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Very short-term wind power density forecasting through artificial neural networks for microgrid control

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  • Rodríguez, Fermín
  • Florez-Tapia, Ane M.
  • Fontán, Luis
  • Galarza, Ainhoa

Abstract

The aim of this study was to develop an artificial intelligence-based tool that is able to predict wind power density. Wind power density is volatile in nature, and this creates certain challenges, such as grid controlling problems or obstacles to guaranteeing power generation capacity. In order to ensure the proper control of the traditional network, energy generation and demand must be balanced, yet the variability of wind power density poses difficulties for fulfilling this requirement. This study addresses the complex control in systems based on wind energies by proposing a tool that is able to predict future wind power density in the near future, specifically, the next 10 min, allowing microgrid's control to be optimized. The tool is validated by examining the root mean square error value of the prediction. The deviation between the actual and forecasted wind power density was less than 6% for 81% of the examined days in the validation step, from January 2017 to August 2017. In addition, the obtained average deviation for the same period was 3.75%. After analysing the results, it was determined that the forecaster is accurate enough to be installed in systems that have wind turbines in order to improve their control strategy.

Suggested Citation

  • Rodríguez, Fermín & Florez-Tapia, Ane M. & Fontán, Luis & Galarza, Ainhoa, 2020. "Very short-term wind power density forecasting through artificial neural networks for microgrid control," Renewable Energy, Elsevier, vol. 145(C), pages 1517-1527.
    • Handle: RePEc:eee:renene:v:145:y:2020:i:c:p:1517-1527
    DOI: 10.1016/j.renene.2019.07.067
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    Citations

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    Cited by:

    1. Meng, Anbo & Xie, Zhifeng & Luo, Jianqiang & Zeng, Ying & Xu, Xuancong & Li, Yidian & Wu, Zhenbo & Zhang, Zhan & Zhu, Jianbin & Xian, Zikang & Li, Chen & Yan, Baiping & Yin, Hao, 2023. "An adaptive variational mode decomposition for wind power prediction using convolutional block attention deep learning network," Energy, Elsevier, vol. 282(C).
    2. Arash Moradzadeh & Sahar Zakeri & Maryam Shoaran & Behnam Mohammadi-Ivatloo & Fazel Mohammadi, 2020. "Short-Term Load Forecasting of Microgrid via Hybrid Support Vector Regression and Long Short-Term Memory Algorithms," Sustainability, MDPI, vol. 12(17), pages 1-17, August.
    3. Szoplik, Jolanta & Muchel, Paulina, 2023. "Using an artificial neural network model for natural gas compositions forecasting," Energy, Elsevier, vol. 263(PD).
    4. Neshat, Mehdi & Nezhad, Meysam Majidi & Abbasnejad, Ehsan & Mirjalili, Seyedali & Groppi, Daniele & Heydari, Azim & Tjernberg, Lina Bertling & Astiaso Garcia, Davide & Alexander, Bradley & Shi, Qinfen, 2021. "Wind turbine power output prediction using a new hybrid neuro-evolutionary method," Energy, Elsevier, vol. 229(C).
    5. Han, Yixiao & Liao, Yanfen & Ma, Xiaoqian & Guo, Xing & Li, Changxin & Liu, Xinyu, 2023. "Analysis and prediction of the penetration of renewable energy in power systems using artificial neural network," Renewable Energy, Elsevier, vol. 215(C).
    6. Xing, Zhikai & He, Yigang, 2023. "Multi-modal multi-step wind power forecasting based on stacking deep learning model," Renewable Energy, Elsevier, vol. 215(C).
    7. Rodríguez, Fermín & Galarza, Ainhoa & Vasquez, Juan C. & Guerrero, Josep M., 2022. "Using deep learning and meteorological parameters to forecast the photovoltaic generators intra-hour output power interval for smart grid control," Energy, Elsevier, vol. 239(PB).
    8. Tawn, R. & Browell, J., 2022. "A review of very short-term wind and solar power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    9. da Silva, Ramon Gomes & Ribeiro, Matheus Henrique Dal Molin & Moreno, Sinvaldo Rodrigues & Mariani, Viviana Cocco & Coelho, Leandro dos Santos, 2021. "A novel decomposition-ensemble learning framework for multi-step ahead wind energy forecasting," Energy, Elsevier, vol. 216(C).

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