| Issue |
E3S Web Conf.
Volume 51, 2018
2018 3rd International Conference on Advances on Clean Energy Research (ICACER 2018)
|
|
|---|---|---|
| Article Number | 02002 | |
| Number of page(s) | 6 | |
| Section | Solar Energy Utilization and Power Generation Technology | |
| DOI | https://doi.org/10.1051/e3sconf/20185102002 | |
| Published online | 24 August 2018 | |
Weather data errors analysis in solar power stations generation forecasting
Ural Federal University named after the first President of Russia B.N. Yeltsin,
Ekaterinburg,
620002 Mira str. 19,
Russia
* Corresponding author: stas_ersh@mail.ru
The paper presents a short-term forecasting model for solar power stations (SPS) generation developed by the authors. This model is based on weather data and built into the existing software product as a separate short-term forecasting module for the SPS generation. The main problems associated with forecasting the SPS generation on cloudy days were revealed in the framework of authors' research, which is due not to the error of the developed model but to the use of the same learning sample for both solar and cloudy days. This paper contains analysis of the main problems related to the learning sampling, samples pattern, quality and representativeness for forecasting the SPS generation on cloudy days. Besides, the paper includes a calculation example performed for the existing SPS and a detailed analysis of the forecast generation on cloudy days based on the actual weather provider data.
© 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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