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Drifting Streaming Peaks-Over-Threshold-Enhanced Self-Evolving Neural Networks for Short-Term Wind Farm Generation Forecast

Author

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  • Yunchuan Liu

    (Division of Science Mathematics and Technology, Governors State University, University Park, IL 60484, USA)

  • Amir Ghasemkhani

    (Department of Computer Science and Engineering, California State University San Bernardino, San Bernardino, CA 92407, USA)

  • Lei Yang

    (Department of Computer Science and Engineering, University of Nevada, Reno, Reno, NV 89557, USA)

Abstract

This paper investigates the short-term wind farm generation forecast. It is observed from the real wind farm generation measurements that wind farm generation exhibits distinct features, such as the non-stationarity and the heterogeneous dynamics of ramp and non-ramp events across different classes of wind turbines. To account for the distinct features of wind farm generation, we propose a Drifting Streaming Peaks-over-Threshold (DSPOT)-enhanced self-evolving neural networks-based short-term wind farm generation forecast. Using DSPOT, the proposed method first classifies the wind farm generation data into ramp and non-ramp datasets, where time-varying dynamics are taken into account by utilizing dynamic ramp thresholds to separate the ramp and non-ramp events. We then train different neural networks based on each dataset to learn the different dynamics of wind farm generation by the NeuroEvolution of Augmenting Topologies (NEAT), which can obtain the best network topology and weighting parameters. As the efficacy of the neural networks relies on the quality of the training datasets (i.e., the classification accuracy of the ramp and non-ramp events), a Bayesian optimization-based approach is developed to optimize the parameters of DSPOT to enhance the quality of the training datasets and the corresponding performance of the neural networks. Based on the developed self-evolving neural networks, both distributional and point forecasts are developed. The experimental results show that compared with other forecast approaches, the proposed forecast approach can substantially improve the forecast accuracy, especially for ramp events. The experiment results indicate that the accuracy improvement in a 60 min horizon forecast in terms of the mean absolute error (MAE) is at least 33.6 % for the whole year data and at least 37 % for the ramp events. Moreover, the distributional forecast in terms of the continuous rank probability score (CRPS) is improved by at least 35.8 % for the whole year data and at least 35.2 % for the ramp events.

Suggested Citation

  • Yunchuan Liu & Amir Ghasemkhani & Lei Yang, 2022. "Drifting Streaming Peaks-Over-Threshold-Enhanced Self-Evolving Neural Networks for Short-Term Wind Farm Generation Forecast," Future Internet, MDPI, vol. 15(1), pages 1-19, December.
    • Handle: RePEc:gam:jftint:v:15:y:2022:i:1:p:17-:d:1018146
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    References listed on IDEAS

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    1. Pierre Pinson & Henrik Madsen, 2012. "Adaptive modelling and forecasting of offshore wind power fluctuations with Markov‐switching autoregressive models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 31(4), pages 281-313, July.
    2. Erdem, Ergin & Shi, Jing, 2011. "ARMA based approaches for forecasting the tuple of wind speed and direction," Applied Energy, Elsevier, vol. 88(4), pages 1405-1414, April.
    3. Zuluaga, Carlos D. & Álvarez, Mauricio A. & Giraldo, Eduardo, 2015. "Short-term wind speed prediction based on robust Kalman filtering: An experimental comparison," Applied Energy, Elsevier, vol. 156(C), pages 321-330.
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