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Artificial neural networks for short-term load forecasting in microgrids environment

Author

Listed:
  • Hernández, Luis
  • Baladrón, Carlos
  • Aguiar, Javier M.
  • Carro, Belén
  • Sánchez-Esguevillas, Antonio
  • Lloret, Jaime

Abstract

The adaptation of energy production to demand has been traditionally very important for utilities in order to optimize resource consumption. This is especially true also in microgrids where many intelligent elements have to adapt their behaviour depending on the future generation and consumption conditions. However, traditional forecasting has been performed only for extremely large areas, such as nations and regions. This work aims at presenting a solution for short-term load forecasting (STLF) in microgrids, based on a three-stage architecture which starts with pattern recognition by a self-organizing map (SOM), a clustering of the previous partition via k-means algorithm, and finally demand forecasting for each cluster with a multilayer perceptron. Model validation was performed with data from a microgrid-sized environment provided by the Spanish company Iberdrola.

Suggested Citation

  • Hernández, Luis & Baladrón, Carlos & Aguiar, Javier M. & Carro, Belén & Sánchez-Esguevillas, Antonio & Lloret, Jaime, 2014. "Artificial neural networks for short-term load forecasting in microgrids environment," Energy, Elsevier, vol. 75(C), pages 252-264.
    • Handle: RePEc:eee:energy:v:75:y:2014:i:c:p:252-264
    DOI: 10.1016/j.energy.2014.07.065
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    References listed on IDEAS

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    7. Che, Jinxing & Wang, Jianzhou & Wang, Guangfu, 2012. "An adaptive fuzzy combination model based on self-organizing map and support vector regression for electric load forecasting," Energy, Elsevier, vol. 37(1), pages 657-664.
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