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首页> 外文期刊>Energy Conversion & Management >A compound framework for wind speed forecasting based on comprehensive feature selection, quantile regression incorporated into convolutional simplified long short-term memory network and residual error correction
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A compound framework for wind speed forecasting based on comprehensive feature selection, quantile regression incorporated into convolutional simplified long short-term memory network and residual error correction

机译：基于综合特征选择的风速预测复合框架，卷积到卷积成简化的长短短期记忆网络和残留误差校正

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摘要

A reliable wind speed forecasting framework can contribute to handling rational dispatching and safe operation for power system effectively. For this purpose, a novel compound framework coupling decomposition technique, subseries aggregation, synchronous optimization, improved deep network and residual error correction (REC) is investigated in this study. To begin with, time varying filter-based empirical mode decomposition (TVF-EMD) is employed to decompose the raw series into a set of subseries, which are further aggregated based on fuzzy entropy (FE) theory and approximation criterion. Then the synchronous optimization implemented by blended coding-based Harris hawks optimization (HHO) is adopted to optimize the parameters of phase space reconstruction (PSR) and applicable features for each aggregated subseries. Subsequently, quantile regression (QR) is incorporated into an improved deep network, namely convolutional simplified long short-term memory network (QRConvSLSTM), to deduce conditional quantiles for each aggregated subseries, in which the optimized arguments obtained above are applied to construct the optimal input matrixes. Later, the initial point forecasting results can be calculated on the basis of accumulating the conditional quantiles of all the aggregated subseries, while the corresponding error series is deduced therewith. Then the conditional quantiles of the error series are estimated by QRConvSLSTM in the light of REC strategy, after which the final conditional quantiles are calculated by summating the conditional quantiles of the raw series and the error series. Finally, kernel density estimation (KDE) is employed to estimate probabilistic density functions (PDF) of wind speed series in accordance to the final conditional quantiles. To validate the efficiency and effectiveness of the proposed compound framework, nine relevant models are performed on three datasets for comparative experiments, among which the results of point, interval and probability prediction are comprehensively demonstrated and analyzed. The experimental results illustrate that: (1) data preprocessing strategy integrating TVF-EMD and FE-based subseries aggregation contributes to balancing forecasting performance and timing computation properly; (2) the applicable deterministic and uncertainty forecasting results can be obtained by the improved deep network, namely QRConvSLSTM; (3) appropriate parameters of PSR and feature selection can be effectively optimized by the proposed synchronous optimization; (4) the application of REC possesses positive effects on further compensating the ultimate forecasting results.
机译：可靠的风速预测框架可以有助于有效地处理功率系统的合理调度和安全操作。为此目的，在本研究中研究了一种新的复合框架耦合分解技术，本研究，子晶体聚集，同步优化，改进的深网络和残余误差校正（REC）。首先，采用时间变化的基于滤波器的经验模式分解（TVF-EMD）来将原始系列分解为一组子系列，基于模糊熵（FE）理论和近似标准进一步聚合。然后采用由混合编码的Harris Hawks优化（HHO）实现的同步优化来优化相位空间重构（PSR）的参数和每个聚合的子系统的适用功能。随后，将量子回归（QR）结合到改进的深网络中，即卷积简化的长短期存储器网络（QRConvslstm），用于推导每个聚合的子系列的条件量级，其中应用于上面获得的优化参数来构造最佳的输入矩阵。稍后，初始点预测结果可以基于累积所有聚合的子系列的条件量级来计算，而相应的错误系列被推导出来。然后，QRConvslstm根据REC策略估计错误系列的条件量数，之后通过将原始系列的条件量级和错误系列求和来计算最终条件量程。最后，采用核密度估计（KDE）来估计根据最终条件量级的风速系列的概率密度函数（PDF）。为了验证所提出的复合框架的效率和有效性，对比较实验的三个数据集进行了九种相关模型，其中广泛地证明和分析了点，间隔和概率预测的结果。实验结果表明：（1）集成TVF-EMD和FE基地聚合的数据预处理策略有助于平衡预测性能和正时计算; （2）可以通过改进的深网络，即QRConvslstm获得适用的确定性和不确定性预测结果; （3）通过所提出的同步优化可以有效地优化PSR和特征选择的适当参数; （4）REC的应用对进一步补偿最终预测结果具有积极影响。

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