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Enhancing photovoltaic power forecasting accuracy via AnEn-corrected NWP data and seasonal CNN-LSTM models.

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Bibliographic Details
Title:	Enhancing photovoltaic power forecasting accuracy via AnEn-corrected NWP data and seasonal CNN-LSTM models.
Authors:	Zhu, Honglu¹ (AUTHOR) hongluzhu@ncepu.edu.cn, Gao, Yanan¹ (AUTHOR), Liu, Jun² (AUTHOR), Yuan, Shuai² (AUTHOR), Li, Sun² (AUTHOR), Qin, Fang³ (AUTHOR), Li, Dengxuan³ (AUTHOR)
Source:	International Journal of Green Energy. 2026, Vol. 23 Issue 9, p1991-2008. 18p.
Subject Terms:	*Clean energy investment, Electric power production forecasting, Deep learning, Atmospheric models, Forecasting, Forecasting methodology, Numerical weather forecasting, Machine learning
Abstract:	Accurate photovoltaic (PV) power forecasting is critical for grid integration of PV energy. To address large errors in numerical weather prediction (NWP) data and poor seasonal adaptability of traditional models, this study proposes an intelligent forecasting method integrating Analog Ensemble (AnEn)-based NWP correction and a seasonally segmented convolutional neural network – long short-term memory (CNN-LSTM) multi-model framework. First, AnEn calibrates NWP data to improve meteorological input accuracy; specifically, corrected irradiance reduces root mean square error (RMSE) by 14.62% and mean absolute error (MAE) by 21.56%, while corrected temperature decreases RMSE by 11.09% and MAE by 12.67%. Second, seasonal CNN-LSTM models capture distinct PV generation characteristics across seasons. Experimental results show the proposed method outperforms traditional models: overall RMSE is reduced by 5.34% (from 16.58 to 15.69), MAE by 6.78% (from 11.85 to 11.05), and the coefficient of determination (R2) is improved by 0.47% (from 0.958 to 0.962). This work enhances forecasting accuracy and robustness, with strong practical applicability for power system operation [ABSTRACT FROM AUTHOR]
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Database:	GreenFILE

Description
Abstract:	Accurate photovoltaic (PV) power forecasting is critical for grid integration of PV energy. To address large errors in numerical weather prediction (NWP) data and poor seasonal adaptability of traditional models, this study proposes an intelligent forecasting method integrating Analog Ensemble (AnEn)-based NWP correction and a seasonally segmented convolutional neural network – long short-term memory (CNN-LSTM) multi-model framework. First, AnEn calibrates NWP data to improve meteorological input accuracy; specifically, corrected irradiance reduces root mean square error (RMSE) by 14.62% and mean absolute error (MAE) by 21.56%, while corrected temperature decreases RMSE by 11.09% and MAE by 12.67%. Second, seasonal CNN-LSTM models capture distinct PV generation characteristics across seasons. Experimental results show the proposed method outperforms traditional models: overall RMSE is reduced by 5.34% (from 16.58 to 15.69), MAE by 6.78% (from 11.85 to 11.05), and the coefficient of determination (R2) is improved by 0.47% (from 0.958 to 0.962). This work enhances forecasting accuracy and robustness, with strong practical applicability for power system operation [ABSTRACT FROM AUTHOR]
ISSN:	15435075
DOI:	10.1080/15435075.2026.2642171