针对风力发电功率存在较强的间歇性和波动性、预测精度较低的问题,提出基于变分模态分解（VMD）与多策略融合的白鲸优化算法（MBWO）的核极限学习机（KELM）预测模型。首先利用VMD对原始风力发电功率序列进行平稳化处理并构建MBWO-KELM模型,然后将分解后的子序列输入MBWO-KELM模型进行预测,最后对不同子序列进行重构以得到最终的风电功率预测值。结果表明,不同季节下该模型的预测精度和稳定性明显优于其他模型,平均绝对百分比误差（MAPE）值均控制在6%以下,可提高风电能源的利用效率。
Abstract
Aiming at the problem of strong intermittency and volatility and low prediction accuracy of wind power, this paper proposes a short-term wind power prediction model based on Beluga whale optimization algorithm based on multi-strategy fusion （MBWO）, Variational mode decomposition （VMD）, and Kernel extreme learning machine （KELM） prediction model. Firstly, the original wind power sequence is smoothed by VMD and the MBWO-KELM model is constructed. secondly, the decomposed subsequences are input into the MBWO-KELM model for prediction. Finaly, the different subsequences are reconstructed to obtain the final wind power prediction. The results show that the prediction accuracy and stability of the model are significantly better than other models under different seasons, and the MAPE values are all controlled below 6%, which can improve the utilization efficiency of wind power energy.
关键词
风电功率 /
变分模态分解 /
预测 /
自适应算法 /
核极限学习机
Key words
wind power /
variational modal decomposition /
forecasting /
adaptive algorithms /
kernel extreme learning machine
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基金
国家自然科学基金（60771014）; 天津市自然科学基金（13JCZDJC29100）; 大学生创新创业计划（202310057101）
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