Short-term photovoltaic power prediction based on FVS-KELM method

Jun Li; Meng Li

doi:10.6180/jase.202006_23(2).0013

Short-term photovoltaic power prediction based on FVS-KELM method

Jun Li This email address is being protected from spambots. You need JavaScript enabled to view it.¹^,^2,3, Meng Li¹

¹School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou, Gansu 730070, P.R. China
²Gansu Provincial Engineering Technology Center for Informatization of Logistics & Transport Equipment, Lanzhou 730070, P.R.China
³Gansu Provincial Industry Technology Center of Logistics&Transport Equipment. Lanzhou 730070, P.R. China

Received: August 29, 2019
Accepted: February 17, 2020
Publication Date: June 1, 2020

Download Citation: ||https://doi.org/10.6180/jase.202006_23(2).0013

ABSTRACT

For the highly nonlinear short-term photovoltaic power problems affected by a variety of factors, a novel modeling method FVS-KELM based on feature vectors selection (FVS) algorithm and kernel extreme learning machine (KELM) is proposed. Firstly, FVS algorithm maps the input data to the high-dimensional feature space through kernel trick, considering the geometric features, selecting the relevant data subset to form the base vector of the subspace. Secondly, the input data is projected onto the feature subspace, and the final prediction model is established by the KELM method, which does not need set the number of the hidden layer nodes, and uses the kernel function representing the unknown nonlinear feature mapping of the hidden layer, so it has good generalization ability. In order to verify the effectiveness of the FVS-KELM modeling method, it is applied to the benchmark photovoltaic power prediction example provided by Global Energy Forecasting Competition 2014 (GEFCOM2014). Under the same condition, the FVS-KELM method is compared with extreme learning machine (ELM), support vector machine (SVM) method etc. The experimental results show that the FVS-KELM method can not only reduce the computational complexity, but also has good prediction accuracy and robustness, and the model has good generalization.

Keywords: Photovoltaic Power, Prediction, Feature Vectors Selection, Extreme Learning Machine, Kernel Method

