ULTRA-SHORT-TERM FORECAST OF SOLAR IRRADIANCE BASED ON KMEANS++-BI-LSTM

Guan Songze; Tang Yuben; Cai Zheng; Wu Lingtao; Zheng Hanbo; Qin Tuanfa

doi:10.19912/j.0254-0096.tynxb.2022-1294

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Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (12) : 170-174. DOI: 10.19912/j.0254-0096.tynxb.2022-1294

ULTRA-SHORT-TERM FORECAST OF SOLAR IRRADIANCE BASED ON KMEANS++-BI-LSTM

Guan Songze¹, Tang Yuben², Cai Zheng³, Wu Lingtao³, Zheng Hanbo², Qin Tuanfa^3,4

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Abstract

A new ultra-short-term prediction scheme for solar irradiance is proposed to address the uncertainty and stochastic fluctuations of surface solar radiation, which in turn has an impact on the stability of large-scale photovoltaic power grid connection to the power system. The scheme uses Pearson correlation analysis and the Kmeans++ algorithm in unsupervised learning to filter multiple meteorological data, identify and classify key meteorological data and add labels to them, and then feed the labelled key meteorological data into a bi-directional long-short term memory network prediction model to achieve a 10-minute ultra-short-term forecast of solar irradiance. The results show that the proposed prediction model has lower root mean square error and lower mean absolute error than the currently used models.

Key words

solar radiation / forecasting / cluster analysis / ultra-short-term / bi-directional long short-term memory network

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Guan Songze, Tang Yuben, Cai Zheng, Wu Lingtao, Zheng Hanbo, Qin Tuanfa. ULTRA-SHORT-TERM FORECAST OF SOLAR IRRADIANCE BASED ON KMEANS++-BI-LSTM[J]. Acta Energiae Solaris Sinica. 2023, 44(12): 170-174 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1294

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