NDLI: Improving supervised wind power forecasting models using extended numerical weather variables and unlabelled data

Content Provider	IET Digital Library
Author	Fang, Shengchen Chiang, Hsiao Dong
Abstract	A variety of supervised forecasting models using numerical weather prediction data have been utilised for short-term wind power forecasting. These forecasting models only use meteorological variables of the target wind farm as essential features. This study proposes a novel method to improve existing supervised forecasting models such as support vector machines, artificial neural networks, and Gaussian processes (GPs) for higher forecasting accuracy. The proposed method develops a data-driven feature extraction procedure to utilise unlabelled numerical weather data, and the feature extraction procedure transforms extended numerical weather variables into supplementary input features which then can be used for supervised forecasting models. The only modification to an existing supervised forecasting model is the addition of these supplementary input features, and thus does not alter the training algorithm of the supervised forecasting model. For illustrative purposes, the GP is used as the supervised forecasting model to be improved. Numerical evaluation of the proposed method was performed on a subset of data provided in the 2012 Global Energy Forecasting Competition (GEF 2012). Evaluation results reveal that the proposed method achieves higher forecasting accuracy for all wind farms.
Starting Page	1616
Ending Page	1624
Page Count	9
ISSN	17521416
Volume Number	10
e-ISSN	17521424
Issue Number	Issue 10, Nov (2016)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rpg/10/10
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rpg.2016.0339
Journal	IET Renewable Power Generation
Publisher	The Institution of Engineering and Technology
Publisher Date	2016-07-12
Access Restriction	Open
Rights License	Creative Commons Attribution-No Derivs License (http://creativecommons.org/licenses/by-nd/3.0/)
Subject Keyword	Artificial Neural Network Data-driven Feature Extraction Procedure Extended Numerical Weather Variables Feature Extraction Gaussian Processes Global Energy Forecasting Competition Knowledge Engineering Technique Load Forecasting Meteorological Variables Neural Computing Technique Neural Nets Numerical Analysis Numerical Evaluation Numerical Method Numerical Weather Prediction Data Power Engineering Computing Power System Planning And Layout Short-term Wind Power Forecasting Statistics Supervised Forecasting Model Supervised Wind Power Forecasting Model Support Vector Machine Unlabelled Data Wind Farm Wind Power Plant
Content Type	Text
Resource Type	Article
Subject	Renewable Energy, Sustainability and the Environment

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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