Random forest ensemble of support vector regression models for solar power forecasting

Mohamed Abuella; Badrul Chowdhury

doi:10.1109/ISGT.2017.8086027

Random forest ensemble of support vector regression models for solar power forecasting

Mohamed Abuella
, Badrul Chowdhury

University of North Carolina at Charlotte

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Original language	English
Title of host publication	2017 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538628904
DOIs	https://doi.org/10.1109/ISGT.2017.8086027
State	Published - Oct 26 2017
Externally published	Yes
Event	2017 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2017 - Washington, United States Duration: Apr 23 2017 → Apr 26 2017

Publication series

Name	2017 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2017

Conference

Conference	2017 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2017
Country/Territory	United States
City	Washington
Period	4/23/17 → 4/26/17

ASJC Scopus Subject Areas

Artificial Intelligence
Computer Networks and Communications
Energy Engineering and Power Technology
Renewable Energy, Sustainability and the Environment
Control and Optimization

Keywords

Ensemble learning
Post-processing
Random forest
Solar power
Support vector regression

Access to Document

10.1109/ISGT.2017.8086027

Cite this

Abuella, M., & Chowdhury, B. (2017). Random forest ensemble of support vector regression models for solar power forecasting. In 2017 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2017 Article 8086027 (2017 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISGT.2017.8086027

Random forest ensemble of support vector regression models for solar power forecasting. / Abuella, Mohamed; Chowdhury, Badrul.
2017 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2017. Institute of Electrical and Electronics Engineers Inc., 2017. 8086027 (2017 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2017).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abuella, M & Chowdhury, B 2017, Random forest ensemble of support vector regression models for solar power forecasting. in 2017 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2017., 8086027, 2017 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2017, Institute of Electrical and Electronics Engineers Inc., 2017 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2017, Washington, United States, 4/23/17. https://doi.org/10.1109/ISGT.2017.8086027

Abuella M, Chowdhury B. Random forest ensemble of support vector regression models for solar power forecasting. In 2017 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2017. Institute of Electrical and Electronics Engineers Inc. 2017. 8086027. (2017 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2017). doi: 10.1109/ISGT.2017.8086027

Abuella, Mohamed ; Chowdhury, Badrul. / Random forest ensemble of support vector regression models for solar power forecasting. 2017 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2017. Institute of Electrical and Electronics Engineers Inc., 2017. (2017 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2017).

@inproceedings{54a23416fd6f41e688f59d229a7875a7,

title = "Random forest ensemble of support vector regression models for solar power forecasting",

keywords = "Ensemble learning, Post-processing, Random forest, Solar power, Support vector regression",

author = "Mohamed Abuella and Badrul Chowdhury",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2017 ; Conference date: 23-04-2017 Through 26-04-2017",

year = "2017",

month = oct,

day = "26",

doi = "10.1109/ISGT.2017.8086027",

language = "English",

series = "2017 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2017 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2017",

}

TY - GEN

T1 - Random forest ensemble of support vector regression models for solar power forecasting

AU - Abuella, Mohamed

AU - Chowdhury, Badrul

PY - 2017/10/26

Y1 - 2017/10/26

KW - Ensemble learning

KW - Post-processing

KW - Random forest

KW - Solar power

KW - Support vector regression

UR - https://www.scopus.com/pages/publications/85040160947

UR - https://www.scopus.com/pages/publications/85040160947#tab=citedBy

U2 - 10.1109/ISGT.2017.8086027

DO - 10.1109/ISGT.2017.8086027

M3 - Conference contribution

AN - SCOPUS:85040160947

T3 - 2017 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2017

BT - 2017 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2017

Y2 - 23 April 2017 through 26 April 2017

ER -

Random forest ensemble of support vector regression models for solar power forecasting

Publication series

Conference

ASJC Scopus Subject Areas

Keywords

Access to Document

Other files and links

Fingerprint

Cite this