The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Improving Renewable Energy Forecasting With a Grid of Numerical Weather Predictions
Open accessAbstract
In the last two decades, renewable energy forecasting progressed toward the development of advanced physical and statistical algorithms aiming at improving point and probabilistic forecast skill. This paper describes a forecasting framework to explore information from a grid of numerical weather predictions (NWP) applied to both wind and solar energy. The methodology combines the gradient boosting trees algorithm with feature engineering techniques that extract the maximum information from the NWP grid. Compared to a model that only considers one NWP point for a specific location, the results show an average point forecast improvement (in terms of mean absolute error) of 16.09% and 12.85% for solar and wind power, respectively. The probabilistic forecast improvement, in terms of continuous ranked probabilistic score, was 13.11% and 12.06%, respectively.
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Cites
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
Practical Bayesian Optimization of Machine Learning Algorithms
Snoek, J., Larochelle, H., & Adams, R. P. (2012) · arXiv · doi:10.48550/arxiv.1206.2944 · Cited by 6,691
Cited by
Simulating wind power forecast error distributions for spatially aggregated wind power plants
Miettinen, J., Holttinen, H., & Hodge, B. (2020) · Wind Energy · doi:10.1002/we.2410 · Cited by 23
Metadata
- Authors
- Andrade, J. R., & Bessa, R. J.
- Venue
- IEEE Transactions on Sustainable Energy
- Published
- 2017-10
- Volume
- 8
- Issue
- 4
- Pages
- 1571-1580
- ISSN
- 1949-3029 (ISSN-L)
- 1949-3037
- OpenAlex
- W2607339923
- Cited by
- 292
- References
- 3
- FWCI
- 11.30
- Type
- journal article
- Language
- en
- Open access
- green
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