SHORT-TERM PREDICTION OF PV POWER OF NGO-CNN-LSTM BASED ON COMPREHENSIVE SIMILAR DAY SELECTION AND QUADRATIC DECOMPOSITION

Song Yu; Xu Ye; Wang Xu; Li Wei; Chen Zhe

doi:10.19912/j.0254-0096.tynxb.2024-1188

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (11) : 219-234. DOI: 10.19912/j.0254-0096.tynxb.2024-1188

SHORT-TERM PREDICTION OF PV POWER OF NGO-CNN-LSTM BASED ON COMPREHENSIVE SIMILAR DAY SELECTION AND QUADRATIC DECOMPOSITION

Song Yu, Xu Ye, Wang Xu, Li Wei, Chen Zhe

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Abstract

Aiming at some problems involved into data preprocessing process of PV power prediction such as single evaluation criteria for similar day selection, fluctuating and non-stationary characteristics of original power series, as well as the difficulty in generating optimal parameter combination of the prediction model, this paper proposes a day-ahead NGO-CNN-LSTM （northern goshawk optimization-convolutional neural network-long short-term memory） short-term PV output prediction model based on similar day selection and double decomposition. Firstly, the Pearson correlation coefficient is used to identify the main meteorological factors; then, the comprehensive similar day selection method combining Euclidean distance and Frechet distance is utilized to select the historical similar day for the predicted day and generate the training set. Secondly, complete ensemble empirical mode decomposition with adaptive noise （CEEMDAN） is used to decompose the historical power sequence of the training set; correspondingly, multi-scale permutation entropy（MPE） is introduced to reconstruct the components with high entropy values. Next, variational mode decomposition （VMD） based on northern goshawk optimization （NGO） is used for the secondary decomposition of reconstructed non-stationary components. The components are reconstructed according to their MPE values. Finally, NGO-CNN-LSTM models corresponding to each reconstructed sequence are constructed, where their predicted results are summed to obtained the final prediction results. The application of the proposed model in PV power station of Yunnan province shows that compared with other benchmark models, NGO-CNN-LSTM model based on similar day selection and double decomposition has the higher prediction accuracy, which has the practical and feasible guide significance for the production plan of power station and the reasonable formulation of electricity market participation strategy.

Key words

comprehensive similarity day / multi-scale arrangement entropy / sequence decomposition / northern goshawk optimization / convolutional neural network-long short-term memory（CNN-LSTM） / photovoltaic power prediction

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Song Yu, Xu Ye, Wang Xu, Li Wei, Chen Zhe. SHORT-TERM PREDICTION OF PV POWER OF NGO-CNN-LSTM BASED ON COMPREHENSIVE SIMILAR DAY SELECTION AND QUADRATIC DECOMPOSITION[J]. Acta Energiae Solaris Sinica. 2025, 46(11): 219-234 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1188

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