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Forecasting short-term electricity consumption using a semantics-based genetic programming framework: The South Italy case

Author

Listed:
  • Castelli, Mauro
  • Vanneschi, Leonardo
  • De Felice, Matteo

Abstract

Accurate and robust short-term load forecasting plays a significant role in electric power operations. This paper proposes a variant of genetic programming, improved by incorporating semantic awareness in algorithm, to address a short term load forecasting problem. The objective is to automatically generate models that could effectively and reliably predict energy consumption. The presented results, obtained considering a particularly interesting case of the South Italy area, show that the proposed approach outperforms state of the art methods. Hence, the proposed approach reveals appropriate for the problem of forecasting electricity consumption. This study, besides providing an important contribution to the energy load forecasting, confirms the suitability of genetic programming improved with semantic methods in addressing complex real-life applications.

Suggested Citation

  • Castelli, Mauro & Vanneschi, Leonardo & De Felice, Matteo, 2015. "Forecasting short-term electricity consumption using a semantics-based genetic programming framework: The South Italy case," Energy Economics, Elsevier, vol. 47(C), pages 37-41.
    • Handle: RePEc:eee:eneeco:v:47:y:2015:i:c:p:37-41
    DOI: 10.1016/j.eneco.2014.10.009
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    Citations

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    Cited by:

    1. Hu, Junjie & López Cabrera, Brenda & Melzer, Awdesch, 2021. "Advanced statistical learning on short term load process forecasting," IRTG 1792 Discussion Papers 2021-020, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    2. Mauro Castelli & Ivo Gonçalves & Leonardo Trujillo & Aleš Popovič, 2017. "An evolutionary system for ozone concentration forecasting," Information Systems Frontiers, Springer, vol. 19(5), pages 1123-1132, October.
    3. de Oliveira, Erick Meira & Cyrino Oliveira, Fernando Luiz, 2018. "Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods," Energy, Elsevier, vol. 144(C), pages 776-788.
    4. Federico Divina & Miguel García Torres & Francisco A. Goméz Vela & José Luis Vázquez Noguera, 2019. "A Comparative Study of Time Series Forecasting Methods for Short Term Electric Energy Consumption Prediction in Smart Buildings," Energies, MDPI, vol. 12(10), pages 1-23, May.
    5. Duangnate, Kannika & Mjelde, James W., 2017. "Comparison of data-rich and small-scale data time series models generating probabilistic forecasts: An application to U.S. natural gas gross withdrawals," Energy Economics, Elsevier, vol. 65(C), pages 411-423.
    6. Federico Divina & Aude Gilson & Francisco Goméz-Vela & Miguel García Torres & José F. Torres, 2018. "Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting," Energies, MDPI, vol. 11(4), pages 1-31, April.
    7. Paul Anton Verwiebe & Stephan Seim & Simon Burges & Lennart Schulz & Joachim Müller-Kirchenbauer, 2021. "Modeling Energy Demand—A Systematic Literature Review," Energies, MDPI, vol. 14(23), pages 1-58, November.
    8. Francisco Martínez-Álvarez & Alicia Troncoso & Gualberto Asencio-Cortés & José C. Riquelme, 2015. "A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting," Energies, MDPI, vol. 8(11), pages 1-32, November.
    9. Saâdaoui, Foued & Ben Jabeur, Sami, 2023. "Analyzing the influence of geopolitical risks on European power prices using a multiresolution causal neural network," Energy Economics, Elsevier, vol. 124(C).
    10. Meira, Erick & Lila, Maurício Franca & Cyrino Oliveira, Fernando Luiz, 2023. "A novel reconciliation approach for hierarchical electricity consumption forecasting based on resistant regression," Energy, Elsevier, vol. 269(C).
    11. Mauro Castelli & Ivo Gonçalves & Leonardo Trujillo & Aleš Popovič, 0. "An evolutionary system for ozone concentration forecasting," Information Systems Frontiers, Springer, vol. 0, pages 1-10.

    More about this item

    Keywords

    Forecasting; Electricity demand; Genetic programming; Semantics;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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