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• W4387573791 abstract "Abstract Energy forecasting is crucial in scheduling and planning future electric load, so as to improve the reliability and safeness of the power grid. Despite recent developments of forecasting algorithms in the machine learning community, there is a lack of general and advanced algorithms specifically considering requirements from the power industry perspective. In this paper, we present eForecaster, a unified AI platform including robust, flexible, and explainable machine learning algorithms for diversified energy forecasting applications. Since October 2021, multiple commercial bus load, system load, and renewable energy forecasting systems built upon eForecaster have been deployed in seven provinces of China. The deployed systems consistently reduce the average Mean Absolute Error (MAE) by 39.8% to 77.0%, with reduced manual work and explainable guidance. In particular, eForecaster also integrates multiple interpretation methods to uncover the working mechanism of the predictive models, which significantly improves forecasts adoption and user satisfaction." @default.
• W4387573791 created "2023-10-13" @default.
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• W4387573791 date "2023-10-11" @default.
• W4387573791 modified "2023-10-13" @default.
• W4387573791 title "Energy forecasting with robust, flexible, and explainable machine learning algorithms" @default.
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• W4387573791 doi "https://doi.org/10.1002/aaai.12130" @default.
• W4387573791 hasPublicationYear "2023" @default.
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