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Research Article

Optimized Forecasting Approach for Scheduling Wind Generation Plants and Maximizing Renewable Energy Utilization

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Received 03 Jan 2024, Accepted 25 Mar 2024, Published online: 23 Apr 2024

References

  • J. Smith, A. Johnson and M. Brown, “Advanced forecasting models for wind power generation: a comparative study,” IEEE Trans. Sustainable Energ., 2023.
  • L. Chen, Q. Zhang and Y. Wang, “Optimal scheduling of renewable energy generation plants using genetic algorithms,” Renewable Energ., 2023.
  • S. Lee, H. Kim and J. Park, “Machine learning-based short-term wind power forecasting: a review,” Energies, 2022.
  • C. Wang, X. Liu and Y. Li, “Demand forecasting for smart grids: a comprehensive review,” Electric Power Syst. Res., 2022.
  • W. Zhang, L. Wu and S. Chen, “Enhancing wind power forecasting using weather data fusion techniques,” J. Renew. Sustain. Energ., 2022.
  • R. Gupta, S. Patel and A. Kumar, “Integration of renewable energy sources into power grids: challenges and opportunities,” Renew. Sustain. Energ. Review., 2021.
  • P. Rodrigues, A. Silva and F. Soares, “Optimization of wind power plant scheduling considering uncertainties,” Electric Power Syst. Res., 2021.
  • M. Li, Y. Zhang and H. Liu, “Deep learning techniques for short-term solar power forecasting: a comparative analysis,” IEEE J. Photovoltaics, 2021.
  • L. Huang, Z. Wang and Y. Jiang, “Multi-objective optimization of wind power generation scheduling considering economic and environmental factors,” Energy Conversion Manage., 2021.
  • K. Park, J. Lee and S. Kim, “Smart grid integration of wind power generation: challenges and solutions,” Energies, 2020.
  • T. Chang, S. Wang and C. Chen, “Long-term wind power generation forecasting using machine learning techniques,” IEEE Trans. Power Syst., 2020.
  • Y. Liu, J. Zhang and X. Li, “Scheduling optimization of wind power generation in microgrids: a review,” Appl. Energ., 2020.
  • E. Kim, Y. Cho and K. Kim, “Ensemble forecasting approaches for wind power generation prediction,” Sustainable Energ. Grid. Network., 2020.
  • A. Gupta, R. Singh and P. Sharma, “Demand response strategies for efficient scheduling of renewable energy generation,” Intern. J. Electrical Power Energ. Sys., 2020.
  • M. Patel, S. Kumar and A. Sharma, “Integration of smart grid operators for effective renewable energy management,” Renewable Energy Focus, 2020.
  • A. Kikuchi, M. Ito and Y. Hayashi, “Scheduling method of wind power generation for electricity market using state-of-charge transition and forecast error journal of international council on electrical engineering,” J. Intern. Council Electric. Engng., vol. 9, no. 1, pp. 123–132, 2020. DOI: 10.1080/22348972.2020.1712018.
  • S. Preethi, et al., “Predicting the wind turbine power generation based on weather conditions,” International Conference on Electronics, Communication and Aerospace Technology (ICECA), 2021.
  • S. C. Sahoo, A. K. Barik and D. C. Das, “Synchronized voltage-frequency regulation in sustainable microgrid using novel Green Leaf-hopper Flame optimization,” Sustain. Energ. Technol. Assess., vol. 52, pp. 102349, 2022. DOI: 10.1016/j.seta.2022.102349.
  • Y.-K. Wu, et al., “Using extreme wind-speed probabilistic forecasts to optimize unit scheduling decision,” IEEE Trans. Sustain. Energ., vol. 13, no. 2, pp. 818–829, 2022. DOI: 10.1109/TSTE.2021.3132342.
  • Q. Chen and K. A. Folly, “Short-term wind power forecasting using mixed input feature-based cascade-connected artificial neural networks,” Front. Energy Res., vol. 9, pp. 634639, 2021. DOI: 10.3389/fenrg.2021.634639.
  • M. Khan, et al., “Forecasting renewable energy for environmental resilience through computational intelligence,” PLoS One, vol. 16, no. 8, pp. e0256381, 2021. DOI: 10.1371/journal.pone.0256381.
  • Z. Liu, R. Hara and H. Kita, “Hybrid forecasting system based on data area division and deep learning neural network for short-term wind speed forecasting,” Energy Convers. Manage., vol. 238, pp. 114136, 2021. DOI: 10.1016/j.enconman.2021.114136.
  • S. R. Moreno, V. C. Mariani and L. d S. Coelho, “Hybrid multi-stage decomposition with parametric model applied to wind speed forecasting in Brazilian Northeast,” Renew. Energ., vol. 164, pp. 1508–1526, 2021. DOI: 10.1016/j.renene.2020.10.126.
