Machine Learning-Based Probabilistic Forecasting of Wind Power Generation: A Combined Bootstrap and Cumulant Method
Author:
Affiliation:
1. College of Electrical Engineering, Zhejiang University, Hangzhou, China
2. State Key Laboratory of Internet of Things for Smart City, University of Macau, Taipa, Macau SAR, China
Funder
National Natural Science Foundation of China
Zhejiang Provincial Natural Science Foundation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Energy Engineering and Power Technology
Link
http://xplorestaging.ieee.org/ielx7/59/10375285/10093077.pdf?arnumber=10093077
Reference40 articles.
1. Security-Constrained Unit Commitment With Volatile Wind Power Generation
2. Probabilistic Forecasting of Wind Power Generation Using Extreme Learning Machine
3. Multi-Area Unit Scheduling and Reserve Allocation Under Wind Power Uncertainty
4. Nonparametric Probabilistic Optimal Power Flow
5. Optimal Prediction Intervals of Wind Power Generation
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