Load forecasting is of great significance in the power industry as it can provide a reference for subsequent tasks such as power grid dispatch, thus bringing huge economic benefits. However, there are many differences between load forecasting and traditional time series forecasting. On the one hand, load forecasting aims to minimize the cost of subsequent tasks such as power grid dispatch, rather than simply pursuing prediction accuracy. On the other hand, the load is largely influenced by many external factors, such as temperature or calendar variables. In addition, the scale of predictions (such as building-level loads and aggregated-level loads) can also significantly impact the predicted results. In this paper, we provide a comprehensive load forecasting archive, which includes load domain-specific feature engineering to help forecasting models better model load data. In addition, different from the traditional loss function which only aims for accuracy, we also provide a method to customize the loss function based on the forecasting error, integrating it into our forecasting framework. Based on this, we conducted extensive experiments on load data at different levels, providing a reference for researchers to compare different load forecasting models.

翻译：电力负荷预测在电力行业中具有重要意义，可为电网调度等后续任务提供参考，从而带来巨大的经济效益。然而，负荷预测与传统时间序列预测存在诸多差异。一方面，负荷预测旨在最小化电网调度等后续任务的成本，而非单纯追求预测精度。另一方面，负荷受温度、日历变量等外部因素的显著影响。此外，预测尺度（如建筑级负荷与聚合级负荷）也会对预测结果产生重要影响。本文提供了一个全面的负荷预测档案库，包含负荷领域特有的特征工程方法，以帮助预测模型更好地建模负荷数据。同时，区别于仅关注精度的传统损失函数，我们提出了一种基于预测误差自定义损失函数的方法，并将其集成至预测框架中。基于此，我们在不同尺度的负荷数据上进行了大量实验，为研究人员比较不同的负荷预测模型提供了参考依据。