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Prediction of electricity generation from a combined cycle power plant based on a stacking ensemble and its hyperparameter optimization with a grid-search method

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  • Qu, Zhijian
  • Xu, Juan
  • Wang, Zixiao
  • Chi, Rui
  • Liu, Hanxin

Abstract

Electric power makes a significant contribution to society. Predicting power generation is becoming increasingly important for electric power planning and energy utilization. A reliable forecasting model is necessary for accurate planning of electricity generation. The main goal of this study is to develop effective and realistic solutions for the full-load power generation prediction of combined cycle power plants. According to 9568 items of data pertaining to a combined cycle power plant in six years of its full-load operation, a prediction method based on stacking ensemble hyperparameter optimization is established. The results demonstrate that this method provides high prediction accuracy for the power plant under multiple complex environmental variables. Besides, the predictions generated using this method are compared with those of traditional machine learning methods, random forest, and other ensemble methods, as well as those cited in the literature using the same dataset. The predictions show that the proposed method offers more accurate predictions of the power generation from a combined cycle plant, which opens up a new idea for power planning and energy utilization.

Suggested Citation

  • Qu, Zhijian & Xu, Juan & Wang, Zixiao & Chi, Rui & Liu, Hanxin, 2021. "Prediction of electricity generation from a combined cycle power plant based on a stacking ensemble and its hyperparameter optimization with a grid-search method," Energy, Elsevier, vol. 227(C).
    • Handle: RePEc:eee:energy:v:227:y:2021:i:c:s0360544221005582
    DOI: 10.1016/j.energy.2021.120309
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    5. Akeratana Noppakant & Boonyang Plangklang, 2022. "Improving Energy Management through Demand Response Programs for Low-Rise University Buildings," Sustainability, MDPI, vol. 14(21), pages 1-15, October.
    6. Tan, Yunhui & Wang, Quan & Zhang, Zhaoyang, 2023. "Near-real-time estimation of global horizontal irradiance from Himawari-8 satellite data," Renewable Energy, Elsevier, vol. 215(C).
    7. Li, Guanzheng & Li, Bin & Li, Chao & Wang, Shuai, 2023. "State-of-health rapid estimation for lithium-ion battery based on an interpretable stacking ensemble model with short-term voltage profiles," Energy, Elsevier, vol. 263(PE).
    8. Sadaf Mahmoudi & Hamed Moftakhari & David F. Muñoz & William Sweet & Hamid Moradkhani, 2024. "Establishing flood thresholds for sea level rise impact communication," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    9. Elizabeth Michael, Neethu & Hasan, Shazia & Al-Durra, Ahmed & Mishra, Manohar, 2022. "Short-term solar irradiance forecasting based on a novel Bayesian optimized deep Long Short-Term Memory neural network," Applied Energy, Elsevier, vol. 324(C).
    10. Konstantinos Blazakis & Yiannis Katsigiannis & Georgios Stavrakakis, 2022. "One-Day-Ahead Solar Irradiation and Windspeed Forecasting with Advanced Deep Learning Techniques," Energies, MDPI, vol. 15(12), pages 1-25, June.
    11. Qingsheng Zhao & Juwen Mu & Xiaoqing Han & Dingkang Liang & Xuping Wang, 2021. "Evaluation Model of Operation State Based on Deep Learning for Smart Meter," Energies, MDPI, vol. 14(15), pages 1-17, August.
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    14. Yang, Yang & Wang, Xiuqin, 2022. "A novel modified conformable fractional grey time-delay model for power generation prediction," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).

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