Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects
Problem
Renewable energy generation is variable and uncertain, making accurate forecasting difficult, which complicates efficient grid operation and energy management.
Why it matters
Accurate forecasts of renewable output are stated as crucial for efficient grid operation as renewables penetrate the electricity grid.
What the AI did
The paper reviews machine learning and deep learning approaches used for renewable energy forecasting, discussing their strengths, limitations and future research directions.
Results
not stated in source
Impact
The evidence only supports that this is a literature review mapping approaches and challenges, not a new solving result.
Record
| Venue | Sustainability |
|---|---|
| Published | 2023 |
| Work type | article |
| Authors | Natei Ermias Benti, Mesfin Diro Chaka, Addisu Gezahegn Semie |
| Citations | 378 (OpenAlex, at import time) |
| References | 232 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/su15097087 |
| Open access | gold |
| Keywords | renewable energy forecasting, machine learning, deep learning, review, grid operation |
Importance rating
major — millions of lives, an industry or a field
Accurate renewable generation forecasting is stated as crucial for efficient grid operation and the sustainable energy transition, affecting an entire industry; paper is a review with no new results.
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