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Model estimation for solar generation forecasting using cloud cover data
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DOI：10.1016/j.solener.2017.08.086.png)
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Abstract
En 中文
This paper presents a parametric model approach to address the problem of photovoltaic generation forecasting in a scenario where measurements of meteorological variables, i.e., solar irradiance and temperature, are not available at the plant site. This scenario is relevant to electricity network operation, when a large number of PV plants are deployed in the grid. The proposed method makes use of raw cloud cover data provided by a meteorological service combined with power generation measurements, and is particularly suitable in PV plant integration on a large-scale basis, due to low model complexity and computational efficiency. An extensive validation is performed using both simulated and real data.
Keywords:
Photovoltaic generation
Modeling
Estimation
Forecasting
Cloud cover
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