基于频谱注意力和无交叉联合分位数回归的海上风电功率超短期概率预测

Ultra-short-term probabilistic forecasting of offshore wind power based on spectral attention and non-crossing joint quantile regression

海上风电功率波动性和随机性强,其时序数据既存在时间上的全局性关联,又包含大量局部的短期噪声;同时,现有概率预测模型普遍采用的分位数回归方法存在分位数交叉问题,严重影响了预测结果的精度与合理性。针对上述挑战,提出了一种基于频谱注意力和无交叉联合分位数回归的海上风电功率超短期概率预测方法。首先,结合长短期记忆网络(longshort-termmemory,LSTM)模型和频谱注意力模型,挖掘风电功率时序数据的全局长期依赖,并滤除局部噪声。其次,在此基础上构建了一种基于联合分位数回归的损失函数模型,通过共享各分位数预测任务间的信息,自主动态调节各分位数损失的权重,避免分位数交叉问题。最后通过真实算例分析表明,相比于现有概率预测模型,所提模型的锐度指标提升了6%以上,连续等级概率分数提高了10%以上,故具有更高的预测精度,并有效解决了分位数交叉问题。

Because of the high fluctuation and random nature of offshore wind power,time-series data contains not only global correlations but also a large amount of short-term local noise.In addition,existing probabilistic forecasting studies are commonly based on quantile regression methods which suffer from the quantile crossing problem and unreliable forecasting results.Therefore,this paper proposes an ultra-short-term offshore wind power probabilistic forecasting method based on a spectral attention and a crossing free joint quantile regression.First,combined with the long short-term memory(LSTM)model and spectral attention model,the global long-term dependence is mined and local noises are filtered in the time-series data.Then,a joint quantile regression loss function model is built to avoid the quantile crossing problem by dynamically adjusting the loss weights and sharing information among all the quantile regression tasks.Real case studies demonstrate that,compared to LSTM and TCN,the proposed model has improved the sharpness of quantile forecasting by over 6%and the continuously ranking probability score by over 10%,with superior prediction accuracy and the quantile crossing problem well addressed.