Data-Driven Day-Ahead PV Estimation Using Autoencoder-LSTM and Persistence Model

Inherent variability in photovoltaic (PV) and associated impacts on power systems is a challenging problem for both the PV owners and the grid operators. Existing statistical and machine learning algorithms typically work well for weather conditions similar to historical data. However, uncertain wea...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transactions on industry applications Jg. 56; H. 6; S. 7185 - 7192
Hauptverfasser:	Zhang, Yue, Qin, Chuan, Srivastava, Anurag K., Jin, Chenrui, Sharma, Ratnesh K.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.11.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Autoencoder long short-term memory (AE-LSTM) data processing automation day-ahead forecasting Deep learning Economic forecasting Electric power generation Electric power grids Electronic devices ENGINEERING Estimation Forecasting hybrid model Machine learning Mathematical models Meteorological data Meteorology Model accuracy photovoltaic (PV) power estimation Photovoltaic cells Predictive models renewable energy integration Training Uncertainty Weather forecasting
ISSN:	0093-9994, 1939-9367
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!