With the growing penetration of wind power in power systems, more accurate prediction of wind speed and wind power is required for real-time scheduling and operation. In this paper, a novel forecast model for shortter...With the growing penetration of wind power in power systems, more accurate prediction of wind speed and wind power is required for real-time scheduling and operation. In this paper, a novel forecast model for shortterm prediction of wind speed and wind power is proposed,which is based on singular spectrum analysis(SSA) and locality-sensitive hashing(LSH). To deal with the impact of high volatility of the original time series, SSA is applied to decompose it into two components: the mean trend,which represents the mean tendency of the original time series, and the fluctuation component, which reveals the stochastic characteristics. Both components are reconstructed in a phase space to obtain mean trend segments and fluctuation component segments. After that, LSH is utilized to select similar segments of the mean trend segments, which are then employed in local forecasting, so that the accuracy and efficiency of prediction can be enhanced. Finally, support vector regression is adopted forprediction, where the training input is the synthesis of the similar mean trend segments and the corresponding fluctuation component segments. Simulation studies are conducted on wind speed and wind power time series from four databases, and the final results demonstrate that the proposed model is more accurate and stable in comparison with other models.展开更多
为了提高日极大风风速的预报能力,特别是8级以上风力的预报,本文以欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts, ECWMF)模式输出的过去3 h阵风风速预报作为输入因子,同时针对ECWMF模式过去3 h阵风风速预...为了提高日极大风风速的预报能力,特别是8级以上风力的预报,本文以欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts, ECWMF)模式输出的过去3 h阵风风速预报作为输入因子,同时针对ECWMF模式过去3 h阵风风速预报存在的小量级风预报偏大、大量级风预报偏小的预报特征,利用近5年地面观测实况以及ECWMF模式过去3 h阵风资料,构建基于Tabnet的日极大风分级订正预报模型。其中,模型的输入设计包含了前期实况、站点的地理信息、ECWMF模式的预报场及其前期预报误差项。该模型在1年半独立检验样本的估测结果中,其预报模型的平均绝对误差相对ECWMF模式插值降低了45.2%,相应的均方根误差也减少了25.7%。进一步地,在1~5级和8~9级以上风力等级的预报上,该预报模型的预报准确率较利用ECWMF模式预报场插值得到的预报方法均有明显提高,表明该预报方法的可行性。展开更多
Amid the randomness and volatility of wind speed, an improved VMD-BP-CNN-LSTM model for short-term wind speed prediction was proposed to assist in power system planning and operation in this paper. Firstly, the wind s...Amid the randomness and volatility of wind speed, an improved VMD-BP-CNN-LSTM model for short-term wind speed prediction was proposed to assist in power system planning and operation in this paper. Firstly, the wind speed time series data was processed using Variational Mode Decomposition (VMD) to obtain multiple frequency components. Then, each individual frequency component was channeled into a combined prediction framework consisting of BP neural network (BPNN), Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) after the execution of differential and normalization operations. Thereafter, the predictive outputs for each component underwent integration through a fully-connected neural architecture for data fusion processing, resulting in the final prediction. The VMD decomposition technique was introduced in a generalized CNN-LSTM prediction model;a BPNN model was utilized to predict high-frequency components obtained from VMD, and incorporated a fully connected neural network for data fusion of individual component predictions. Experimental results demonstrated that the proposed improved VMD-BP-CNN-LSTM model outperformed other combined prediction models in terms of prediction accuracy, providing a solid foundation for optimizing the safe operation of wind farms.展开更多
