Solar Power Prediction via Support Vector Machine and Random Forest

Chih-Feng Yen; He-Yen Hsieh; Kuan-Wu Su; Min-Chieh Yu; Jenq-Shiou Leu

doi:10.1051/e3sconf/20186901004

All issues

Volume 69 (2018)

E3S Web Conf., 69 (2018) 01004

References

Open Access

Issue		E3S Web Conf. Volume 69, 2018 International Conference Green Energy and Smart Grids (GESG 2018)


Article Number		01004
Number of page(s)		6
Section		Properties, Regimes and Development of Renewable Energy Sources
DOI		https://doi.org/10.1051/e3sconf/20186901004
Published online		27 November 2018

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[1] W. Gu, Z. Wu, R. Bo, W. Liu, G. Zhou, W. Chen, and Z. Wu, ”Modeling, Planning and Optimal Energy Management of Combined Cooling, Heating and Power Microgrid: A Review,” International Journal of Electrical Power and Energy Systems, vol 54, pp. 26-37, 2014. [CrossRef] [Google Scholar]

[2] A. Hoke, R. B. J. Hambrick, and B. Kroposki, “Maximum Photovoltaic Penetration Levels on Typical Distribution Feeders,” Nat. Renew. Energy Lab., Golden, CO, USA, (No. NREL/JA-5500-55094), 2012. [Google Scholar]

[3] N. Sharma, P. Sharma, D. Irwin, and P. Shenoy, “Predicting Solar Generation from Weather Forecasts using Machine Learning,” in Proc. 2nd IEEE Int. Conf. Smart Grid Commun., pp. 528–533, Brussels, Belgium, Oct. 2011. [Google Scholar]

[4] A. Botterud, Z. Zhou, J. Wang, R. J. Bessa, H. Keko, J. Sumaili, and V. Miranda, “Wind Power Trading under Uncertainty in LMP markets,” IEEE Trans. Power Syst., vol. 27, no. 2, pp. 894–903, May 2012. [CrossRef] [Google Scholar]

[5] J. L. Torres, A. Garcia, M. De Blas, and A. De Francisco, “Forecast of Hourly Average Wind Speed with ARMA models in Navarre (Spain),” Solar Energy, vol. 79, no. 1, pp. 65–77, 2005. [CrossRef] [Google Scholar]

[6] G. Reikard, “Predicting Solar Radiation at High Resolutions: A Comparison of Time Series Forecasts,” Solar Energy, vol. 83, no. 3, pp. 342–349, 2009. [CrossRef] [Google Scholar]

[7] Y. Li, Y. Su, and L. Shu, “An ARMAX Model for Forecasting the Power Output of a Grid Connected Photovoltaic System,” Renew. Energy, vol. 66, pp. 78–89, 2014. [CrossRef] [Google Scholar]

[8] K. Benmouiza and A. Cheknane, “Forecasting Hourly Global Solar Radiation Using Hybrid K-means and Nonlinear Autoregressive Neural Network Models,” Energy Convers. Manag., vol. 75, pp. 561–569, 2013. [CrossRef] [Google Scholar]

[9] J. Shi, W.-J. Lee, Y. Liu, Y. Yang, and P. Wang, “Forecasting Power Output of Photovoltaic Systems Based on Weather Classification and Support Vector Machines,” IEEE Trans. Ind. Appl., vol. 48, no. 3, pp. 1064–1069, May/Jun. 2012. [CrossRef] [Google Scholar]

[10] M. Abuella, B. Chowdhury, Random Forest Ensemble of Support Vector Regression Models for Solar Power Forecasting, 2017. ArXiv170500033 Cs, http://arxiv.org/abs/1705.00033 (accessed February 21, 2018). [Google Scholar]

[11] P. H. Chiang, S. P. V. Chiluvuri, S. Dey, T. Q. Nguyen, “Forecasting of Solar Photovoltaic System. Power Generation using Wavelet Decomposition and Bias-compensated Random Forest,” in Green Technologies Conference (GreenTech), 2017 Ninth Annual IEEE, pp. 260-266, Mar. 2017. [CrossRef] [Google Scholar]

[12] Central Weather Bureau, “CWB Observation Data Inquire System,” http://e-service.cwb.gov.tw/HistoryDataQuery/index.jsp (accessed May 21, 2018). [Google Scholar]

[13] Ablerex Electronics Co., Ltd., “Solar System Monitoring database”, http://solar.ablerex.com.tw/SolarSystem/MainMap.aspx (accessed May 21, 2018). [Google Scholar]

[14] G. T. Ferrari and V. Ozaki, “Missing Data Imputation of Climate Datasets: Implications to Modeling Extreme Drought Events,” Revista Brasileira de Meteorologia, vol. 29, no. 1, pp. 21-28, 2014. [CrossRef] [Google Scholar]

[15] S. F. Wu, C. Y. Chang and S. J. Lee, “Time Series Forecasting with Missing Values,” in 2015 1st International Conference on Industrial Networks and Intelligent Systems (INISCom), IEEE, pp. 151-156, Mar. 2015. [Google Scholar]