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Improved Short-Term Load Forecasting Based on Two-Stage Predictions with Artificial Neural Networks in a Microgrid Environment

Author

Listed:
  • Luis Hernández

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

  • Carlos Baladrón

    (Universidad de Valladolid, E.T.S.I. Telecomunicación, Campus Miguel Delibes, Paseo de Belén 15, 47011 Valladolid, Spain)

  • Javier M. Aguiar

    (Universidad de Valladolid, E.T.S.I. Telecomunicación, Campus Miguel Delibes, Paseo de Belén 15, 47011 Valladolid, Spain)

  • Lorena Calavia

    (Universidad de Valladolid, E.T.S.I. Telecomunicación, Campus Miguel Delibes, Paseo de Belén 15, 47011 Valladolid, Spain)

  • Belén Carro

    (Universidad de Valladolid, E.T.S.I. Telecomunicación, Campus Miguel Delibes, Paseo de Belén 15, 47011 Valladolid, Spain)

  • Antonio Sánchez-Esguevillas

    (Universidad de Valladolid, E.T.S.I. Telecomunicación, Campus Miguel Delibes, Paseo de Belén 15, 47011 Valladolid, Spain)

  • Javier Sanjuán

    (Universidad de Zaragoza, Escuela de Ingeniería y Arquitectura, 50018 Zaragoza, Spain)

  • Álvaro González

    (Universidad de Zaragoza, Ingeniería Informática, 50018 Zaragoza, Spain)

  • Jaime Lloret

    (Departamento de Comunicaciones, Universidad Politécnica de Valencia, Camino Vera s/n, 46022 Valencia, Spain)

Abstract

Short-Term Load Forecasting plays a significant role in energy generation planning, and is specially gaining momentum in the emerging Smart Grids environment, which usually presents highly disaggregated scenarios where detailed real-time information is available thanks to Communications and Information Technologies, as it happens for example in the case of microgrids . This paper presents a two stage prediction model based on an Artificial Neural Network in order to allow Short-Term Load Forecasting of the following day in microgrid environment, which first estimates peak and valley values of the demand curve of the day to be forecasted. Those, together with other variables, will make the second stage, forecast of the entire demand curve, more precise than a direct, single-stage forecast. The whole architecture of the model will be presented and the results compared with recent work on the same set of data, and on the same location, obtaining a Mean Absolute Percentage Error of 1.62% against the original 2.47% of the single stage model.

Suggested Citation

  • Luis Hernández & Carlos Baladrón & Javier M. Aguiar & Lorena Calavia & Belén Carro & Antonio Sánchez-Esguevillas & Javier Sanjuán & Álvaro González & Jaime Lloret, 2013. "Improved Short-Term Load Forecasting Based on Two-Stage Predictions with Artificial Neural Networks in a Microgrid Environment," Energies, MDPI, vol. 6(9), pages 1-19, August.
    • Handle: RePEc:gam:jeners:v:6:y:2013:i:9:p:4489-4507:d:28406
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    References listed on IDEAS

    as
    1. Rob J Hyndman & Shu Fan, 2008. "Density forecasting for long-term peak electricity demand," Monash Econometrics and Business Statistics Working Papers 6/08, Monash University, Department of Econometrics and Business Statistics.
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    Citations

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    Cited by:

    1. 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.
    2. Ying-Yi Hong & Ti-Hsuan Yu & Ching-Yun Liu, 2013. "Hour-Ahead Wind Speed and Power Forecasting Using Empirical Mode Decomposition," Energies, MDPI, vol. 6(12), pages 1-16, November.
    3. Christopher Bennett & Rodney A. Stewart & Junwei Lu, 2014. "Autoregressive with Exogenous Variables and Neural Network Short-Term Load Forecast Models for Residential Low Voltage Distribution Networks," Energies, MDPI, vol. 7(5), pages 1-23, April.
    4. Seung Wan Kim & Jip Kim & Young Gyu Jin & Yong Tae Yoon, 2016. "Optimal Bidding Strategy for Renewable Microgrid with Active Network Management," Energies, MDPI, vol. 9(1), pages 1-15, January.
    5. Balasubramanian, C. & Lal Raja Singh, R., 2024. "IOT based energy management in smart grid under price based demand response based on hybrid FHO-RERNN approach," Applied Energy, Elsevier, vol. 361(C).
    6. Saxena, Harshit & Aponte, Omar & McConky, Katie T., 2019. "A hybrid machine learning model for forecasting a billing period’s peak electric load days," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1288-1303.
    7. Fatma Yaprakdal & M. Berkay Yılmaz & Mustafa Baysal & Amjad Anvari-Moghaddam, 2020. "A Deep Neural Network-Assisted Approach to Enhance Short-Term Optimal Operational Scheduling of a Microgrid," Sustainability, MDPI, vol. 12(4), pages 1-27, February.
    8. Dongkyu Lee & Jinhwa Jeong & Sung Hoon Yoon & Young Tae Chae, 2019. "Improvement of Short-Term BIPV Power Predictions Using Feature Engineering and a Recurrent Neural Network," Energies, MDPI, vol. 12(17), pages 1-17, August.
    9. Laib, Oussama & Khadir, Mohamed Tarek & Mihaylova, Lyudmila, 2019. "Toward efficient energy systems based on natural gas consumption prediction with LSTM Recurrent Neural Networks," Energy, Elsevier, vol. 177(C), pages 530-542.
    10. 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.
    11. Jaime Buitrago & Shihab Asfour, 2017. "Short-Term Forecasting of Electric Loads Using Nonlinear Autoregressive Artificial Neural Networks with Exogenous Vector Inputs," Energies, MDPI, vol. 10(1), pages 1-24, January.
    12. Ping-Huan Kuo & Chiou-Jye Huang, 2018. "A Green Energy Application in Energy Management Systems by an Artificial Intelligence-Based Solar Radiation Forecasting Model," Energies, MDPI, vol. 11(4), pages 1-15, April.

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