Increasing the Accuracy of Hourly Multi-Output Solar Power Forecast with Physics-Informed Machine Learning

Increasing the Accuracy of Hourly Multi-Output Solar Power Forecast with Physics-Informed Machine Learning

Machine Learning (ML)-based methods have been identified as capable of providing up to one day ahead Photovoltaic (PV) power forecasts. In this research, we introduce a generic physical model of a PV system into ML predictors to forecast from one to three days ahead. The only requirement is a basic...

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Bibliographic Details
Main Authors:	Daniel Vázquez Pombo, Henrik W. Bindner, Sergiu Viorel Spataru, Poul Ejnar Sørensen, Peder Bacher
Format:	Article
Language:	English
Published:	MDPI AG 2022-01-01
Series:	Sensors
Subjects:	solar power forecasting deep-learning physics-informed machine learning PV
Online Access:	https://www.mdpi.com/1424-8220/22/3/749

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