Open Access
ITM Web Conf.
Volume 32, 2020
International Conference on Automation, Computing and Communication 2020 (ICACC-2020)
Article Number 03010
Number of page(s) 7
Section Computing
Published online 29 July 2020
  1. H. Demirhan, Z. Renwick, Missing value imputation for short to mid-term horizontal solar irradiance data. Appl Energy, vol. 225, pp. 998-1012 (2018). 10.1016/j.apenergy.2018.05.054. [Google Scholar]
  2. A. Mellit, A.M. Pavan, A 24-h forecast of solar irradiance using artificial neural network: application for performance prediction of a grid-connected PV plant at Trieste, Italy. Solar Energy, vol. 84(5), pp. 807-821, (2010). 10.1016/j.solener.2010.02.006. [Google Scholar]
  3. S. Wu, C. Chang, S. Lee, Time Series Forecasting with Missing Values, 1st Int. Conf. Ind. Networks Intell. Syst., pp. 151-156, (2015). 10.4108/icst.iniscom.2015.258269. [Google Scholar]
  4. W. Shi et al., Effective prediction of missing data on Apache Spark over multivariable time series”, IEEE Trans. Big Data, vol. 6, pp. 57239-27248, (2017). 10.1109/TBDATA.2017.2719703. [Google Scholar]
  5. Yanjie Wei et al., Any-time Methods For Time-series Prediction With Missing Observations, IEEE International Congress on Big Data (BigData Congress), (2017). 10.1109/BigDataCongress.2017.62. [Google Scholar]
  6. M.O.D. Rizwan et al., A Novel Approach For Time Series Data Forecasting Based On Arima Model For Marine Fishes, International Conference on Algorithms, Methodology, Models and Applications in Emerging Technologies (ICAMMAET), (2017). 10.1109/ICAMMAET.2017.8186707. [Google Scholar]
  7. V. Layanun, S. Suksamosorn, J. Songsiri, Missing-data Imputation for Solar Irradiance Forecasting in Thailand. In: Proceedings of the SICE Annual Conference 2017 September 19-22, Kanazawa University, Kanazawa, Japan, (2017). 10.23919/SICE.2017.8105472. [Google Scholar]
  8. W.L. Junger, A.P. de Leon, Imputation of missing data in time series for air pollutants, Atmospheric Environment, vol. 102, pp. 96-104, (2015). 10.1016/j.atmosenv.2014.11.049. [Google Scholar]
  9. B. Amrouche, X. Pivert Le, Artificial neural network based daily local forecasting for global solar radiation. Appl Energy vol. 130, pp. 333-341 (2014) 10.1016/j.apenergy.2014.05.055. [Google Scholar]
  10. S.X. Chen, H.B. Gooi, M. Wang. Solar radiation forecast based on fuzzy logic and neural networks. Renew Energy, vol. 60, pp. 195-201 (2013). 10.1016/j.renene.2013.05.011. [Google Scholar]
  11. Z. Gao, W. Cheng, X. Qiu, L. Meng, A Missing Sensor Data Estimation Algorithm Based on Temporal and Spatial Correlation, International Journal of Distributed Sensor Networks, vol. 2015, pp. 1-10, (2015). 10.1155/2015/435391. [Google Scholar]
  12. Y. Guo, X. Song, D. Fang, An Efficient Missing Data Prediction Method Based on Kronecker Compressive Sensing in Multivariable Time Series.IEEE Trans, pp. 57239-57248 (2018). 0.1109/ACCESS.2018.2873414. [Google Scholar]
  13. M. David, L. Mazorra, P. Lauret, Comparison of intraday probabilistic forecasting of solar irradiance using only endogenous data. Int J Forecast, vol. 19(4), pp. 299-311 (2018). 10.1016/j.ijforecast.2018.02.003 . [Google Scholar]
  14. A. Oren, H. Elad, Z Assaf, Online time series prediction with missing data. In Proceedings of the 32nd International Conference on Machine Learning, vol. 37, pp. 2191-2199 (2015). [Google Scholar]
  15. S.A. Kalogirou, Artificial neural networks in renewable energy systems applications: a review. Renew Sustain Energy Rev, vol. 5(4), pp. 373-401 (2001). 10.1016/S1364-0321(01)00006-5. [Google Scholar]
  16. E. Akarslan, FO Hocaoglu. A novel adaptive approach for hourly solar radiation forecasting. Renew Energy, vol. 87, pp. 628-633 (2016). 10.1016/j.renene.2015.10.063. [Google Scholar]
  17. E. Koubli, D. Palmer, T. Betts, P. Rowley, R. Gottschalg, Inference of missing PV monitoring data using neural networks, 43rd IEEE Photovoltaic Specialists Conference, PVSC, Portland, pp. 1-6, (2016). 10.1109/PVSC.2016.7750305 . [Google Scholar]
  18. E. Koubli, D. Palmer, P. Rowley, R. Gottschalg. Inference of missing data in photovoltaic monitoring datasets, IET Renew. Power Gener., vol. 10(4), pp. 434-439, (2016). 10.1049/iet-rpg.2015.0355 [Google Scholar]

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