A Neural Approach to Electricity Demand Forecasting-Reference-Cited by-同舟云学术

A Neural Approach to Electricity Demand Forecasting

Published:2016 Issue: Volume: Page:281-306
ISSN:1860-949X
Container-title:Artificial Neural Network Modelling
language:
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Author:

Motlagh Omid,Grozev George,Papageorgiou Elpiniki I.

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-319-28495-8_12

Reference60 articles.

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3. C.-H. Wang, G. Grozev, S. Seo, Decomposition and statistical analysis for regional electricity demand forecasting. Energy 41(1), 313–325 (2012)

4. AEMO, Australian Energy Market Operator’s Historical demand. (AEMO Web, 2013), http://www.nemweb.com.au/REPORTS/Archive/HistDemand/ . Accessed 27 Oct 2013

5. L. Hernández, C. Baladrón, J.M. Aguiar, L. Calavia, B. Carro, A study of the relationship between weather variables and electric power demand inside a smart grid. Sensors 12, 11571–11591 (2012)

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