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Selecting the key control parameters for the ionospheric total electron content nowcasting

Zhukov A.V., Sidorov D.N., Mylnikova A.A., Yasyukevich Yu.V. Selecting the key control parameters for the ionospheric total electron content nowcasting // Sovremennye Problemy Distantsionnogo Zondirovaniya Zemli iz Kosmosa. Vol.15. №3. 2018. P.263-272. DOI: 10.21046/2070-7401-2018-15-3-263-272 Nowcasting the dynamics of ionospheric parameters is an actual and at the same time rather complicated task. One of the main issues is the selection of control parameters for constructing accurate predictive...

Теги: absolute total electron content , gradient boosting , machine learning , nowcasting , random forest , support vector machine
Machine learning in electric power systems adequacy assessment using Monte-Carlo method

... using Monte-Carlo method. To attack this problem the novel method is suggested to reduce number of random states to be analyzed. The machine learning methods are employed for electric power system states precalculated classification. Random forest and support vector machine methods are proposed to use for solving this problem. Efficiency of proposed approach is demonstrated on test scheme. © 2017 IEEE. входит

Теги: adequacy assessment , electric power systems , machine learning , monte carlo method , random forest , support vector machine , artificial intelligence , computational efficiency , decision trees , efficiency , learning systems , problem solving , support vector mach
A combined forecasting approach with model self-adjustment for renewable generations and energy loads in smart community

... Then, a combined forecasting approach, which enables to build a real-time forecasting model with parameters self-adjustment, is proposed for the forecasting of the net load in smart community. Compared with the traditional forecasting methods such as support vector machine (SVM), the proposed approach can wavily optimize the parameters of the forecasting model. Besides, an optimal method named Grid-GA searching is developed to reduce the computation time during the forecasting. Therefore, it can improve ...

Теги: wind power , photovoltaic generation , support vector machine , combined forecasting , smart community , extreme learning-machine , wavelet transform , neural-network , optimization , hybrid , regression , algorithm , management , design


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