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Statistical Learning Tools for Electricity Load Forecasting

This monograph explores a set of statistical and machine learning tools that can be effectively utilized for applied data analysis in the context of electricity load forecasting. Drawing on their substantial research and experience with forecasting electricity demand in industrial settings, the auth...

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Ngā taipitopito rārangi puna kōrero
Ngā kaituhi matua:	Antoniadis, Anestis (Author), Cugliari, Jairo (Author), Fasiolo, Matteo (Author), Goude, Yannig (Author), Poggi, Jean-Michel (Author)
Kaituhi rangatōpū:	SpringerLink (Online service)
Hōputu:	Tāhiko īPukapuka
Reo:	Ingarihi
I whakaputaina:	Cham : Springer International Publishing : Imprint: Birkhäuser, 2024.
Putanga:	1st ed. 2024.
Rangatū:	Statistics for Industry, Technology, and Engineering,
Ngā marau:	Statistics . Machine learning. Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. Statistical Learning. Machine Learning.
Urunga tuihono:	https://doi.org/10.1007/978-3-031-60339-6
Ngā Tūtohu:	Tāpirihia he Tūtohu Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!

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