本文分析并介绍了各种国内外开源数据的发展、来源和共享情况,提出了开源数据的相关概念,并结合近年来开源数据在国内外电力工程实践应用案例,如MM5模式在750 k V哈密-吐鲁番-乌鲁木齐北输电线路中的应用、核电站温排水热污染遥感监测...本文分析并介绍了各种国内外开源数据的发展、来源和共享情况,提出了开源数据的相关概念,并结合近年来开源数据在国内外电力工程实践应用案例,如MM5模式在750 k V哈密-吐鲁番-乌鲁木齐北输电线路中的应用、核电站温排水热污染遥感监测等案例,并列举了开源数据在国外电力工程中的应用和发展方向,指出了其优势和存在的问题并给出了建议,对开源数据在国内外电力工程中的顺利实施提供了参考和建议。展开更多
基金supported by the Guangdong Innovative Research Team Program(No.201001N0104744201)the State Key Program of the National Natural Science Foundation of China(No.51437006)
文摘With the growing penetration of wind power in power systems, more accurate prediction of wind speed and wind power is required for real-time scheduling and operation. In this paper, a novel forecast model for shortterm prediction of wind speed and wind power is proposed,which is based on singular spectrum analysis(SSA) and locality-sensitive hashing(LSH). To deal with the impact of high volatility of the original time series, SSA is applied to decompose it into two components: the mean trend,which represents the mean tendency of the original time series, and the fluctuation component, which reveals the stochastic characteristics. Both components are reconstructed in a phase space to obtain mean trend segments and fluctuation component segments. After that, LSH is utilized to select similar segments of the mean trend segments, which are then employed in local forecasting, so that the accuracy and efficiency of prediction can be enhanced. Finally, support vector regression is adopted forprediction, where the training input is the synthesis of the similar mean trend segments and the corresponding fluctuation component segments. Simulation studies are conducted on wind speed and wind power time series from four databases, and the final results demonstrate that the proposed model is more accurate and stable in comparison with other models.
文摘为了提高日极大风风速的预报能力,特别是8级以上风力的预报,本文以欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts, ECWMF)模式输出的过去3 h阵风风速预报作为输入因子,同时针对ECWMF模式过去3 h阵风风速预报存在的小量级风预报偏大、大量级风预报偏小的预报特征,利用近5年地面观测实况以及ECWMF模式过去3 h阵风资料,构建基于Tabnet的日极大风分级订正预报模型。其中,模型的输入设计包含了前期实况、站点的地理信息、ECWMF模式的预报场及其前期预报误差项。该模型在1年半独立检验样本的估测结果中,其预报模型的平均绝对误差相对ECWMF模式插值降低了45.2%,相应的均方根误差也减少了25.7%。进一步地,在1~5级和8~9级以上风力等级的预报上,该预报模型的预报准确率较利用ECWMF模式预报场插值得到的预报方法均有明显提高,表明该预报方法的可行性。
文摘Amid the randomness and volatility of wind speed, an improved VMD-BP-CNN-LSTM model for short-term wind speed prediction was proposed to assist in power system planning and operation in this paper. Firstly, the wind speed time series data was processed using Variational Mode Decomposition (VMD) to obtain multiple frequency components. Then, each individual frequency component was channeled into a combined prediction framework consisting of BP neural network (BPNN), Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) after the execution of differential and normalization operations. Thereafter, the predictive outputs for each component underwent integration through a fully-connected neural architecture for data fusion processing, resulting in the final prediction. The VMD decomposition technique was introduced in a generalized CNN-LSTM prediction model;a BPNN model was utilized to predict high-frequency components obtained from VMD, and incorporated a fully connected neural network for data fusion of individual component predictions. Experimental results demonstrated that the proposed improved VMD-BP-CNN-LSTM model outperformed other combined prediction models in terms of prediction accuracy, providing a solid foundation for optimizing the safe operation of wind farms.
文摘本文分析并介绍了各种国内外开源数据的发展、来源和共享情况,提出了开源数据的相关概念,并结合近年来开源数据在国内外电力工程实践应用案例,如MM5模式在750 k V哈密-吐鲁番-乌鲁木齐北输电线路中的应用、核电站温排水热污染遥感监测等案例,并列举了开源数据在国外电力工程中的应用和发展方向,指出了其优势和存在的问题并给出了建议,对开源数据在国内外电力工程中的顺利实施提供了参考和建议。
