A generalized regression model based on hybrid empirical mode decomposition and support vector regression with back‐propagation neural network for mid‐short‐term load forecasting-Reference-Cited by-同舟云学术

A generalized regression model based on hybrid empirical mode decomposition and support vector regression with back‐propagation neural network for mid‐short‐term load forecasting

Published:2020-02-03 Issue:5 Volume:39 Page:737-756
ISSN:0277-6693
Container-title:Journal of Forecasting
language:en
Short-container-title:Journal of Forecasting

Author:

Fan Guo‐Feng¹,Guo Yan‐Hui¹,Zheng Jia‐Mei¹,Hong Wei‐Chiang²

Affiliation:

1. School of Mathematics and Statistics SciencePing Ding Shan University, Ping Ding Shan Henan China

2. Department of Information ManagementOriental Institute of Technology New Taipei 220 Taiwan

Funder

Jiangsu Normal University

Scientific Startup Foundation for Doctors of Northwest A and F University

Publisher

Wiley

Subject

Management Science and Operations Research,Statistics, Probability and Uncertainty,Strategy and Management,Computer Science Applications,Modelling and Simulation

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/for.2655

Reference47 articles.

1. Medium-term electric load forecasting using singular value decomposition

2. Short-term electricity demand forecasting with MARS, SVR and ARIMA models using aggregated demand data in Queensland, Australia

3. Midterm Demand Prediction of Electrical Power Systems Using a New Hybrid Forecast Technique

4. The macroeconomic impact of renewable electricity power generation projects

5. SVR optimization with soft computing algorithms for incipient SGTR diagnosis

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