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Experimental Analysis of the Input Variables’ Relevance to Forecast Next Day’s Aggregated Electric Demand Using Neural Networks

Author

Listed:
  • Luis Hernández

    (CIEMAT (Research Centre for Energy, Environment and Technology), Autovía de Navarra A15, salida 56, Lubia 42290, Soria, Spain)

  • Carlos Baladrón

    (Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad de Valladolid, Campus Miguel Delibes, Paseo de Belén 15, Valladolid 47011, Spain)

  • Javier M. Aguiar

    (Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad de Valladolid, Campus Miguel Delibes, Paseo de Belén 15, Valladolid 47011, Spain)

  • Lorena Calavia

    (Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad de Valladolid, Campus Miguel Delibes, Paseo de Belén 15, Valladolid 47011, Spain)

  • Belén Carro

    (Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad de Valladolid, Campus Miguel Delibes, Paseo de Belén 15, Valladolid 47011, Spain)

  • Antonio Sánchez-Esguevillas

    (Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad de Valladolid, Campus Miguel Delibes, Paseo de Belén 15, Valladolid 47011, Spain)

  • Pablo García

    (Faculty of Sciences, University of Oviedo, c/Calvo Sotelo s/n, Oviedo 33007, Spain)

  • Jaime Lloret

    (Department of Communications, Polytechnic University of Valencia, Camino Vera s/n 46022, Valencia, Spain)

Abstract

Thanks to the built in intelligence (deployment of new intelligent devices and sensors in places where historically they were not present), the Smart Grid and Microgrid paradigms are able to take advantage from aggregated load forecasting, which opens the door for the implementation of new algorithms to seize this information for optimization and advanced planning. Therefore, accuracy in load forecasts will potentially have a big impact on key operation factors for the future Smart Grid/Microgrid-based energy network like user satisfaction and resource saving, and new methods to achieve an efficient prediction in future energy landscapes (very different from the centralized, big area networks studied so far). This paper proposes different improved models to forecast next day’s aggregated load using artificial neural networks, taking into account the variables that are most relevant for the aggregated. In particular, seven models based on the multilayer perceptron will be proposed, progressively adding input variables after analyzing the influence of climate factors on aggregated load. The results section presents the forecast from the proposed models, obtained from real data.

Suggested Citation

  • Luis Hernández & Carlos Baladrón & Javier M. Aguiar & Lorena Calavia & Belén Carro & Antonio Sánchez-Esguevillas & Pablo García & Jaime Lloret, 2013. "Experimental Analysis of the Input Variables’ Relevance to Forecast Next Day’s Aggregated Electric Demand Using Neural Networks," Energies, MDPI, vol. 6(6), pages 1-22, June.
    • Handle: RePEc:gam:jeners:v:6:y:2013:i:6:p:2927-2948:d:26455
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    References listed on IDEAS

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    1. Luis Hernandez & Carlos Baladrón & Javier M. Aguiar & Belén Carro & Antonio J. Sanchez-Esguevillas & Jaime Lloret, 2013. "Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks," Energies, MDPI, vol. 6(3), pages 1-24, March.
    2. Qian Zhang & Kin Keung Lai & Dongxiao Niu & Qiang Wang & Xuebin Zhang, 2012. "A Fuzzy Group Forecasting Model Based on Least Squares Support Vector Machine (LS-SVM) for Short-Term Wind Power," Energies, MDPI, vol. 5(9), pages 1-18, September.
    3. J W Taylor & S Majithia, 2000. "Using combined forecasts with changing weights for electricity demand profiling," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 51(1), pages 72-82, January.
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    Cited by:

    1. Carolina Deina & João Lucas Ferreira dos Santos & Lucas Henrique Biuk & Mauro Lizot & Attilio Converti & Hugo Valadares Siqueira & Flavio Trojan, 2023. "Forecasting Electricity Demand by Neural Networks and Definition of Inputs by Multi-Criteria Analysis," Energies, MDPI, vol. 16(4), pages 1-24, February.
    2. Magnus Dahl & Adam Brun & Oliver S. Kirsebom & Gorm B. Andresen, 2018. "Improving Short-Term Heat Load Forecasts with Calendar and Holiday Data," Energies, MDPI, vol. 11(7), pages 1-16, June.
    3. 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.
    4. Ding, Song & Hipel, Keith W. & Dang, Yao-guo, 2018. "Forecasting China's electricity consumption using a new grey prediction model," Energy, Elsevier, vol. 149(C), pages 314-328.
    5. Rodríguez, Fermín & Fleetwood, Alice & Galarza, Ainhoa & Fontán, Luis, 2018. "Predicting solar energy generation through artificial neural networks using weather forecasts for microgrid control," Renewable Energy, Elsevier, vol. 126(C), pages 855-864.
    6. Maria Grazia De Giorgi & Stefano Campilongo & Antonio Ficarella & Paolo Maria Congedo, 2014. "Comparison Between Wind Power Prediction Models Based on Wavelet Decomposition with Least-Squares Support Vector Machine (LS-SVM) and Artificial Neural Network (ANN)," Energies, MDPI, vol. 7(8), pages 1-22, August.
    7. Li, Lechen & Meinrenken, Christoph J. & Modi, Vijay & Culligan, Patricia J., 2021. "Short-term apartment-level load forecasting using a modified neural network with selected auto-regressive features," Applied Energy, Elsevier, vol. 287(C).

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