Open Access
E3S Web Conf.
Volume 173, 2020
2020 5th International Conference on Advances on Clean Energy Research (ICACER 2020)
Article Number 01004
Number of page(s) 4
Section Renewable Energy and Clean Energy
Published online 09 June 2020
  1. G. Zhang, B. E. Patuwo, and M. Y. Hu, “Forecasting with artificial neural networks:: The state of the art,” Int. J. Forecast., vol. 14, no. 1, pp. 35–62, (1998). [Google Scholar]
  2. K. Amarasinghe, D. L. Marino, and M. Manic, “Deep neural networks for energy load forecasting, ” IEEE Int. Symp. Ind. Electron., pp. 1483–1488, (2017). [Google Scholar]
  3. J. W. Taylor and P. E. McSharry, “Univariate methods for short-term load forecasting,” Advances in Electric Power and Energy Systems: Load and Price Forecasting. Wiley Online Library, pp. 17–39, (2017). [CrossRef] [Google Scholar]
  4. B. Kamranzad, A. Etemad-Shahidi, and M. H. Kazeminezhad, “Wave height forecasting in Dayyer, the Persian Gulf,” Ocean Eng., vol. 38, no. 1, pp. 248–255, (2011). [CrossRef] [Google Scholar]
  5. M. C. Deo, A. Jha, A. S Chaphekar, and K. Ravikant, “Neural networks for wave forecasting,” Ocean Eng., vol. 28, no. 7, pp. 889–898, (2001). [CrossRef] [Google Scholar]
  6. E. Nakamura, “Inflation forecasting using a neural network,” Econ. Lett., vol. 86, no. 3, pp. 373–378, (2005). [Google Scholar]
  7. Z.-W. Zheng, Y.-Y. Chen, X.-W. Zhou, M.-M. Huo, B. Zhao, and M. Guo, “Short-term wind power forecasting using empirical mode decomposition and RBFNN,” Int. J. Smart Grid Clean Energy, vol. 2, no. 2, pp. 192–199, (2013). [CrossRef] [Google Scholar]
  8. L. Shijian, H. Yongjun, and L. Fuchao, “Application of the combined model in short-term wind power forecasting.”, International Journal of Smart Grid and Clean Energy, vol. 5, no. 3, July 2016 [Google Scholar]
  9. U. Cali and V. Sharma, “Short-term wind power forecasting using long-short term memory based recurrent neural network model and variable selection,” Int. J. Smart Grid Clean Energy, vol. 8, no. 2, pp. 103–110, (2019). [CrossRef] [Google Scholar]
  10. J. Y. Jin, R. Ghani, and M. S. Virk, “Wind turbine wake effects on wind resource assessments—A case study.” International Journal of Smart Grid and Clean Energy, vol. 9, no. 1, January (2020). [Google Scholar]
  11. S. Al-Dahidi, P. Baraldi, E. Zio, and M. Lorenzo, “Quantification of uncertainty of wind energy predictions,” in Proc. 3rd Int. Conf. Syst. Rel. Saf., 2018, pp. 1–5, (2018). [Google Scholar]
  12. G. Petneházi, “Recurrent neural networks for time series forecasting,” arXiv Prepr. arXiv1901.00069, (2019). [Google Scholar]
  13. A. Subhajini and others, “Application of Neural Networks in Weather Forecasting,” (2014). [Google Scholar]
  14. K. Abhishek, M. P. Singh, S. Ghosh, and A. Anand, “Weather Forecasting Model using Artificial Neural Network,” Procedia Technol., vol. 4, pp. 311–318, (2012). [CrossRef] [Google Scholar]
  15. S. Suksri and W. Kimpan, “Neural network training model for weather forecasting using fireworks algorithm,” in 2016 International Computer Science and Engineering Conference (ICSEC), pp. 1–7, (2016). [Google Scholar]
  16. N. Kumar and G. K. Jha, “Time Series ANN Approach for Weather Forecasting,” Int. J. Control Theory Comput. Model., vol. 3, no. 1, pp. 19–25, (2013). [CrossRef] [Google Scholar]
  17. J. M. Al-Ansari, H. Bakhsh, and K. I. Madni, “Wind energy atlas for the kingdom of Saudi Arabia,” King Fahd Univ. Pet. Miner. Press. Dhahran, (1986). [Google Scholar]
  18. C. Lewis, “International and Business Forecasting Methods Butterworths: London, ” (1982). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.