Predicting power usage over long periods is a big deal for planning power systems. It's tough because power use changes in complex ways over time. Transformers are a type of model that can handle both short and long-term patterns. However, they can be too complicated and have too many parameters. This can make them hard to use.

    A new model called the Multi-Granularity Autoformer has been developed. This model is designed to make long-term power predictions more accurate. It uses a special attention mechanism. This mechanism can pick up on both small and large changes in power use over time. This helps the model to understand both quick changes and long-term trends.

    To make the model work better, a shared query-key mechanism is used. This helps the model to spot important patterns at different levels of detail. It also makes the model simpler and more efficient. This is important because simpler models are easier to use and understand.

    Power use can be uncertain. To deal with this, the model uses a special loss function. This function allows the model to make predictions that include a range of possible outcomes. This helps to show how uncertain the predictions are.

    The model has been tested on data from different places. These include Portugal, Australia, America, and ISO New England. The results show that the model works well for both predicting exact power use and showing the range of possible outcomes.

    Power systems are always changing. This makes predicting power use a moving target. The Multi-Granularity Autoformer is a step forward in making these predictions more accurate. It shows that by using the right tools, it's possible to make better sense of complex data.

    However, it's important to remember that no model is perfect. The Multi-Granularity Autoformer is a tool that can help, but it's not a magic solution. It's always important to keep learning and improving.

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