Stacking Model for Photovoltaic-Power-Generation Prediction
Despite the clean and renewable advantages of solar energy, the instability of photovoltaic power generation limits its wide applicability. In order to ensure stable power-grid operations and the safe dispatching of the power grid, it is necessary to develop a model that can accurately predict the p...
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| Vydáno v: | Sustainability Ročník 14; číslo 9; s. 5669 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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MDPI AG
01.05.2022
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| ISSN: | 2071-1050, 2071-1050 |
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| Abstract | Despite the clean and renewable advantages of solar energy, the instability of photovoltaic power generation limits its wide applicability. In order to ensure stable power-grid operations and the safe dispatching of the power grid, it is necessary to develop a model that can accurately predict the photovoltaic power generation. As a widely used prediction method, the stacking model has been applied in many fields. However, few studies have used stacking models to predict photovoltaic power generation. In the research, we develop four different stacking models that are based on extreme gradient boosting, random forest, light gradient boosting, and gradient boosting decision tree to predict photovoltaic power generation, by using two datasets. The results show that the prediction accuracy of the stacking model is higher than that of the single ensemble-learning model, and that the prediction accuracy of the Stacking-GBDT model is higher than the other stacking models. The stacking model that is proposed in this research provides a reference for the accurate prediction of photovoltaic power generation. |
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| AbstractList | Despite the clean and renewable advantages of solar energy, the instability of photovoltaic power generation limits its wide applicability. In order to ensure stable power-grid operations and the safe dispatching of the power grid, it is necessary to develop a model that can accurately predict the photovoltaic power generation. As a widely used prediction method, the stacking model has been applied in many fields. However, few studies have used stacking models to predict photovoltaic power generation. In the research, we develop four different stacking models that are based on extreme gradient boosting, random forest, light gradient boosting, and gradient boosting decision tree to predict photovoltaic power generation, by using two datasets. The results show that the prediction accuracy of the stacking model is higher than that of the single ensemble-learning model, and that the prediction accuracy of the Stacking-GBDT model is higher than the other stacking models. The stacking model that is proposed in this research provides a reference for the accurate prediction of photovoltaic power generation. |
| Author | Zhu, Tengteng Zhang, Hongchao |
| Author_xml | – sequence: 1 givenname: Hongchao orcidid: 0000-0002-1560-6940 surname: Zhang fullname: Zhang, Hongchao – sequence: 2 givenname: Tengteng surname: Zhu fullname: Zhu, Tengteng |
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| Cites_doi | 10.1613/jair.594 10.3390/en12071249 10.1016/j.solener.2019.07.061 10.1016/j.enconman.2016.04.051 10.1016/j.jclepro.2020.123285 10.1016/j.ijepes.2018.01.025 10.1016/j.renene.2019.12.131 10.1016/S0893-6080(05)80023-1 10.1109/TIA.2012.2190816 10.1016/j.energy.2020.117743 10.1016/j.renene.2020.05.150 10.1016/j.solener.2014.11.017 10.1016/j.apenergy.2019.114216 10.1016/j.enconman.2020.112582 10.1109/TSTE.2017.2762435 10.3390/en9010011 10.1016/j.renene.2018.02.006 10.3390/su132111833 10.1016/j.energy.2018.09.116 10.1016/j.egyr.2020.11.006 10.1146/annurev-soc-073117-041106 10.1109/ACCESS.2020.2981819 10.1016/j.enconman.2015.02.052 10.1016/j.ijepes.2015.02.006 10.1016/j.enbuild.2010.04.006 10.1016/j.solener.2012.04.004 10.1016/j.apenergy.2016.08.093 10.1016/j.renene.2020.09.141 10.1080/19397038.2021.1986590 10.1023/A:1010933404324 10.1016/j.apenergy.2017.06.104 10.1016/j.apenergy.2019.114001 10.1145/2939672.2939785 10.1162/neco.1997.9.8.1735 10.1007/3-540-45014-9_1 10.1016/j.renene.2017.11.011 10.1016/j.renene.2020.11.089 10.1109/TSTE.2014.2313600 10.1016/j.renene.2018.08.005 10.1109/ACCESS.2022.3156942 10.1016/j.renene.2013.11.067 10.1016/j.enconman.2016.05.025 10.1016/j.solener.2020.01.034 10.1016/j.solener.2009.05.016 10.1016/j.rser.2019.02.006 |
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| References | Shi (ref_27) 2012; 48 Ting (ref_48) 1999; 10 Hassan (ref_8) 2017; 203 Gupta (ref_15) 2021; 14 Dimd (ref_2) 2022; 10 Eseye (ref_34) 2017; 118 Gao (ref_39) 2020; 162 Guo (ref_19) 2020; 6 ref_17 ref_16 Liu (ref_28) 2019; 189 Bacher (ref_3) 2009; 83 Bessa (ref_4) 2015; 72 Hochreiter (ref_37) 1997; 9 Wolpert (ref_47) 1992; 5 Dewangan (ref_26) 2020; 202 ref_22 Chu (ref_5) 2015; 112 Fouilloy (ref_9) 2018; 165 Kumari (ref_46) 2021; 279 Ramsami (ref_35) 2015; 95 Yu (ref_43) 2010; 42 Cao (ref_50) 2019; 26 Reiman (ref_51) 2001; 45 Lou (ref_7) 2016; 181 Yagli (ref_11) 2019; 105 ref_32 ref_30 Li (ref_41) 2020; 259 Li (ref_6) 2014; 66 Breiman (ref_18) 1996; 46 Rosiek (ref_31) 2018; 99 Eom (ref_20) 2020; 8 Lee (ref_14) 2020; 208 Sharadga (ref_36) 2020; 150 Narvaez (ref_13) 2021; 167 Zang (ref_38) 2020; 160 Babar (ref_12) 2020; 198 ref_45 Koster (ref_21) 2019; 132 Rana (ref_24) 2016; 121 Pedro (ref_29) 2012; 86 ref_42 ref_1 Molina (ref_23) 2019; 45 Zheng (ref_40) 2020; 257 Sun (ref_44) 2016; 119 ref_49 Pedro (ref_10) 2018; 123 Gigoni (ref_25) 2017; 9 Yang (ref_33) 2014; 5 |
