Abstract
Due to the randomness and instability of photovoltaic power generation, accurate power generation forecasting is of great significance to ensure grid stability and economic dispatch. This paper proposes a photovoltaic (PV) power output prediction model based on randomized multi-scale kernels, where the centers of the kernels are extracted by K-means method and the scales are sampled from a pre-defined uniform distribution. Through the experiment analysis, we believe that for generation data, establishing a model according to the season can improve the prediction accuracy. Compared with the existing models, the results show that the proposed method in this paper has a better effect on power generation prediction.
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Acknowledgements
This work is supported partly by First Class Discipline of Zhejiang-A (Zhejiang Gongshang University-Statistics), Zhejiang college students science and technology innovation activity plan (**nmiao talent plan).
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Deng, Y., Liu, Y., Dong, X. (2021). Photovoltaic Power Forecasting Based on Randomized Multi-scale Kernels. In: Jia, Y., Zhang, W., Fu, Y. (eds) Proceedings of 2020 Chinese Intelligent Systems Conference. CISC 2020. Lecture Notes in Electrical Engineering, vol 706. Springer, Singapore. https://doi.org/10.1007/978-981-15-8458-9_72
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DOI: https://doi.org/10.1007/978-981-15-8458-9_72
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