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25 September 2023 Data characterization of photovoltaic power plants by considering correlation
Ning Zhang, Cong Tian, TaiQing Tang
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Proceedings Volume 12788, Second International Conference on Energy, Power, and Electrical Technology (ICEPET 2023); 1278823 (2023) https://doi.org/10.1117/12.3004299
Event: Second International Conference on Energy, Power, and Electrical Technology (ICEPET 2023), 2023, Kuala Lumpur, Malaysia
Abstract
With the adjustment of the energy structure system, the proportion of new energy generation such as wind power and photovoltaic (PV) is increasing, and the forecasting technology for new energy loads such as wind power and PV has become a common concern for grid development and planning. Unlike wind power generation, PV output is coupled with more factors, and its cyclicality and volatility are more significant. For PV power forecasting, the data mining analysis of the PV plant itself is more important than the forecasting algorithm, because the accuracy of the forecasting result is not only related to the model, but also depends on the variables input to the model. This paper proposes a correlation analysis of PV plant data to address the above issues. Firstly, the periodic and fluctuating characteristics of PV power generation are systematically and intuitively described from the PV plant data itself, then the data types of PV plants are described from three aspects: data sources, time scale and spatial scale. Finally, the Pearson correlation coefficient is used to correlate the data, and the impact of sampling granularity on PV output forecasting is pointed out, providing a theoretical basis for PV forecasting work
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Ning Zhang, Cong Tian, and TaiQing Tang "Data characterization of photovoltaic power plants by considering correlation", Proc. SPIE 12788, Second International Conference on Energy, Power, and Electrical Technology (ICEPET 2023), 1278823 (25 September 2023); https://doi.org/10.1117/12.3004299
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KEYWORDS
Photovoltaics
Solar energy
Data conversion
Data modeling
Correlation coefficients
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