| Issue |
E3S Web Conf.
Volume 51, 2018
2018 3rd International Conference on Advances on Clean Energy Research (ICACER 2018)
|
|
|---|---|---|
| Article Number | 02004 | |
| Number of page(s) | 5 | |
| Section | Solar Energy Utilization and Power Generation Technology | |
| DOI | https://doi.org/10.1051/e3sconf/20185102004 | |
| Published online | 24 August 2018 | |
Machine learning techniques for short-term solar power stations operational mode planning
Ural Federal University named after the first President of Russia B.N. Yeltsin,
Ekaterinburg,
620002 Mira str. 19,
Russia
* Corresponding author: lkhalyasmaa@mail.ru
The paper presents the operational model of very-short term solar power stations (SPS) generation forecasting developed by the authors, based on weather information and built into the existing software product as a separate module for SPS operational forecasting. It was revealed that one of the optimal mathematical methods for SPS generation operational forecasting is gradient boosting on decision trees. The paper describes the basic principles of operational forecasting based on the boosting of decision trees, the main advantages and disadvantages of implementing this algorithm. Moreover, this paper presents an example of this algorithm implementation being analyzed using the example of data analysis and forecasting the generation of the existing SPS.
© The authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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