@inproceedings{0382a8c4d4174759afcfc6d1796409cc,
title = "Big data and machine learning for applied weather forecasts: Forecasting solar power for utility operations",
abstract = "To blend growing amounts of renewable energy into utility grids requires accurate estimate of the power from those resources for both day ahead planning and real-Time operations. This requires predicting the wind and solar resource on those timescales. Accurate prediction of these meteorological variables is a big data problem that requires a multitude of disparate data, multiple models that are each applicable to a specific time frame, and application of computational intelligence techniques to successfully blend all of the model and observational information in real-Time and deliver it to the decision-makers at utilities and grid operators. Considering that the capacity of renewable energy continues to grow an additional challenge includes selecting and archiving data for continuous retraining of machine learning algorithms.",
author = "Haupt, \{Sue Ellen\} and Branko Kosovic",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE Symposium Series on Computational Intelligence, SSCI 2015 ; Conference date: 08-12-2015 Through 10-12-2015",
year = "2015",
doi = "10.1109/SSCI.2015.79",
language = "English",
series = "Proceedings - 2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "496--501",
booktitle = "Proceedings - 2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015",
address = "United States",
}