Abstract
Wind power is the most promising renewable energy for its rich resources, low cost, and cleanliness. However, the intermittency of wind power would put the safety of the power system at risk. An effective method to solve this problem is making the power generation scheduling through wind power forecast. Due to the volatility and complex temporal dependence, it is a challenging task to predict wind power over multiple time steps. In this paper, a novel method based on a chain echo state network(CESN) is proposed to enhance mapping capability for multi-step prediction. The multiple echo states of CESN is utilized to prevent the error accumulation for multi-step prediction. Experimental results in three cases demonstrate that the proposed method has promising performance on multi-step prediction and could prevent error accumulation effectively.
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Acknowledgements
This paper is supported by the National Key Research and Development Program of China (2021YFF0500903), and the National Natural Science Foundation of China (52178271).
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Zeng, S., Jiang, R., Wu, Z., Ye, X., Wang, Z. (2022). Wind Power Forecast Based on Multiple Echo States. In: Zhang, H., et al. Neural Computing for Advanced Applications. NCAA 2022. Communications in Computer and Information Science, vol 1638. Springer, Singapore. https://doi.org/10.1007/978-981-19-6135-9_23
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DOI: https://doi.org/10.1007/978-981-19-6135-9_23
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