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

Benchmarks and Custom Package for Electrical Load Forecasting