The increasing integration of renewable energy sources introduces significant variability and non-stationarity into power system,challenging accurate net load forecasting.Although net load forecasting research has dev...The increasing integration of renewable energy sources introduces significant variability and non-stationarity into power system,challenging accurate net load forecasting.Although net load forecasting research has devoted considerable efforts to handle non-stationarity-via normalization,incremental learning,or drift detection-existing solutions often suffer from hyperparameter tuning,threshold-based triggers,or reliance on specialized architectures.To overcome these limitations,we propose Adaptive Smoothing Drift Normalization(ASDN),a lightweight normalization layer that continuously adapts to distribution shifts without threshold tuning.ASDN effectively adapts to new data via a mechanism that combines entropy-based adjustments with a dynamic filtering approach.At the same time,it maintains stability with respect to historical patterns,allowing the method to capture both gradual and abrupt shifts in the data distribution.We provide a theoretical guarantee that the estimation error of ASDN remains bounded under piecewise-stationary drift;as incremental drift and noise decrease,this bound tightens and converges to zero.Experiments on nine forecasting models across five public datasets and four prediction horizons show that ASDN consistently outperforms traditional normalization techniques,reducing mean squared error and enhancing robustness.These results confirm ASDN’s effectiveness in handling complex temporal dynamics,making it valuable for improving forecast accuracy in dynamic renewable power systems.展开更多
Wave shapes that induce velocity skewness and acceleration asymmetry are usually responsible for onshore sediment transport, whereas undertow and bottom slope effect normally contribute to offshore sediment transport....Wave shapes that induce velocity skewness and acceleration asymmetry are usually responsible for onshore sediment transport, whereas undertow and bottom slope effect normally contribute to offshore sediment transport. By incorporating these counteracting driving forces in a phase-averaged manner, the theoretically-based quasi-steady formula of Wang （2007） is modified to predict the magnitude and direction of net cross-shore total load transport under the coaction of wave and current. The predictions show an excellent agreement with the measurement data on medium and fine sand collected by Dohmen-Janssen and Hanes （2002） and Schretlen （2012） in a full-scale wave flume at the Coastal Research Centre in Hannover, Germany. The modified formula can predict the net onshore transport of fine sand in sheet flows. In particular, it can predict the net offshore transport of medium sand in rippled beds through enlarged bed roughness, as well as the net offshore transport of fine-to-coarse sand in sheet flows with the aid of a new criterion to judge the occurrence of net offshore transport.展开更多
提出了一种基于.NET的电力负荷预测系统总体架构,并对本系统的需求分析、整体架构和安全性设计进行了阐述。按照软件工程规范的要求,详细设计了系统各功能模块:负荷分析、负荷预测、数据接收与上报管理、考核管理、数据查询与报表管理...提出了一种基于.NET的电力负荷预测系统总体架构,并对本系统的需求分析、整体架构和安全性设计进行了阐述。按照软件工程规范的要求,详细设计了系统各功能模块:负荷分析、负荷预测、数据接收与上报管理、考核管理、数据查询与报表管理、系统维护等六个模块。重点阐述了系统实现的关键技术,如NetAdvantage组件、XML Web Service技术和负荷数据读取实现。展开更多
基金supported by the Research Grants Council of the Hong Kong Special Administrative Region,China(Project Reference No.AoE/P-601/23-N)。
文摘The increasing integration of renewable energy sources introduces significant variability and non-stationarity into power system,challenging accurate net load forecasting.Although net load forecasting research has devoted considerable efforts to handle non-stationarity-via normalization,incremental learning,or drift detection-existing solutions often suffer from hyperparameter tuning,threshold-based triggers,or reliance on specialized architectures.To overcome these limitations,we propose Adaptive Smoothing Drift Normalization(ASDN),a lightweight normalization layer that continuously adapts to distribution shifts without threshold tuning.ASDN effectively adapts to new data via a mechanism that combines entropy-based adjustments with a dynamic filtering approach.At the same time,it maintains stability with respect to historical patterns,allowing the method to capture both gradual and abrupt shifts in the data distribution.We provide a theoretical guarantee that the estimation error of ASDN remains bounded under piecewise-stationary drift;as incremental drift and noise decrease,this bound tightens and converges to zero.Experiments on nine forecasting models across five public datasets and four prediction horizons show that ASDN consistently outperforms traditional normalization techniques,reducing mean squared error and enhancing robustness.These results confirm ASDN’s effectiveness in handling complex temporal dynamics,making it valuable for improving forecast accuracy in dynamic renewable power systems.
基金supported by the National Natural Science Foundation of China(Grant No.51179211)
文摘Wave shapes that induce velocity skewness and acceleration asymmetry are usually responsible for onshore sediment transport, whereas undertow and bottom slope effect normally contribute to offshore sediment transport. By incorporating these counteracting driving forces in a phase-averaged manner, the theoretically-based quasi-steady formula of Wang （2007） is modified to predict the magnitude and direction of net cross-shore total load transport under the coaction of wave and current. The predictions show an excellent agreement with the measurement data on medium and fine sand collected by Dohmen-Janssen and Hanes （2002） and Schretlen （2012） in a full-scale wave flume at the Coastal Research Centre in Hannover, Germany. The modified formula can predict the net onshore transport of fine sand in sheet flows. In particular, it can predict the net offshore transport of medium sand in rippled beds through enlarged bed roughness, as well as the net offshore transport of fine-to-coarse sand in sheet flows with the aid of a new criterion to judge the occurrence of net offshore transport.
文摘提出了一种基于.NET的电力负荷预测系统总体架构,并对本系统的需求分析、整体架构和安全性设计进行了阐述。按照软件工程规范的要求,详细设计了系统各功能模块:负荷分析、负荷预测、数据接收与上报管理、考核管理、数据查询与报表管理、系统维护等六个模块。重点阐述了系统实现的关键技术,如NetAdvantage组件、XML Web Service技术和负荷数据读取实现。
