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Estimating Solar and Wind Power Production Using Computer Vision Deep Learning Techniques on Weather Maps
Author(s) -
Bosma Sebastian B. M.,
Nazari Negar
Publication year - 2022
Publication title -
energy technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.91
H-Index - 44
eISSN - 2194-4296
pISSN - 2194-4288
DOI - 10.1002/ente.202200289
Subject(s) - deep learning , renewable energy , convolutional neural network , computer science , artificial intelligence , production (economics) , weather research and forecasting model , grid , meteorology , work (physics) , solar power , wind power , wind power forecasting , power (physics) , machine learning , electric power system , engineering , geography , electrical engineering , mechanical engineering , physics , geodesy , quantum mechanics , economics , macroeconomics
Accurate renewable energy production forecasting has become a priority as the share of intermittent energy sources on the grid increases. Recent work has shown that convolutional deep learning models can successfully be applied to forecast weather maps. Building on this capability, a ResNet‐inspired model that estimates solar and wind power production based on weather maps is proposed. By capturing both spatial and temporal correlations using convolutional neural networks with stacked input frames, the model is designed to capture the complex dynamics governing these energy sources. A dataset that focuses on the state of California is constructed and made available as a secondary contribution of the work. It is demonstrated that the novel model outperforms traditional deep learning techniques: it predicts an accurate power production profile that is smooth and includes high‐frequency details.