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Enhancing Photovoltaic Power Predictions with Deep Physical Chain Model.

Enhancing Photovoltaic Power Predictions with Deep Physical Chain Model.

Authors :
Dormido-Canto, Sebastián
Rohland, Joaquín
López, Matías
Garcia, Gonzalo
Fabregas, Ernesto
Farias, Gonzalo
Source :
Algorithms; Oct2024, Vol. 17 Issue 10, p445, 23p
Publication Year :
2024

Abstract

Predicting solar power generation is a complex challenge with multiple issues, such as data quality and choice of methods, which are crucial to effectively integrate solar power into power grids and manage photovoltaic plants. This study creates a hybrid methodology to improve the accuracy of short-term power prediction forecasts using a model called Transformer Bi-LSTM (Bidirectional Long Short-Term Memory). This model, which combines elements from the transformer architecture and bidirectional LSTM (Long–Short-Term Memory), is evaluated using two strategies: the first strategy makes a direct prediction using meteorological data, while the second employs a chain of deep learning models based on transfer learning, thus simulating the traditional physical chain model. The proposed approach improves performance and allows you to incorporate physical models to refine forecasts. The results outperform existing methods on metrics such as mean absolute error, specifically by around 24%, which could positively impact power grid operation and solar adoption. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
19994893
Volume :
17
Issue :
10
Database :
Complementary Index
Journal :
Algorithms
Publication Type :
Academic Journal
Accession number :
180486581
Full Text :
https://doi.org/10.3390/a17100445