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. (15th October 2020) - Wang, Kai; Fu, Wenlong; Chen, Tie; Zhang, Binqiao; Xiong, Dongzhen; Fang, Ping
Highlights: Subseries aggregation adopting FE is implemented after executing TVF-EMD. Blended coding-based HHO is applied to achieve synchronous optimization. A simplified version of ConvLSTM is developed for wind speed prediction. Quantile regression is incorporated into ConvSLSTM for quantifying uncertainty. Residual error correction is introduced to compensate forecasting results. Abstract: 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.