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SOM-Based Selection of Monitored Consumers for Demand Prediction

  • Conference paper
Intelligent Data Engineering and Automated Learning - IDEAL 2009 (IDEAL 2009)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5788))

  • 1898 Accesses

Abstract

Modern utility companies manage extensive distribution systems to provide multiple consumers with water, heat and electrical power. At the same time significant savings can be received from a combination of monitoring systems and modelling applications used to optimize the distribution systems. Thus, in case of pipeline systems, the problem of identifying key hydraulic control and monitoring points has been extensively studied.

Our recent research shows that by investigating the data acquired from heat meters in a district heating company, the accuracy of demand prediction can be significantly improved. The proposed methods rely on the availability of a system monitoring selected heat meters in an on-line manner. Thus, the purpose of this study is to develop and evaluate different methods of selecting heat meters to be monitored in order to provide data for prediction models. Self-organising maps have been applied to identify groups of consumers. The optimal number of monitored consumers in every group and the strategies of selecting the consumers to be monitored are searched for.

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Author information

Authors and Affiliations

  1. Faculty of Mathematics and Information Science, Warsaw University of Technology, 00-661, Warszawa, Pl. Politechniki 1, Poland

    Maciej Grzenda

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  1. Maciej Grzenda
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Editors and Affiliations

  1. Escuela Politécnica Superior, Universidad de Burgos, Calle Francisco de Vitoria, S/N, Edifico C, 09006, Burgos, Spain

    Emilio Corchado

  2. School of Electrical and Electronic Engineering, University of Manchester, Sackville Street Building, Sackville Street, M60 1QD, Manchester, UK

    Hujun Yin

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Grzenda, M. (2009). SOM-Based Selection of Monitored Consumers for Demand Prediction. In: Corchado, E., Yin, H. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2009. IDEAL 2009. Lecture Notes in Computer Science, vol 5788. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04394-9_99

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  • DOI: https://doi.org/10.1007/978-3-642-04394-9_99

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04393-2

  • Online ISBN: 978-3-642-04394-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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