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International Conference on Intelligent Computing

ICIC 2012: Intelligent Computing Technology pp 538–546Cite as

Very Short Term Load Forecasting for Macau Power System

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7389))

Abstract

This paper presents the implementation of very short time load forecasting (VSLF) for Macau power system with the forecasting period ranging from several minutes to 8 hours. The methodology adopted is the hybrid model with ANN-based and similar days methods included weather information variables, which are seldom considered as input variables of VSLF in other literatures. It is shown that weather information is one of influence factors of the VSLF for a small city like Macau and the MAPE of VSLF for 15-minutes to 3-hours ahead load is 0.96% and 0.85% for Jan 2011 and Jul 2011 respectively. In this work, the author also utilizes the result of VSLF to adjust a day ahead short term load forecasting (STLF) result, by this approach, it is demonstrated that MAPE of STLF can be reduced by 20% for the data of Jul 2011.

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Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Macau, Macau, China

    Chong Yin Fok & Mang I Vai

Authors
  1. Chong Yin Fok
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  2. Mang I Vai
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Editor information

Editors and Affiliations

  1. School of Electronics and Information Engineering, Machine Learning and Systems Biology Laboratory, Tongji University, 4800 Caoan Road, 201804, Shanghai, China

    De-Shuang Huang

  2. School of Electronics and Information Engineering, Tongji University, 4800 Caoan Road, 201804, Shanghai, China

    Changjun Jiang

  3. Electrical and Electronics Department, Polytechnic of Bari, Via Orabona, 4, 70125, Bari, Italy

    Vitoantonio Bevilacqua

  4. Faculty of Engineering, District University Francisco José de Caldas, Cra. 7a No. 40-53, Fifth Floor, Bogotá, Colombia

    Juan Carlos Figueroa

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© 2012 Springer-Verlag Berlin Heidelberg

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Fok, C.Y., Vai, M.I. (2012). Very Short Term Load Forecasting for Macau Power System. In: Huang, DS., Jiang, C., Bevilacqua, V., Figueroa, J.C. (eds) Intelligent Computing Technology. ICIC 2012. Lecture Notes in Computer Science, vol 7389. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31588-6_69

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  • DOI: https://doi.org/10.1007/978-3-642-31588-6_69

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31587-9

  • Online ISBN: 978-3-642-31588-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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