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何耀耀

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[21]Yaoyao He，Jinhong Wan，Flood Hazard Assessment based on Fuzzy Clustering Iterative Model and Chaotic Particle Swarm Optimization,.[J:Journal of Ambient Intelligence and Humanized Computing,2021,12(1):933-942.
[22]Yaoyao He，Wanying Zhang，Probability density forecasting of wind power based on a multi-core parallel quantile regression neural network method:Knowledge-Based Systems,2020,209:):106431.
[23]Yaoyao He，Yang Qin,Shanlin Yang，Medium-term Power Load Probability Density Forecasting Method based on LASSO Quantile Regression.[J:Systems Engineering - Theory & Practice,2019,39(7):1845-1854.
[24]Yaoyao He，Yang Qin,Xiaohui Lei,Nanping Feng，A study on short-term power load probability density forecasting considering wind power eﬀects.[J:International Journal of Electrical Power and Energy Systems,2019,113:):502-514.
[25]Yaoyao He，Yudong Yan,QIfa Xu，Wind and solar power probability density prediction via fuzzy information granulation and support vector quantile regression.[J:International Journal of Electrical Power and Energy Systems,2019,113:):515-527.
[26]Yaoyao He，Yaya Zheng,Qifa Xu，Forecasting energy consumption in Anhui province of China through two Box-Cox transformation quantile regression probability density methods.[J:Measurement,2019,136):579-593.
[27]Yaoyao He，Yang Qin,Shuo Wang,Xu Wang,Chao Wang，Electricity consumption probability density forecasting method based on LASSO-Quantile Regression Neural Network.[J:Applied Energy,2019,233-234:):565-575.
[28]Yaoyao He，Qifa Xu,Jinhong Wan,Shanlin Yang，Electrical load forecasting based on self-adaptive chaotic neural network using Chebyshev map.[J:Neural Computing and Applications,2018,29(7):603-612.
[29]Yaoyao He，Yaya Zheng，Short-term power load probability density forecasting based on Yeo-Johnson transformation quantile regression and Gaussian kernel function:Energy,2018,154):143-156.
[30]Yaoyao He，Haiyan Li，Probability density forecasting of wind power using quantile regression neural network and kernel density estimation.[J:Energy Conversion and Management,2018,164):374-384.

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