As one of the hot topics in the field of new energy,short-term wind power prediction research should pay attention to the impact of meteorological characteristics on wind power while improving the prediction accuracy....As one of the hot topics in the field of new energy,short-term wind power prediction research should pay attention to the impact of meteorological characteristics on wind power while improving the prediction accuracy.Therefore,a short-term wind power prediction method based on the combination of meteorological features and Cat Boost is presented.Firstly,morgan-stone algebras and sure independence screening(MS-SIS)method is designed to filter the meteorological features,and the influence of the meteorological features on the wind power is explored.Then,a sort enhancement algorithm is designed to increase the accuracy and calculation efficiency of the method and reduce the prediction risk of a single element.Finally,a prediction method based on Cat Boost network is constructed to further realize short-term wind power prediction.The National Renewable Energy Laboratory(NREL)dataset is used for experimental analysis.The results show that the short-term wind power prediction method based on the combination of meteorological features and Cat Boost not only improve the prediction accuracy of short-term wind power,but also have higher calculation efficiency.展开更多
In view of the fact that current data delivery methods are not enough to meet the security requirements of today’s distributed crowd sensing,and the data delivery methods are not flexible enough,this paper proposes a...In view of the fact that current data delivery methods are not enough to meet the security requirements of today’s distributed crowd sensing,and the data delivery methods are not flexible enough,this paper proposes a crowd sensing data interaction method based on tangle directed acyclic graph(DAG)network.In this method,users and platforms are regarded as nodes of the network in the process of performing crowd sensing tasks.First,the heaviest chain is generated through the main chain strategy to ensure the stability of the network.Then,the hidden Markov model(HMM)prediction model is used to improve the correlation of the perceived data to improve the performance.Then,the confidential transaction and commitment algorithm is used to ensure the reliability of the transaction,overcome the security risks faced by the trusted third party,and simplify the group intelligence aware transaction mode.Finally,through simulation experiments,the security and feasibility of the group intelligence aware data delivery method based on tangle DAG network are verified.展开更多
基金Supported by the National Science and Technology Basic Work Project of China Meteorological Administration(2005DKA31700-06)Innovation Fund of Public Meteorological Service Center of China Meteorological Administration(M2020013)。
文摘As one of the hot topics in the field of new energy,short-term wind power prediction research should pay attention to the impact of meteorological characteristics on wind power while improving the prediction accuracy.Therefore,a short-term wind power prediction method based on the combination of meteorological features and Cat Boost is presented.Firstly,morgan-stone algebras and sure independence screening(MS-SIS)method is designed to filter the meteorological features,and the influence of the meteorological features on the wind power is explored.Then,a sort enhancement algorithm is designed to increase the accuracy and calculation efficiency of the method and reduce the prediction risk of a single element.Finally,a prediction method based on Cat Boost network is constructed to further realize short-term wind power prediction.The National Renewable Energy Laboratory(NREL)dataset is used for experimental analysis.The results show that the short-term wind power prediction method based on the combination of meteorological features and Cat Boost not only improve the prediction accuracy of short-term wind power,but also have higher calculation efficiency.
文摘In view of the fact that current data delivery methods are not enough to meet the security requirements of today’s distributed crowd sensing,and the data delivery methods are not flexible enough,this paper proposes a crowd sensing data interaction method based on tangle directed acyclic graph(DAG)network.In this method,users and platforms are regarded as nodes of the network in the process of performing crowd sensing tasks.First,the heaviest chain is generated through the main chain strategy to ensure the stability of the network.Then,the hidden Markov model(HMM)prediction model is used to improve the correlation of the perceived data to improve the performance.Then,the confidential transaction and commitment algorithm is used to ensure the reliability of the transaction,overcome the security risks faced by the trusted third party,and simplify the group intelligence aware transaction mode.Finally,through simulation experiments,the security and feasibility of the group intelligence aware data delivery method based on tangle DAG network are verified.
