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Generalised Regression Hypothesis Induction for Energy Consumption Forecasting

Author

Listed:
  • R. Rueda

    (Department of Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, Spain)

  • M. P. Cuéllar

    (Department of Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, Spain)

  • M. Molina-Solana

    (Data Science Institute, Imperial College, London SW7 2AZ, UK)

  • Y. Guo

    (Data Science Institute, Imperial College, London SW7 2AZ, UK)

  • M. C. Pegalajar

    (Department of Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, Spain)

Abstract

This work addresses the problem of energy consumption time series forecasting. In our approach, a set of time series containing energy consumption data is used to train a single, parameterised prediction model that can be used to predict future values for all the input time series. As a result, the proposed method is able to learn the common behaviour of all time series in the set (i.e., a fingerprint) and use this knowledge to perform the prediction task, and to explain this common behaviour as an algebraic formula. To that end, we use symbolic regression methods trained with both single- and multi-objective algorithms. Experimental results validate this approach to learn and model shared properties of different time series, which can then be used to obtain a generalised regression model encapsulating the global behaviour of different energy consumption time series.

Suggested Citation

  • R. Rueda & M. P. Cuéllar & M. Molina-Solana & Y. Guo & M. C. Pegalajar, 2019. "Generalised Regression Hypothesis Induction for Energy Consumption Forecasting," Energies, MDPI, vol. 12(6), pages 1-22, March.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:6:p:1069-:d:215506
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    References listed on IDEAS

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