  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.
