Open Access
Issue
E3S Web Conf.
Volume 491, 2024
International Conference on Environmental Development Using Computer Science (ICECS’24)
Article Number 02043
Number of page(s) 11
Section Smart Systems for Environmental Development
DOI https://doi.org/10.1051/e3sconf/202449102043
Published online 21 February 2024
  1. Adedeji, P. A., S. Akinlabi, and N. Madushele. (2020). “Wind Turbine Power Output Very Short-Term Forecast: A Comparative Study of Data Clustering Techniques in a PSO-ANFIS Model.” Of Cleaner Production. http://www.sciencedirect.com/science/article/pii/S0196890419308052. [Google Scholar]
  2. Demolli, H., A. S. Dokuz, A. Ecemis, and M. Gokcek. (2019). “Wind Power Forecasting Based on Daily Wind Speed Data Using Machine Learning Algorithms.” Energy Conversion and. https://www.sciencedirect.com/science/article/pii/S0196890419308052. [Google Scholar]
  3. Golden, Chase E., Michael J. Rothrock Jr, and Abhinav Mishra. (2019). “Comparison between Random Forest and Gradient Boosting Machine Methods for Predicting Listeria Spp. Prevalence in the Environment of Pastured Poultry Farms.” Food Research International 122 (August): 47–55. https://doi.org/10.1016/j.foodres.2019.03.062. [CrossRef] [Google Scholar]
  4. Hofmann, Fabian, Johannes Hampp, Fabian Neumann, Tom Brown, and Jonas Hörsch. (2021). “Atlite: A Lightweight Python Package for Calculating Renewable Power Potentials and Time Series.” Journal of Open Source Software 6 (62): 3294. https://doi.org/10.21105/joss.03294. [CrossRef] [Google Scholar]
  5. Hong, Ying-Yi, and Thursy Rienda Aulia Satriani. (2020). “Day-Ahead Spatiotemporal Wind Speed Forecasting Using Robust Design-Based Deep Learning Neural Network.” Energy 209 (October): 118441. https://doi.org/10.1016/j.energy.2020.118441. [Google Scholar]
  6. Irfan, A. S. M., Nur Hossain Bhuiyan, Mehedi Hasan, and Mohammad Monirujjaman Khan. (2021). “Performance Analysis of Machine Learning Techniques for Wind Speed Prediction.” In 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), 1–6. http://ieeexplore.ieee.org. http://doi.org/10.1109/ICCCNT51525.2021.9579564. [Google Scholar]
  7. Jia, Liangyue, Jia Hao, John Hall, Hamid Khakpour Nejadkhaki, Guoxin Wang, Yan Yan, and Mengyuan Sun. (2021). “A Reinforcement Learning Based Blade Twist Angle Distribution Searching Method for Optimizing Wind Turbine Energy Power.” Energy 215 (January): 119148. https://doi.org/10.1016/j.energy.2020.119148. [CrossRef] [Google Scholar]
  8. Jørgensen, Kathrine Lau, and Hamid Reza Shaker. (2020). “Wind Power Forecasting Using Machine Learning: State of the Art, Trends and Challenges.” In 2020 IEEE 8th International Conference on Smart Energy Grid Engineering (SEGE), 44–50. ieeexplore.ieee.org. https://doi.org/10.1109/SEGE49949.2020.9181870. [Google Scholar]
  9. Li, Jinghua, Jiasheng Zhou, and Bo Chen. (2020). “Review of Wind Power Scenario Generation Methods for Optimal Operation of Renewable Energy Systems.” Applied Energy 280 (December): 115992. https://doi.org/10.1016/j.apenergy.2020.115992. [Google Scholar]
  10. Liu, Hui, Chao Chen, Xinwei Lv, Xing Wu, and Min Liu. (2019). “Deterministic Wind Energy Forecasting: A Review of Intelligent Predictors and Auxiliary Methods.” Energy Conversion & Management 195 (September): 328–45. https://doi.org/10.1016/j.enconman.2019.05.020. [CrossRef] [Google Scholar]
