WIND FARM POWER PREDICTION METHOD BASED ON ADAPTIVE VMD SAMPLE ENTROPY AND DYNAMIC MEMRISTOR-BASED RESERVOIR COMPUTING

Yang Ningning; Ma Zhirui; Wu Zhaojun

doi:10.19912/j.0254-0096.tynxb.2024-1524

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (1) : 485-493. DOI: 10.19912/j.0254-0096.tynxb.2024-1524

WIND FARM POWER PREDICTION METHOD BASED ON ADAPTIVE VMD SAMPLE ENTROPY AND DYNAMIC MEMRISTOR-BASED RESERVOIR COMPUTING

Yang Ningning¹, Ma Zhirui¹, Wu Zhaojun²

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Abstract

With the large-scale integration of wind power into the grid, wind power forecasting is confronted with multifaceted new challenges in terms of time scales, feature capture, data processing, and uncertainty quantification. Research in this domain holds significant importance for enhancing power system stability, optimizing resource allocation, mitigating investment risks, and fostering the development of the wind power industry. This paper proposes a dynamic memristor-based reservoir computing approach that integrates variational mode decomposition and sample entropy. Initially, the adaptive VMD technique is employed to decompose the original wind power time series into a series of sub-modes with varying bandwidths, thereby reducing its nonlinearity and instability. Subsequently, the complexity of each sub-path is analyzed by calculating the sample entropy, and the sub-pah are recombined accordingly to obtain sub-sequences that are more suitable for prediction. In the aspect of prediction model construction, this paper introduces a dynamic memristor-based reservoir computing framework, which incorporates an adaptive algorithm to optimize the reservoir parameters, enhancing the accuracy and robustness of the prediction model. By dynamically adjusting the connection weights and neuron states within the reservoir, the model is better equipped to adapt to the real-time variations in wind power. When compared to the backpropagation neural network, long short-term memory neural network, and dynamic memristor-based reservoir computing based on VMD-SE, the proposed model exhibits higher accuracy and faster computation speed. In summary, the VMD-SE-DMRPC model presented in this paper demonstrates notable advantages in addressing the volatility and instability of wind power, providing a novel and effective approach for wind farm power prediction.

Key words

wind farms / power forecasting / memristors / variational mode decomposition / time series

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Yang Ningning, Ma Zhirui, Wu Zhaojun. WIND FARM POWER PREDICTION METHOD BASED ON ADAPTIVE VMD SAMPLE ENTROPY AND DYNAMIC MEMRISTOR-BASED RESERVOIR COMPUTING[J]. Acta Energiae Solaris Sinica. 2026, 47(1): 485-493 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1524

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