Wind speed big data forecasting using time-variant multi-resolution ensemble model with clustering auto-encoder

Hui Liu, Zhu Duan and Chao Chen

Applied Energy, 2020, vol. 280, issue C, No S0306261920314252

Abstract: The fluctuation of wind speed brings difficulty for wind energy utilization. Wind speed forecasting can improve safety of wind power integration. To better forecast fluctuating wind speed, a time-variant multi-resolution ensemble model is proposed, which contains three stages. In stage I, the proposed model uses low-resolution and high-resolution datasets to build base forecasting models. In this manner, the additional fluctuation information in the high-resolution big data is used for better forecasting. In stage II, the wind speed is clustered with a clustering auto-encoder algorithm, and an ensemble weight set is obtained by optimizing weights on each cluster respectively. The clustering auto-encoder algorithm can analyze the nonlinear fluctuation, and generate clustering-friendly features. In stage III, by switching ensemble weights over time, the time-variant ensemble forecasting results can be obtained by combining the base models. The time-variant structure can track the changes of fluctuating characteristics over time, further improving forecasting performance. Four groups of real wind speed high-resolution big data with 51,840 sample points and the corresponding low-resolution data with 8640 sample points are utilized for case study. The forecasting results indicate: (a) the proposed model can effectively predict the wind speed. Taking 1-step forecasting as an example, the mean absolute errors of the proposed model for dataset #1, #2, #3 and #4 are 0.6318 m/s, 0.6957 m/s, 0.6121 m/s and 0.6175 m/s, respectively; (b) the proposed model outperforms other benchmark models.

Keywords: Wind speed forecasting; Time series multi-step forecasting; Deep learning; Time-variant model; Big data forecasting (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (10)

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DOI: 10.1016/j.apenergy.2020.115975

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