STUDY ON PHOTOVOLTAIC POWER COMBINED PREDICTION MODEL EMPLOYING CEEMDAN-INFORMER BASED ON COMPREHENSIVE DISTANCE SIMILAR

Zhan Ying; Wang Xiaotian; Wang Xu; Xu Ye; Li Wei; Wang Wenwen

doi:10.19912/j.0254-0096.tynxb.2024-0521

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (8) : 315-326. DOI: 10.19912/j.0254-0096.tynxb.2024-0521

STUDY ON PHOTOVOLTAIC POWER COMBINED PREDICTION MODEL EMPLOYING CEEMDAN-INFORMER BASED ON COMPREHENSIVE DISTANCE SIMILAR

Zhan Ying¹, Wang Xiaotian², Wang Xu¹, Xu Ye¹, Li Wei¹, Wang Wenwen¹

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Abstract

To address the issues of prediction in the selection of similar days, poor signal decomposition quality, long training times, and the tendency of prediction models to get stuck in local optima, leading to poor prediction accuracy in current photovoltaic power forecasting, this paper innovatively constructs a combined prediction model for PV output. This model integrates comprehensive distance-based similar day selection, complete ensemble empirical mode decomposition with adaptive noise（CEEMDAN）, and the Informer algorithm. On the basis of using the Pearson correlation coefficient method to screen key meteorological factors, a comprehensive similar distance method, which combines grey relational analysis（GRA）, dynamic time warping（DTW）, Euclidean distance, and cosine similarity with weighted combinations, is employed to select historical similar days for the day to be predicted. The CEEMDAN algorithm is then utilized to decompose historical output sequences, generating high-quality training sample sets. Finally, an Informer-based combined prediction model is constructed to achieve accurate PV output forecasting. The application results from a PV power satation in Yunnan indicate that, compared to other combined prediction models, the proposed CEEMDAN-Informer model based on comprehensive distance similarity captures abrupt trends in PV output more effectively and offers higher prediction accuracy. This model provides a robust foundation for subsequent optimal scheduling of PV power satations.

Key words

forecasting / PV power satation / neural network / Informer model / complete ensemble empirical mode decomposition with adaptive noise（CEEMDAN） / comprehensive distance similar

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Zhan Ying, Wang Xiaotian, Wang Xu, Xu Ye, Li Wei, Wang Wenwen. STUDY ON PHOTOVOLTAIC POWER COMBINED PREDICTION MODEL EMPLOYING CEEMDAN-INFORMER BASED ON COMPREHENSIVE DISTANCE SIMILAR[J]. Acta Energiae Solaris Sinica. 2025, 46(8): 315-326 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0521

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