FORECASTING MODEL OF ULTRA-SHORT-TERM PHOTOVOLTAIC POWER BASED ON METEOROLOGICAL CLASSIFICATION

Gu Jin; Liu Yu; Gu Leitao; Jiao Shang; Zhou Jinbai; Xu Zhengyi

doi:10.19912/j.0254-0096.tynxb.2024-1385

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (12) : 263-270. DOI: 10.19912/j.0254-0096.tynxb.2024-1385

FORECASTING MODEL OF ULTRA-SHORT-TERM PHOTOVOLTAIC POWER BASED ON METEOROLOGICAL CLASSIFICATION

Gu Jin¹, Liu Yu¹, Gu Leitao¹, Jiao Shang¹, Zhou Jinbai¹, Xu Zhengyi²

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Abstract

To address the limitations of traditional similar-day clustering methods, which fail to achieve low-cost and accurate identification of short-term variations in weather types and thus affect photovoltaic （PV） power forecasting accuracy, this study integrates the differential features between ideal global horizontal irradiance （GHI） and measured GHI, employing the XGBoost algorithm for ultra-short-term weather scenario classification. Subsequently, a combined VMD-CNN-LSTM prediction model is adopted to capture both local and global characteristics of time-series data, thereby improving forecasting accuracy. Experimental results demonstrate that the proposed method can achieve higher accuracy in ultra-short-term PV power forecasting without requiring additional high-cost meteorological observations. Compared with traditional forecasting algorithms, under ideal sunny conditions, the 30-minute-ahead forecasting performance achieves an R² increase of 3.4%, while the MSE, MAE, and RMSE are reduced by 52.6%, 33%, and 29%, respectively.

Key words

photovoltaic power generation / classification analysis / XGBoost / power prediction

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Gu Jin, Liu Yu, Gu Leitao, Jiao Shang, Zhou Jinbai, Xu Zhengyi. FORECASTING MODEL OF ULTRA-SHORT-TERM PHOTOVOLTAIC POWER BASED ON METEOROLOGICAL CLASSIFICATION[J]. Acta Energiae Solaris Sinica. 2025, 46(12): 263-270 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1385

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