Short-term load forecasting with neural network ensembles: A comparative study

Matteo DE FELICE, Xin YAO

Research output: Journal PublicationsReview articleOther Review

126 Citations (Scopus)

Abstract

Load Forecasting plays a critical role in the management, scheduling and dispatching operations in power systems, and it concerns the prediction of energy demand in different time spans. In future electric grids, to achieve a greater control and flexibility than in actual electric grids, a reliable forecasting of load demand could help to avoid dispatch problems given by unexpected loads, and give vital information to make decisions on energy generation and purchase, especially market-based dynamic pricing strategies. Furthermore, accurate prediction would have a significant impact on operation management, e.g. preventing overloading and allowing an efficient energy storage. © 2011 IEEE.
Original languageEnglish
Pages (from-to)47-56
Number of pages10
JournalIEEE Computational Intelligence Magazine
Volume6
Issue number3
Early online date20 Jul 2011
DOIs
Publication statusPublished - Aug 2011
Externally publishedYes

Bibliographical note

Part of this work was done while the first author visited CERC IA, School of Computer Science, University of Bir mingham, UK. We would like to thank H. Chen for providing the RNCL source code and for his support. This work was partially fu nded by EU Intel-liCIS (COST Action IC0806) and by CERCIA.

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