Short-Term Wind Power Forecast Based on Continuous Conditional Random Field
Author:
Affiliation:
1. Key Laboratory of Power System Intelligent Dispatch and Control, Shandong University, Jinan, China
2. Tsinghua-Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
Funder
National Key R&D Program of China
National Natural Science Foundation of China
China Postdoctoral Science Foundation
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Energy Engineering and Power Technology
Link
http://xplorestaging.ieee.org/ielx7/59/10375285/10109851.pdf?arnumber=10109851
Reference49 articles.
1. Probabilistic Prediction of Regional Wind Power Based on Spatiotemporal Quantile Regression
2. Forecasting the High Penetration of Wind Power on Multiple Scales Using Multi-to-Multi Mapping
3. Improved Deep Mixture Density Network for Regional Wind Power Probabilistic Forecasting
4. A Regional Wind Power Probabilistic Forecast Method Based on Deep Quantile Regression
5. Multistep Wind Power Forecast Using Mean Trend Detector and Mathematical Morphology-Based Local Predictor
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