NDLI: Short-term aggregated load and distributed generation forecast using fuzzy grouping approach

Content Provider	IEEE Xplore Digital Library
Author	Rejc, M. Einfalt, A. Gawron-Deutsch, T.
Copyright Year	2015
Description	Author affiliation: Siemens AG - Austria, Vienna, Austria (Rejc, M.; Einfalt, A.; Gawron-Deutsch, T.)
Abstract	The increasing shares of renewable energy sources at low voltage distribution nodes are the cause of increased operational uncertainty. This uncertainty must also be taken into account during operational planning for the short term period, i.e. up to five days ahead. Therefore the system operators must take into account how low-voltage load as well as generation change at each node in the near future. This requires appropriate short-term forecast load and distributed generation (DG) methods. In the past, standard profiles for nodes were used to approximately forecast the expected load in the absence of smart metering and historical time-series storage. With increased number of smart meters in the grid, it is possible to use more complex methods to more accurately forecast system load and generation and therefore ensure secure system operation. In this paper, we present the forecasting approach used for load and distributed generation nodes equipped with smart meters. The presented approach was designed at SIEMENS as one part of the Austrian nationally founded research project ProAktivNetz. The forecasts represent aggregated load and distributed generation nodal injection values. The approach was tested for a small node with 100 households and a 34 kWp photovoltaic power plant. The results show that the grouped forecast results using the fuzzy weighting procedure give better forecasting results than individual methods and significantly better results than using standard profiles for the nodes.
Starting Page	212
Ending Page	217
File Size	508940
Page Count	6
File Format	PDF
e-ISBN	9781479977369
DOI	10.1109/SEDST.2015.7315209
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-08
Publisher Place	Austria
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Time series analysis Weather forecasting Artificial neural networks Short-term forecasts Time series Fuzzy grouping Smart meters Distributed generation Mathematical model Forecasting Standards Autoregressive processes
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	Mr. Siddhartha Mukherjee siddhartha@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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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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