Optimizing Prediction Error for Time-dependent Solar Radiation Modeling Article Swipe
Dylan McDowell
,
Aaron Epiney
,
R.R. Flanagan
,
Mark Deinert
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.2172/2339689
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.2172/2339689
Numerical weather forecasting models and statistical methods have found wide use to help power companies estimate renewable output, but better methods are needed, particularly for extended forecasts. Machine learning approaches have been used here as well, but so far a major limitation is the ability to also predict the corresponding uncertainty in a forecast. Here we show that both can be done and demonstrate this using a long-term-short memory neural network where the difference between predicted and ground truth data are used to train a model for the corresponding forecast uncertainties.
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All OpenAlex metadata
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Optimizing Prediction Error for Time-dependent Solar Radiation ModelingWork title
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2024Year of publication
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2024-03-01Full publication date if available
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Dylan McDowell, Aaron Epiney, R.R. Flanagan, Mark DeinertList of authors in order
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| abstract_inverted_index.difference | 73 |
| abstract_inverted_index.forecasts. | 26 |
| abstract_inverted_index.limitation | 41 |
| abstract_inverted_index.demonstrate | 63 |
| abstract_inverted_index.forecasting | 2 |
| abstract_inverted_index.statistical | 5 |
| abstract_inverted_index.uncertainty | 50 |
| abstract_inverted_index.particularly | 23 |
| abstract_inverted_index.corresponding | 49, 88 |
| abstract_inverted_index.uncertainties. | 90 |
| abstract_inverted_index.long-term-short | 67 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.5400000214576721 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile |