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Neural Networks Applied to Short Term Load Forecasting: A Case Study

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Smart Energy Control Systems for Sustainable Buildings

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 67))

Abstract

A good management of renewable energy systems and energy storage requires Short Term Load Forecasting (STLF). In particular, Artificial Neural Networks (ANN) have proved their ability to cope with data driven nonlinear models. In this paper ANN models are used with input variables such as apartment area, numbers of occupants, electrical appliance consumption and time, in order to achieve a robust model to be used in forecasting energy consumption of general homes. A feed-forward ANN trained with the Levenberg-Marquardt algorithm is tested and their results show a quite accurate model foreseeing that ANNs are a promising tool for STLF.

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Acknowledgements

This work was partially supported by FCT, through IDMEC, under LAETA PestOE/EME/LA0022 and by the Sustainable Urban Energy System (SUES) project under the MIT Portugal Program (MPP).

Author information

Authors and Affiliations

  1. Departamento de Engenharia Mecânica, IDMEC/ISEL—Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa, Rua Conselheiro Emídio Navarro, 1959-007, Lisbon, Portugal

    Filipe Rodrigues & J. M. F. Calado

  2. MIT Portugal, Campus IST-Tagus Park, Av. Professor Cavaco Silva, 2744-016, Porto Salvo, Portugal

    Filipe Rodrigues

  3. IDMEC/LAETA, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001, Lisbon, Portugal

    Carlos Cardeira & J. M. F. Calado

Authors
  1. Filipe Rodrigues
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  2. Carlos Cardeira
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  3. J. M. F. Calado
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Corresponding author

Correspondence to Filipe Rodrigues .

Editor information

Editors and Affiliations

  1. Cardiff School of Art & Design, The Ecological Built Environment group (EBERE), Cardiff Metropolitan University, Cardiff, United Kingdom

    John Littlewood

  2. Bartlett School Env, Energy & Resou, University College London Bartlett School Env, Energy & Resou, London, United Kingdom

    Catalina Spataru

  3. KES International, Shoreham-by-Sea, United Kingdom

    Robert J. Howlett

  4. School of Electrical and Information Engineering, University of South Australia, Adelaide, South Australia, Australia

    Lakhmi C. Jain

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Rodrigues, F., Cardeira, C., Calado, J.M.F. (2017). Neural Networks Applied to Short Term Load Forecasting: A Case Study. In: Littlewood, J., Spataru, C., Howlett, R., Jain, L. (eds) Smart Energy Control Systems for Sustainable Buildings. Smart Innovation, Systems and Technologies, vol 67. Springer, Cham. https://doi.org/10.1007/978-3-319-52076-6_8

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  • DOI: https://doi.org/10.1007/978-3-319-52076-6_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-52074-2

  • Online ISBN: 978-3-319-52076-6

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