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Assessment of an Adaptive Load Forecasting Methodology in a Smart Grid Demonstration Project

Author

Listed:
  • Ricardo Vazquez

    (Department of Electrical Engineering, University Carlos III of Madrid, Avda de la Universidad 30, 28911 Madrid, Spain)

  • Hortensia Amaris

    (Department of Electrical Engineering, University Carlos III of Madrid, Avda de la Universidad 30, 28911 Madrid, Spain)

  • Monica Alonso

    (Department of Electrical Engineering, University Carlos III of Madrid, Avda de la Universidad 30, 28911 Madrid, Spain)

  • Gregorio Lopez

    (Department of Telematic Engineering, University Carlos III of Madrid, Avda de la Universidad 30, 28911 Madrid, Spain)

  • Jose Ignacio Moreno

    (Department of Telematic Engineering, University Carlos III of Madrid, Avda de la Universidad 30, 28911 Madrid, Spain)

  • Daniel Olmeda

    (Department of Electrical Engineering, University Carlos III of Madrid, Avda de la Universidad 30, 28911 Madrid, Spain)

  • Javier Coca

    (Unión Fenosa Distribución, Avda. San Luis 77, 28033 Madrid, Spain)

Abstract

This paper presents the implementation of an adaptive load forecasting methodology in two different power networks from a smart grid demonstration project deployed in the region of Madrid, Spain. The paper contains an exhaustive comparative study of different short-term load forecast methodologies, addressing the methods and variables that are more relevant to be applied for the smart grid deployment. The evaluation followed in this paper suggests that the performance of the different methods depends on the conditions of the site in which the smart grid is implemented. It is shown that some non-linear methods, such as support vector machine with a radial basis function kernel and extremely randomized forest offer good performance using only 24 lagged load hourly values, which could be useful when the amount of data available is limited due to communication problems in the smart grid monitoring system. However, it has to be highlighted that, in general, the behavior of different short-term load forecast methodologies is not stable when they are applied to different power networks and that when there is a considerable variability throughout the whole testing period, some methods offer good performance in some situations, but they fail in others. In this paper, an adaptive load forecasting methodology is proposed to address this issue improving the forecasting performance through iterative optimization: in each specific situation, the best short-term load forecast methodology is chosen, resulting in minimum prediction errors.

Suggested Citation

  • Ricardo Vazquez & Hortensia Amaris & Monica Alonso & Gregorio Lopez & Jose Ignacio Moreno & Daniel Olmeda & Javier Coca, 2017. "Assessment of an Adaptive Load Forecasting Methodology in a Smart Grid Demonstration Project," Energies, MDPI, vol. 10(2), pages 1-23, February.
  • Handle: RePEc:gam:jeners:v:10:y:2017:i:2:p:190-:d:89745
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    References listed on IDEAS

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    1. Cheng-Wen Lee & Bing-Yi Lin, 2016. "Application of Hybrid Quantum Tabu Search with Support Vector Regression (SVR) for Load Forecasting," Energies, MDPI, vol. 9(11), pages 1-16, October.
    2. 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.
    3. Yan Hong Chen & Wei-Chiang Hong & Wen Shen & Ning Ning Huang, 2016. "Electric Load Forecasting Based on a Least Squares Support Vector Machine with Fuzzy Time Series and Global Harmony Search Algorithm," Energies, MDPI, vol. 9(2), pages 1-13, January.
    4. Usman, Ahmad & Shami, Sajjad Haider, 2013. "Evolution of Communication Technologies for Smart Grid applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 19(C), pages 191-199.
    5. Luis Hernández & Carlos Baladrón & Javier M. Aguiar & Lorena Calavia & Belén Carro & Antonio Sánchez-Esguevillas & Francisco Pérez & Ángel Fernández & Jaime Lloret, 2014. "Artificial Neural Network for Short-Term Load Forecasting in Distribution Systems," Energies, MDPI, vol. 7(3), pages 1-23, March.
    6. Nantian Huang & Guobo Lu & Dianguo Xu, 2016. "A Permutation Importance-Based Feature Selection Method for Short-Term Electricity Load Forecasting Using Random Forest," Energies, MDPI, vol. 9(10), pages 1-24, September.
    7. Gregorio López & Pedro Moura & José Ignacio Moreno & José Manuel Camacho, 2014. "Multi-Faceted Assessment of a Wireless Communications Infrastructure for the Green Neighborhoods of the Smart Grid," Energies, MDPI, vol. 7(5), pages 1-31, May.
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    Cited by:

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