting.

我们提出了一种交互式 GAM 模型，它不仅具有解释性，还可以结合电力行业的特定领域知识，提高性能。这种基于提升的 GAM 利用分段线性函数，并通过我们高效的算法进行学习。在公共基准和电力数据集中，我们的交互式 GAM 优于当前最先进的方法，并在极端天气事件的情况下表现出良好的泛化能力。我们还推出了一个用户友好的基于交互式 GAM 的网络工具，并将其整合到我们的 eForecaster 产品中，这是一个统一的电力预测 AI 平台。