Titlebook: Data Analytics for Renewable Energy Integration. Technologies, Systems and Society; 6th ECML PKDD Worksh Wei Lee Woon,Zeyar Aung,Stuart Mad

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Class, Surplus, and the Division of Labourlts are particularly encouraging as manual feature extraction is a subjective process that may require significant redesign when confronted with new operating conditions and data types. In contrast, the ability to automatically learn feature sets from the raw input data (AE signals) promises better

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Data Analytics for Renewable Energy Integration. Technologies, Systems and Society6th ECML PKDD Worksh

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https://doi.org/10.1007/978-981-13-1102-4he stronger connections. As shown experimentally, training the models over the correlation graph-based reduced dataset allows to decrease the overall computational time while preserving almost the same error in the case of Support Vector Regressors and even improving the error of the MLPs, if the original dimension is high.

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https://doi.org/10.1007/978-3-030-16222-1 the same results as with the original time series. In this work, we improve our previous algorithm with the help of specialized sampling strategies. Furthermore, we provide a new method to compare power analysis results achieved with the representative time series to the original time series.

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Sampling Strategies for Representative Time Series in Load Flow Calculations, the same results as with the original time series. In this work, we improve our previous algorithm with the help of specialized sampling strategies. Furthermore, we provide a new method to compare power analysis results achieved with the representative time series to the original time series.

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