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A novel genetic LSTM model for wind power forecast

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  • Shahid, Farah
  • Zameer, Aneela
  • Muneeb, Muhammad

Abstract

Variations of produced power in windmills may influence the appropriate integration in power-driven grids which may disrupt the balance between electricity demand and its production. Consequently, accurate prediction is extremely preferred for planning reliable and effective execution of power systems and to guarantee the continuous supply. For this purpose, a novel genetic long short term memory (GLSTM) framework comprising of long short term memory and genetic algorithm (GA) is proposed to predict short-term wind power. In the proposed GLSTM model, the strength of LSTM is employed due to its capability of automatically learning features from sequential data, while the global optimization strategy of GA is exploited to optimize window size and number of neurons in LSTM layers. Prediction from GLSTM has been compared with actual power, predictions of support vector regressor, and with reported techniques in terms of standard performance indices. It can be evaluated from the comparison that GLSTM and its variants provide accurate, reliable, and robust predictions of wind power of seven wind farms in Europe. In terms of percentage improvement, GLSTM, on average, improves wind power predictions from 6% to 30% as opposed to existing techniques. Wilcoxon signed-rank test demonstrates that GLSTM is significantly different from standard LSTM.

Suggested Citation

  • Shahid, Farah & Zameer, Aneela & Muneeb, Muhammad, 2021. "A novel genetic LSTM model for wind power forecast," Energy, Elsevier, vol. 223(C).
    • Handle: RePEc:eee:energy:v:223:y:2021:i:c:s0360544221003182
    DOI: 10.1016/j.energy.2021.120069
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    References listed on IDEAS

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    1. Yu, Feng & Xu, Xiaozhong, 2014. "A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network," Applied Energy, Elsevier, vol. 134(C), pages 102-113.
    2. Marciukaitis, Mantas & Katinas, Vladislovas & Kavaliauskas, Andrius, 2008. "Wind power usage and prediction prospects in Lithuania," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(1), pages 265-277, January.
    3. Taylor, James W. & Buizza, Roberto, 2003. "Using weather ensemble predictions in electricity demand forecasting," International Journal of Forecasting, Elsevier, vol. 19(1), pages 57-70.
    4. Han, Shuang & Qiao, Yan-hui & Yan, Jie & Liu, Yong-qian & Li, Li & Wang, Zheng, 2019. "Mid-to-long term wind and photovoltaic power generation prediction based on copula function and long short term memory network," Applied Energy, Elsevier, vol. 239(C), pages 181-191.
    5. Lahouar, A. & Ben Hadj Slama, J., 2017. "Hour-ahead wind power forecast based on random forests," Renewable Energy, Elsevier, vol. 109(C), pages 529-541.
    6. Abbas Mardani & Ahmad Jusoh & Edmundas Kazimieras Zavadskas & Fausto Cavallaro & Zainab Khalifah, 2015. "Sustainable and Renewable Energy: An Overview of the Application of Multiple Criteria Decision Making Techniques and Approaches," Sustainability, MDPI, vol. 7(10), pages 1-38, October.
    7. P. Pinson, 2012. "Very-short-term probabilistic forecasting of wind power with generalized logit–normal distributions," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 61(4), pages 555-576, August.
    8. Shahid, Farah & Zameer, Aneela & Mehmood, Ammara & Raja, Muhammad Asif Zahoor, 2020. "A novel wavenets long short term memory paradigm for wind power prediction," Applied Energy, Elsevier, vol. 269(C).
    9. Mohandes, M.A. & Halawani, T.O. & Rehman, S. & Hussain, Ahmed A., 2004. "Support vector machines for wind speed prediction," Renewable Energy, Elsevier, vol. 29(6), pages 939-947.
    10. Aggarwal, S.K. & Saini, L.M., 2014. "Solar energy prediction using linear and non-linear regularization models: A study on AMS (American Meteorological Society) 2013–14 Solar Energy Prediction Contest," Energy, Elsevier, vol. 78(C), pages 247-256.
    11. Peng, Lu & Liu, Shan & Liu, Rui & Wang, Lin, 2018. "Effective long short-term memory with differential evolution algorithm for electricity price prediction," Energy, Elsevier, vol. 162(C), pages 1301-1314.
    12. Han, Li & Jing, Huitian & Zhang, Rongchang & Gao, Zhiyu, 2019. "Wind power forecast based on improved Long Short Term Memory network," Energy, Elsevier, vol. 189(C).
    13. Foley, Aoife M. & Leahy, Paul G. & Marvuglia, Antonino & McKeogh, Eamon J., 2012. "Current methods and advances in forecasting of wind power generation," Renewable Energy, Elsevier, vol. 37(1), pages 1-8.
    Full references (including those not matched with items on IDEAS)

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