REFERENCES

[1] Ahmed A., Khalid M. (2019) A review on the selected applications of forecasting models in renewable power systems, Renewable and Sustainable Energy Reviews 100, 9-21. doi.org/10.1016/j.rser.2018.09.046
[2] Sobri S., Koohi-Kamali S., Rahim N. A (2018) Solar photovoltaic generation forecasting methods: A review, Energy Conversion and Management, 156, 459-497. doi.org/10.1016/j.enconman.2017.11.019
[3] Barbieri F., Rajakaruna S., Ghosh A. (2017) Very short-term photovoltaic power forecasting with cloud modeling: A review, Renewable and Sustainable Energy Reviews 75, 242-263. doi.org/10.1016/j.rser.2016.10.068
[4] Tuohy A., Zack J., Haupt S E., et al. (2015) Solar forecasting: methods, challenges, and performance, IEEE Power and Energy Magazine, 13(6), 50-59.
[5] Dolara A., Leva S., Manzolini (2015) Comparison of different physical models for PV power output prediction, Solar energy,119, 83-99. doi.org/10.1016/j.solener.2015.06.017
[6] Pedro H. T. C., Coimbra C. F. M. (2012) Assessment of forecasting techniques for solar power production with no exogenous inputs, Solar Energy, 86(7), 2017-2028. doi.org/10.1016/j.solener.2012.04.004
[7] Li Y. T., Su Y., Shu L. J. (2014) An ARMAX model for forecasting the power output of a grid connected photovoltaic system, Renewable Energy, 66, 78-89. doi.org/10.1016/j.renene.2013.11.067
[8] Verzijlbergh R. A., Heijnen P. W., de Roode S. R. (2015) Alexander Los, Jonker H J. J. Improved model output statistics of numerical weather prediction based irradiance forecasts for solar power applications, Solar Energy, 118, 634-645. doi.org/10.1016/j.solener.2015.06.005
[9] Voyant C., Notton G., Kalogirou S., et al. (2017) Machine learning methods for solar radiation forecasting: A review, Renewable Energy, 105, 569-582. doi.org/10.1016/j.renene.2016.12.095
[10] Cervone G., Clemente-Harding L., Alessandrini S., et al. (2017) Short-term photovoltaic power forecasting using Artificial Neural Networks and an Analog Ensemble, Renewable energy, 108, 274-286. doi.org/10.1016/j.renene.2017.02.052
[11] Yao X. S., Wang Z. S., Zhang H. G. (2019) A novel photovoltaic power forecasting model based on echo state network, Neurocomputing, 325, 182-189. doi.org/10.1016/j.neucom.2018.10.022
[12] Leva S., Dolara A., Grimaccia F., et al. (2017) Analysis and validation of 24 hours ahead neural network forecasting of photovoltaic output power, Mathematics and computers in simulation, 131, 88-100. doi.org/10.1016/j.matcom.2015.05.010
[13] VanDeventer W., Jamei E., Thirunavukkarasu G. S., et al, (2019) Short-term PV power forecasting using hybrid GASVM technique, Renewable Energy, 140, 367-379. doi.org/10.1016/j.renene.2019.02.087
[14] Bouzerdoum M., Mellit A., Pavan A. M. (2013) A hybrid model (SARIMA–SVM) for short-term power forecasting of a small-scale grid-connected photovoltaic plant, Solar Energy, 98, 226-235. doi.org/10.1016/j.solener.2013.10.002
[15] Eseye A. T., Zhang J. H., Zheng D. Y. H. (2018) Short-term photovoltaic solar power forecasting using a hybrid Wavelet-PSO-SVM model based on SCADA and Meteorological information, Renewable energy, 118, 357-367. doi.org/10.1016/j.renene.2017.11.011
[16] Malvoni M., De Giorgi M. G., Congedo P. M. (2016) Photovoltaic forecast based on hybrid PCA–LSSVM using dimensionality reducted data, Neurocomputing, 211, 72-83. doi.org/10.1016/j.neucom.2016.01.104
[17] Huang G. B., Zhu Q. Y., Siew C. K. (2006) Extreme learning machine: theory and applications, Neurocomputing, 70(1), 489-501. doi.org/10.1016/j.neucom.2005.12.126
[18] Al-Dahidi S., Ayadi O., Adeeb J., et al, (2018) Extreme Learning Machines for Solar Photovoltaic Power Predictions, Energies, 11(10), 2725. doi.org/10.3390/en11102725
[19] Shamshirband S., Mohammadi K., Yee L., et al, (2015) A comparative evaluation for identifying the suitability of extreme learning machine to predict horizontal global solar radiation. Renewable and Sustainable Energy Reviews, 52, 1031-1042. doi.org/10.1016/j.rser.2015.07.173
[20] Tang P. Z., Chen D. Y., Hou S. (2016) Entropy method combined with extreme learning machine method for the short-term photovoltaic power generation forecasting, Chaos, Solitons & Fractals 89, 243-248. doi.org/10.1016/j.chaos.2015.11.008
[21] Li J., Li D. C. (2016) Wind power time series prediction using optimized kernel extreme learning machine method, Acta Physica Sinica, 65(13), 33-42.
[22] Davò F., Alessandrini S., Sperati S., et al, (2016) Post-processing techniques and principal component analysis for regional wind power and solar irradiance forecasting, Solar Energy, 134, 327-338. doi.org/10.1016/j.solener.2016.04.049
[23] Dou C. X., Qi H., Luo W., et al, (2018) Elman neural network based short-term photovoltaic power forecasting using association rules and kernel principal component analysis, Journal of Renewable and Sustainable Energy, 10(4), 043501.
[24] Baudat G., Anouar F. (2003) Feature vector selection and projection using kernels, Neurocomputing, 55(1-2), 21-38. doi.org/10.1016/S0925-2312(03)00429-6
[25] Zhang Y., Wang J. (2015) GEFCom2014 probabilistic solar power forecasting based on k-nearest neighbor and kernel density estimator, 2015 IEEE Power & Energy Society General Meeting. IEEE, 1-5.
[26] Hong T., Pinson P., Fan S., et al, (2016) Probabilistic energy forecasting: Global energy forecasting competition 2014 and beyond. doi.org/10.1016/j.ijforecast.2016.02.001
[27] Ahmed Mohammed A., Aung Z. (2016) Ensemble Learning Approach for Probabilistic Forecasting of Solar Power Generation, Energies, 9(12), 1017. doi.org/10.3390/en9121017