A Hybrid Model for Long-Term Wind Power Forecasting Utilizing NWP Subsequence Correction and Multi-Scale Deep Learning Regression Methods
Author:
Affiliation:
1. College of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
2. College of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China
Funder
National Natural Science Foundation of China
General Program of Shenzhen Science and Technology Research and Development Fund
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Renewable Energy, Sustainability and the Environment
Link
http://xplorestaging.ieee.org/ielx7/5165391/10367191/10144668.pdf?arnumber=10144668
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5. Short-term offshore wind power forecasting - A hybrid model based on Discrete Wavelet Transform (DWT), Seasonal Autoregressive Integrated Moving Average (SARIMA), and deep-learning-based Long Short-Term Memory (LSTM)
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