Research on short-term photovoltaic power prediction based on multi-scale similar days and ESN-KELM dual core prediction model

Li, Naiqing; Li, Longhao; Zhang, Fan; Jiao, Ticao; Wang, Shuang; Li, Xuefeng; Wu, Xinghua

doi:10.1016/j.energy.2023.127557

Cited by 22 publications

(2 citation statements)

References 34 publications

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“…In ref. [30], Li et al introduced a short-term PV power prediction approach for dual-core prediction that combines an ESN and kernel extreme learning machine (ESN-KELM). This approach employed a multiscale similar-day algorithm and the fast iterative filter decomposition method to obtain the model input data and then applied the upgraded optimization method to optimize the ESN-KELM model parameters to improve the prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Photovoltaic Power Prediction Using Nonlinear Spiking Neural P Systems

Gao,

Wang,

Guo

et al. 2024

Sustainability

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To ensure high-quality electricity, improve the dependability of power systems, reduce carbon emissions, and promote the sustainable development of clean energy, short-term photovoltaic (PV) power prediction is crucial. However, PV power is highly stochastic and volatile, making accurate predictions of PV power very difficult. To address this challenging prediction problem, in this paper, a novel method to predict the short-term PV power using a nonlinear spiking neural P system-based ESN model has been proposed. First, we combine a nonlinear spiking neural P (NSNP) system with a neural-like computational model, enabling it to effectively capture the complex nonlinear trends in PV sequences. Furthermore, an NSNP system featuring a layer is designed. Input weights and NSNP reservoir weights are randomly initialized in the proposed model, while the output weights are trained by the Ridge Regression algorithm, which is motivated by the learning mechanism of echo state networks (ESNs), providing the model with an adaptability to complex nonlinear trends in PV sequences and granting it greater flexibility. Three case studies are conducted on real datasets from Alice Springs, Australia, comparing the proposed model with 11 baseline models. The outcomes of the experiments exhibit that the model performs well in tasks of PV power prediction.

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Section: Introductionmentioning

confidence: 99%

Short-Term Photovoltaic Power Prediction Using Nonlinear Spiking Neural P Systems

Gao,

Wang,

Guo

et al. 2024

Sustainability

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“…Furthermore, signal decomposition technology enables models to adaptively adjust according to changes in the environmental and operational conditions, thereby maintaining the flexibility and accuracy of the forecasting strategy. Reference [25] employed Fast Iterative Filtering Decomposition (FIFD) to extract the complex features of photovoltaic power time series. Reference [26] introduces Variational Mode Decomposition (VMD) to address the volatility of raw photovoltaic data.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Photovoltaic Output Prediction Based on Decomposition and Reconstruction and XGBoost under Two Base Learners

Xu,

Wang,

Wang

et al. 2024

Energies

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Photovoltaic power generation prediction constitutes a significant research area within the realm of power system artificial intelligence. Accurate prediction of future photovoltaic output is imperative for the optimal dispatchment and secure operation of the power grid. This study introduces a photovoltaic prediction model, termed ICEEMDAN-Bagging-XGBoost, aimed at enhancing the accuracy of photovoltaic power generation predictions. In this paper, the original photovoltaic power data initially undergo decomposition utilizing the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) algorithm, with each intrinsic mode function (IMF) derived from this decomposition subsequently reconstructed into high-frequency, medium-frequency, and low-frequency components. Targeting the high-frequency and medium-frequency components of photovoltaic power, a limiting gradient boosting tree (XGBoost) is employed as the foundational learner in the Bagging parallel ensemble learning method, with the incorporation of a sparrow search algorithm (SSA) to refine the hyperparameters of XGBoost, thereby facilitating more nuanced tracking of the changes in the photovoltaic power’s high-frequency and medium-frequency components. Regarding the low-frequency components, XGBoost-Linear is utilized to enable rapid and precise prediction. In contrast with the conventional superposition reconstruction approach, this study employs XGBoost for the reconstruction of the prediction output’s high-frequency, intermediate-frequency, and low-frequency components. Ultimately, the efficacy of the proposed methodology is substantiated by the empirical operation data from a photovoltaic power station in Hebei Province, China. Relative to integrated and traditional single models, this paper’s model exhibits a markedly enhanced prediction accuracy, thereby offering greater applicational value in scenarios involving short-term photovoltaic power prediction.

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MBSSA-Bi-AESN: Classification prediction of bi-directional adaptive echo state network based on modified binary salp swarm algorithm and feature selection

Wu,

Zhan,

et al. 2024

Appl Intell

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Research on short-term photovoltaic power prediction based on multi-scale similar days and ESN-KELM dual core prediction model

Cited by 22 publications

References 34 publications

Short-Term Photovoltaic Power Prediction Using Nonlinear Spiking Neural P Systems

Short-Term Photovoltaic Power Prediction Using Nonlinear Spiking Neural P Systems

Short-Term Photovoltaic Output Prediction Based on Decomposition and Reconstruction and XGBoost under Two Base Learners

MBSSA-Bi-AESN: Classification prediction of bi-directional adaptive echo state network based on modified binary salp swarm algorithm and feature selection

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