Energy (load, wind, photovoltaic) forecasting is significant 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 energy forecasting and traditional time series forecasting. On the one hand, traditional time series mainly focus on capturing characteristics like trends and cycles. In contrast, the energy series is largely influenced by many external factors, such as meteorological and calendar variables. On the other hand, energy forecasting aims to minimize the cost of subsequent tasks such as power grid dispatch, rather than simply pursuing prediction accuracy. In addition, the scale of energy data can also significantly impact the predicted results. In this paper, we collected large-scale load datasets and released a new renewable energy dataset that contains both station-level and region-level renewable generation data with meteorological data. For load data, we also included load domain-specific feature engineering and provided a method to customize the loss function and link the forecasting error to requirements related to subsequent tasks (such as power grid dispatching costs), integrating it into our forecasting framework. Based on such a situation, we conducted extensive experiments with 21 forecasting methods in these energy datasets at different levels under 11 evaluation metrics, providing a comprehensive reference for researchers to compare different energy forecasting models.