Advanced Methods of Power Load Forecasting
This reprint introduces advanced prediction models focused on power load forecasting. Models based on artificial intelligence and more traditional approaches are shown, demonstrating the real possibilities of use to improve prediction in this field. Models of LSTM neural networks, LSTM networks with...
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Format: | eBook |
Language: | English |
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MDPI - Multidisciplinary Digital Publishing Institute
2022
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Collection: | Directory of Open Access Books - Collection details see MPG.ReNa |
Summary: | This reprint introduces advanced prediction models focused on power load forecasting. Models based on artificial intelligence and more traditional approaches are shown, demonstrating the real possibilities of use to improve prediction in this field. Models of LSTM neural networks, LSTM networks with a SESDA architecture, in even LSTM-CNN are used. On the other hand, multiple seasonal Holt-Winters models with discrete seasonality and the application of the Prophet method to demand forecasting are presented. These models are applied in different circumstances and show highly positive results. This reprint is intended for both researchers related to energy management and those related to forecasting, especially power load. |
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Item Description: | Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/ |
Physical Description: | 1 electronic resource (128 p.) |
ISBN: | 9783036542188 9783036542171 books978-3-0365-4217-1 |