A macroscopical anomaly detection method based on intrusion statistic and Bayesian dynamic forecast is presented. A large number of alert data that cannot be dealt with in time are always aggregated in control centers...A macroscopical anomaly detection method based on intrusion statistic and Bayesian dynamic forecast is presented. A large number of alert data that cannot be dealt with in time are always aggregated in control centers of large-scale intrusion detection systems. In order to improve the efficiency and veracity of intrusion analysis, the intrusion intensity values are picked from alert data and Bayesian dynamic forecast method is used to detect anomaly. The experiments show that the new method is effective on detecting macroscopical anomaly in large-scale intrusion detection systems.展开更多
Photovoltaic(PV)systems are widely spread across MV and LV distribution systems and the penetration of PV generation is solidly growing.Because of the uncertain nature of the solar energy resource,PV power forecasting...Photovoltaic(PV)systems are widely spread across MV and LV distribution systems and the penetration of PV generation is solidly growing.Because of the uncertain nature of the solar energy resource,PV power forecasting models are crucial in any energy management system for smart distribution networks.Although point forecasts can suit many scopes,probabilistic forecasts add further flexibility to an energy management system and are recommended to enable a wider range of decision making and optimization strategies.This paper proposes methodology towards probabilistic PV power forecasting based on a Bayesian bootstrap quantile regression model,in which a Bayesian bootstrap is applied to estimate the parameters of a quantile regression model.A novel procedure is presented to optimize the extraction of the predictive quantiles from the bootstrapped estimation of the related coefficients,raising the predictive ability of the final forecasts.Numerical experiments based on actual data quantify an enhancement of the performance of up to 2.2%when compared to relevant benchmarks.展开更多
To solve the problem of information fusion from multiple sources in innovation alliances, an information fusion model based on the Bayesian network is presented. The multi-source information fusion process of innovati...To solve the problem of information fusion from multiple sources in innovation alliances, an information fusion model based on the Bayesian network is presented. The multi-source information fusion process of innovation alliances was classified into three layers, namely, the information perception layer, the feature clustering layer,and the decision fusion layer. The agencies in the alliance were defined as sensors through which information is perceived and obtained, and the features were clustered. Finally, various types of information were fused by the innovation alliance based on the fusion algorithm to achieve complete and comprehensive information. The model was applied to a study on economic information prediction, where the accuracy of the fusion results was higher than that from a single source and the errors obtained were also smaller with the MPE less than 3%, which demonstrates the proposed fusion method is more effective and reasonable. This study provides a reasonable basis for decision-making of innovation alliances.展开更多
文摘A macroscopical anomaly detection method based on intrusion statistic and Bayesian dynamic forecast is presented. A large number of alert data that cannot be dealt with in time are always aggregated in control centers of large-scale intrusion detection systems. In order to improve the efficiency and veracity of intrusion analysis, the intrusion intensity values are picked from alert data and Bayesian dynamic forecast method is used to detect anomaly. The experiments show that the new method is effective on detecting macroscopical anomaly in large-scale intrusion detection systems.
基金supported by the Swiss Federal Office of Energy(SFOE)and by the Italian Ministry of Education,University and Research(MIUR),through the ERA-NET Smart Energy Systems RegSys joint call 2018 project“DiGRiFlex-Real time Distribution GRid control and Flexibility provision under uncertainties.”。
文摘Photovoltaic(PV)systems are widely spread across MV and LV distribution systems and the penetration of PV generation is solidly growing.Because of the uncertain nature of the solar energy resource,PV power forecasting models are crucial in any energy management system for smart distribution networks.Although point forecasts can suit many scopes,probabilistic forecasts add further flexibility to an energy management system and are recommended to enable a wider range of decision making and optimization strategies.This paper proposes methodology towards probabilistic PV power forecasting based on a Bayesian bootstrap quantile regression model,in which a Bayesian bootstrap is applied to estimate the parameters of a quantile regression model.A novel procedure is presented to optimize the extraction of the predictive quantiles from the bootstrapped estimation of the related coefficients,raising the predictive ability of the final forecasts.Numerical experiments based on actual data quantify an enhancement of the performance of up to 2.2%when compared to relevant benchmarks.
基金supported by the National Natural Science Foundation of China(Nos.71472053,71429001,and91646105)
文摘To solve the problem of information fusion from multiple sources in innovation alliances, an information fusion model based on the Bayesian network is presented. The multi-source information fusion process of innovation alliances was classified into three layers, namely, the information perception layer, the feature clustering layer,and the decision fusion layer. The agencies in the alliance were defined as sensors through which information is perceived and obtained, and the features were clustered. Finally, various types of information were fused by the innovation alliance based on the fusion algorithm to achieve complete and comprehensive information. The model was applied to a study on economic information prediction, where the accuracy of the fusion results was higher than that from a single source and the errors obtained were also smaller with the MPE less than 3%, which demonstrates the proposed fusion method is more effective and reasonable. This study provides a reasonable basis for decision-making of innovation alliances.
