A novel load forecasting system leveraging database technology

& (2018) A novel load forecasting system leveraging database technology. In Ha, Q T, Nunez, M, Sieminski, A, & Kozierkiewicz, A (Eds.) Modern approaches for intelligent information and database systems (Studies in Computational Intelligence, Volume 769). Springer, Switzerland, pp. 491-503.

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Description

Load forecasting has been a key process in electricity utility companies. While there are demands for utilising data mining to meet the requirements of load forecasting, there are substantial challenges in implementing a big data solution. Cost, expertise and new acquisitions are only some of the reasons that hinder this endeavour. The goal of this paper is to propose an interim load forecasting solution to meet the challenge of using big data, data mining, existing hardware and resource expertise while minimizing the cost and overheads.

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ID Code: 118400
Item Type: Chapter in Book, Report or Conference volume (Chapter)
ORCID iD:
Wee, Cheeorcid.org/0000-0001-6594-0704
Nayak, Richiorcid.org/0000-0002-9954-0159
Measurements or Duration: 13 pages
DOI: 10.1007/978-3-319-76081-0_42
ISBN: 978-3-319-76080-3
Pure ID: 33307732
Divisions: Past > Institutes > Institute for Future Environments
Past > QUT Faculties & Divisions > Science & Engineering Faculty
Copyright Owner: Consult author(s) regarding copyright matters
Copyright Statement: This work is covered by copyright. Unless the document is being made available under a Creative Commons Licence, you must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. If the document is available under a Creative Commons License (or other specified license) then refer to the Licence for details of permitted re-use. It is a condition of access that users recognise and abide by the legal requirements associated with these rights. If you believe that this work infringes copyright please provide details by email to qut.copyright@qut.edu.au
Deposited On: 29 May 2018 00:23
Last Modified: 02 Mar 2024 02:27