期刊
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
卷 10, 期 2, 页码 780-789出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSTE.2018.2847558
关键词
Lasso; photovoltaic generation; probabilistic forecasts; quantile regression; reliability; sharpness; spatio-temporal
资金
- company Coruscant SA
Photovoltaic (PV) power generation is characterized by significant variability. Accurate PV forecasts are a prerequisite to securely and economically operating electricity networks, especially in the case of large-scale penetration. In this paper, we propose a probabilistic spatio-temporal model for the PV power production that exploits production information from neighboring plants. The model provides the complete future probability density function of PV production for very short-term horizons (0-6 h). The method is based on quantile regression and a L-1 penalization technique for automatic selection of the input variables. The proposed modeling chain is simple, making the model fast and scalable to direct on-line application. The performance of the proposed approach is evaluated using a real-world test case, with a high number of geographically distributed PV installations and by comparison with state-of-the-art probabilistic methods.
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