  • L.-L. Li, Z.-F. Liu, M.-L. Tseng, K. Jantarakolica and M. K. Lim, “Using enhanced crow search algorithm optimization-extreme learning machine model to forecast short-term wind power,” Expert Syst. Appl., vol. 184, pp. 115579, 2021. DOI: 10.1016/j.eswa.2021.115579.
  • J. Duan, P. Wang, W. Ma, S. Fang and Z. Hou, “A novel hybrid model based on nonlinear weighted combination for short-term wind power forecasting,” Int. J. Elect. Power Energ. Syst., vol. 134, pp. 107452, 2022. DOI: 10.1016/j.ijepes.2021.107452.
  • R. G. da Silva, M. H. D. M. Ribeiro, S. R. Moreno, V. C. Mariani and L. d S. Coelho, “A novel decomposition-ensemble learning framework for multi-step ahead wind energy forecasting,” Energy, vol. 216, pp. 119174, 2021. DOI: 10.1016/j.energy.2020.119174.
  • Z. Tian, “Modes decomposition forecasting approach for ultra-short-term wind speed,” Appl. Soft Comput., vol. 105, pp. 107303, 2021. DOI: 10.1016/j.asoc.2021.107303.
  • S. R. Moreno, R. Gomes da Silva, V. Cocco Mariani and L. dos Santos Coelho, “Multi-step wind speed forecasting based on hybrid multi-stage decomposition model and long short-term memory neural network,” Energ. Convers. Manage., vol. 213, pp. 112869, 2020. DOI: 10.1016/j.enconman.2020.112869.
  • J. Wang and Z. Yang, “Ultra-short-term wind speed forecasting using an optimized artificial intelligence algorithm,” Renewable Energ., vol. 171, pp. 1418–1435, 2021. DOI: 10.1016/j.renene.2021.03.020.
  • R. K. B. Navas and S. Prakash, “A novel ultra-short term wind power forecasting intelligence system based on hybrid neural network,” Materials Today: Proc., vol. 47, pp. 1145–1148, 2021. DOI: 10.1016/j.matpr.2021.07.336.
  • R. Kalpana, et al., “Internet of things (IOT) based machine learning techniques for wind energy harvesting,” Electric Power Components Syst., pp. 1–17, 2023. pp. DOI: 10.1080/15325008.2023.2293952.
  • A. Rajaram and K. Sathiyaraj, “An improved optimization technique for energy harvesting system with grid connected power for green house management,” J. Electr. Eng. Technol., vol. 17, no. 5, pp. 2937–2949, 2022. DOI: 10.1007/s42835-022-01033-2.
  • P. Ashok Babu, et al., “Power control and optimization for power loss reduction using deep learning in microgrid systems,” Electric Power Components Syst., vol. 52, no. 2, pp. 219–232, 2023. DOI: 10.1080/15325008.2023.2217175.
  • P. K. Pathak and A. Kumar Yadav, “Fuzzy assisted optimal tilt control approach for LFC of renewable dominated micro-grid: a step towards grid decarbonisation,” Sustain. Energ. Technol. Assess., vol. 60, pp. 103551, 2023. DOI: 10.1016/j.seta.2023.103551.
  • R. Gupta, et al., “Composition of feature selection techniques for improving the global horizontal irradiance estimation,” Thermal Sci. Engng. Progress, vol. 48, pp. 102394, 2024. DOI: 10.1016/j.tsep.2024.102394.
  • R. Gupta, A. K. Yadav, S. K. Jha and P. K. Pathak, “A robust regressor model for estimating solar radiation using an ensemble stacking approach based on machine learning,” Intern. J. Green Energ., pp. 1–21, 2023. DOI: 10.1080/15435075.2023.2276152.
  • A. Ahmed and M. Khalid, “A review on the selected applications of forecasting models in renewable power systems,” Renew. Sustain. Energ. Review., vol. 100, pp. 9–21, 2019. DOI: 10.1016/j.rser.2018.09.046.
  • A. T. Dosdogru and A. Ipek, “Hybrid boosting algorithms and artificial neural network for wind speed prediction,” Int. J. Hydrogen Energ., vol. 47, no. 3, pp. 1449–1460, 2022.
  • Q. Li, Y. Liu, H. Ma and B. Zhang, “A novel wind speed forecasting model based on long short-term memory neural network,” Energies, 2019.
  • Y. Zhou, Y. Wang and Y. Tang, “Wind speed forecasting based on extreme learning machine optimized by differential evolution algorithm,” Energies, 2019.
  • M. S. El-Nasr, M. E. Seliaman and H. M. Hossam-Eldin, “Optimal design of wind farm layout and power cable routing for maximum power production,” Energies, 2020.
  • C. Dey and T. K. Roy, “An integrated wind power forecasting model using wavelet transform and extreme learning machine,” J. Renew. Sustain. Energ., 2020.
  • X. Li, H. Chen and X. Ding, “Short-term wind speed forecasting based on wavelet decomposition and LSTM neural network,” Sustainability, vol. 134, pp. 107365, 2021.

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