NDLI: One-hour ahead electric load and wind-solar power generation forecasting using artificial neural network

Content Provider	IEEE Xplore Digital Library
Author	Laouafi, A. Mordjaoui, M. Dib, D.
Copyright Year	2015
Description	Author affiliation: Dept. of Electr. Eng., Univ. of Tebessa, Tebessa, Algeria (Dib, D.) \|\| Dept. of Electr. Eng., Univ. of 20 August 1955, Skikda, Algeria (Laouafi, A.; Mordjaoui, M.)
Abstract	Forecasting electricity demand is a key activity in power systems as it is one of the most important entries for production planning; particularly in liberalized, deregulated markets. With the growing penetration of renewable energy sources, there is a pressing need for better load forecasting, since the generated power in wind and solar farms cannot be scheduled and dispatched in the classical sense. Consequently, in addition to the need of accurate load forecasts, a reliable forecasting method of such intermittent energy resources is an important issue that can helps the grid operators to better manage supply/demand balance. The purpose of this work is to develop a feed-forward back propagation neural network (FF-BPNN) based approach for performing hour-ahead electricity demand and wind-solar power generation forecasting. Results from real-world case study; based on the quarter-hourly electricity demand and power generation data in French, are presented in order to illustrate the proficiency of the proposed method. With an average MAPE value of electricity demand, wind, and solar power forecasting respectively equal to 0.765%, 6.008%, and 6.414%; the effectiveness of the proposed methodology is clearly implied.
Starting Page	1
Ending Page	6
File Size	549147
Page Count	6
File Format	PDF
ISBN	9781479979479
DOI	10.1109/IREC.2015.7110894
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-03-24
Publisher Place	Tunisia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Load forecasting Electric load Wind power generation Predictive models Solar power Artificial neural network Wind power Forecasting Wind forecasting Load modeling
Content Type	Text
Resource Type	Article

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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
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