  11. Liu, Ming-De, Lin Ding, and Yu-Long Bai. (2021). “Application of Hybrid Model Based on Empirical Mode Decomposition, Novel Recurrent Neural Networks and the ARIMA to Wind Speed Prediction.” Energy Conversion & Management 233 (April): 113917. https://doi.org/10.1016/j.enconman.2021.113917. [CrossRef] [Google Scholar]
  12. Liu, Yongqian, Tao Tao, Xingyu Zhao, Ce Zhang, and Yuanchi Ma. (2021). “Support Vector Regression-Based Fatigue Damage Assessment Method for Wind Turbine Nacelle Chassis.” Structures 33 (October): 759–68. https://doi.org/10.1016/j.istruc.2021.04.093. [CrossRef] [Google Scholar]
  13. Malakouti, Seyed Matin. (2023). “Use Machine Learning Algorithms to Predict Turbine Power Generation to Replace Renewable Energy with Fossil Fuels.” Energy Exploration & Exploitation 41 (2): 836–57. https://doi.org/10.1177/01445987221138135. [CrossRef] [Google Scholar]
  14. Mujeeb, Sana, Turki Ali Alghamdi, Sameeh Ullah, Aisha Fatima, Nadeem Javaid, and Tanzila Saba.( 2019). “Exploiting Deep Learning for Wind Power Forecasting Based on Big Data Analytics.” NATO Advanced Science Institutes Series E: Applied Sciences 9 (20): 4417. https://doi.org/10.3390/app9204417. [Google Scholar]
  15. Peng, Xiaosheng, Hongyu Wang, Jianxun Lang, Wenze Li, Qiyou Xu, Zuowei Zhang, Tao Cai, Shanxu Duan, Fangjie Liu, and Chaoshun Li. (2021). “EALSTMQR: Interval Wind-Power Prediction Model Based on Numerical Weather Prediction and Deep Learning.” Energy 220 (April): 119692. https://doi.org/10.1016/j.energy.2020.119692. [CrossRef] [Google Scholar]
  16. Shabbir, Noman, Roya AhmadiAhangar, Lauri Kütt, N. Iqbal, and Argo Rosin. (2019). “Forecasting Short Term Wind Energy Generation Using Machine Learning.” In 2019 IEEE 60th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON), 1–4. ieeexplore.ieee.org. https://doi.org/10.1109/RTUCON48111.2019.8982365. [Google Scholar]
  17. Vijay, Satpute Anand, and E. Vijay Kumar. (2020). “Current Scenario of Wind Power in India, Government Policies, Initiatives, Status and Challenges.” International Journal of Energy Sector Management 15 (1): 209–26. https://doi.org/10.1108/IJESM-03-2020-0007. [Google Scholar]
  18. Yang, Hao-Fan, and Yi-Ping Phoebe Chen. (2019). “Representation Learning with Extreme Learning Machines and Empirical Mode Decomposition for Wind Speed Forecasting Methods.” Artificial Intelligence 277 (December): 103176. https://doi.org/10.1016/j.artint.2019.103176. [CrossRef] [Google Scholar]
  19. Yang, Luoxiao, and Zijun Zhang. (2021). “Wind Turbine Gearbox Failure Detection Based on SCADA Data: A Deep Learning-Based Approach.” IEEE Transactions on Instrumentation and Measurement 70: 1–11. https://doi.org/10.1109/TIM.2020.3045800. [Google Scholar]
  20. Yan, Xiaoan, Ying Liu, Yadong Xu, and Minping Jia. (2020). “Multistep Forecasting for Diurnal Wind Speed Based on Hybrid Deep Learning Model with Improved Singular Spectrum Decomposition.” Energy Conversion & Management 225 (December): 113456. https://doi.org/10.1016/j.enconman.2020.113456. [CrossRef] [Google Scholar]
  21. Ying, Xiang, Keke Zhao, Zhiqiang Liu, Jie Gao, Dongxiao He, Xuewei Li, and Wei Xiong. (2022). “Wind Speed Prediction via Collaborative Filtering on Virtual Edge Expanding Graphs.” Science in China, Series A: Mathematics 10 (11): 1943. https://doi.org/10.3390/math10111943. [Google Scholar]
  22. Yockey, Ronald D. (2017). SPSS® Demystified: A Simple Guide and Reference. Routledge. https://www.taylorfrancis.com/books/mono/10.4324/9781315268545/spss%C2%A E-demystified-ronald-yockey. [Google Scholar]

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