| References_xml | – volume: 10 start-page: 271 year: 1999 ident: ref_48 article-title: Issues in Stacked Generalization publication-title: J. Artif. Intell. Res. doi: 10.1613/jair.594 – ident: ref_32 doi: 10.3390/en12071249 – volume: 189 start-page: 291 year: 2019 ident: ref_28 article-title: A recursive ensemble model for forecasting the power output of photovoltaic systems publication-title: Sol. Energy doi: 10.1016/j.solener.2019.07.061 – volume: 119 start-page: 121 year: 2016 ident: ref_44 article-title: Assessing the potential of random forest method for estimating solar radiation using air pollution index publication-title: Energy Convers. Manag. doi: 10.1016/j.enconman.2016.04.051 – volume: 279 start-page: 123285 year: 2021 ident: ref_46 article-title: Extreme gradient boosting and deep neural network based ensemble learning approach to forecast hourly solar irradiance publication-title: J. Clean. Prod. doi: 10.1016/j.jclepro.2020.123285 – volume: 99 start-page: 261 year: 2018 ident: ref_31 article-title: Online 3-h forecasting of the power output from a BIPV system using satellite observations and ANN publication-title: Int. J. Electr. Power Energy Syst. doi: 10.1016/j.ijepes.2018.01.025 – volume: 150 start-page: 797 year: 2020 ident: ref_36 article-title: Time series forecasting of solar power generation for large-scale photovoltaic plants publication-title: Renew. Energy doi: 10.1016/j.renene.2019.12.131 – ident: ref_1 – volume: 5 start-page: 241 year: 1992 ident: ref_47 article-title: Stacked generalization publication-title: Neural Netw. doi: 10.1016/S0893-6080(05)80023-1 – volume: 48 start-page: 1064 year: 2012 ident: ref_27 article-title: Forecasting Power Output of photovoltaic Systems Based on Weather Classification and Support Vector Machines publication-title: IEEE Trans. Ind. Appl. doi: 10.1109/TIA.2012.2190816 – volume: 26 start-page: 2231 year: 2019 ident: ref_50 article-title: The Two-layer Classifier Model and its Application to Personal Credit Assessment publication-title: Control. Eng. China – volume: 202 start-page: 117743 year: 2020 ident: ref_26 article-title: Combining forecasts of day-ahead solar power publication-title: Energy doi: 10.1016/j.energy.2020.117743 – volume: 160 start-page: 26 year: 2020 ident: ref_38 article-title: Short-term global horizontal irradiance forecasting based on a hybrid CNN-LSTM model with spatiotemporal correlations publication-title: Renew. Energy doi: 10.1016/j.renene.2020.05.150 – volume: 112 start-page: 68 year: 2015 ident: ref_5 article-title: Short-term reforecasting of power output from a 48 MWe solar PV plant publication-title: Sol. Energy doi: 10.1016/j.solener.2014.11.017 – volume: 259 start-page: 114216 year: 2020 ident: ref_41 article-title: A hybrid deep learning model for short-term PV power forecasting publication-title: Appl. Energy doi: 10.1016/j.apenergy.2019.114216 – volume: 208 start-page: 112582 year: 2020 ident: ref_14 article-title: Reliable solar irradiance prediction using ensemble learning-based models: A comparative study publication-title: Energy Convers. Manag. doi: 10.1016/j.enconman.2020.112582 – volume: 9 start-page: 831 year: 2017 ident: ref_25 article-title: Day-Ahead Hourly Forecasting of Power Generation from photovoltaic Plants publication-title: IEEE Trans. Sustain. Energy doi: 10.1109/TSTE.2017.2762435 – ident: ref_30 doi: 10.3390/en9010011 – ident: ref_17 – ident: ref_45 – volume: 123 start-page: 191 year: 2018 ident: ref_10 article-title: Assessment of machine learning techniques for deterministic and probabilistic intra-hour solar forecasts publication-title: Renew. Energy doi: 10.1016/j.renene.2018.02.006 – ident: ref_49 doi: 10.3390/su132111833 – volume: 165 start-page: 620 year: 2018 ident: ref_9 article-title: Solar irradiation prediction with machine learning: Forecasting models selection method depending on weather variability publication-title: Energy doi: 10.1016/j.energy.2018.09.116 – volume: 6 start-page: 1424 year: 2020 ident: ref_19 article-title: Study on short-term photovoltaic power prediction model based on the Stacking ensemble learning publication-title: Energy Rep. doi: 10.1016/j.egyr.2020.11.006 – volume: 45 start-page: 27 year: 2019 ident: ref_23 article-title: Machine Learning for Sociology publication-title: Annu. Rev. Sociol. doi: 10.1146/annurev-soc-073117-041106 – volume: 8 start-page: 54620 year: 2020 ident: ref_20 article-title: Feature-Selective Ensemble Learning-Based Long-Term Regional PV Generation Forecasting publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2981819 – volume: 95 start-page: 406 year: 2015 ident: ref_35 article-title: A hybrid method for forecasting the energy output of photovoltaic systems publication-title: Energy Convers. Manag. doi: 10.1016/j.enconman.2015.02.052 – volume: 72 start-page: 16 year: 2015 ident: ref_4 article-title: Solar power forecasting in smart grids using distributed information publication-title: Int. J. Electr. Power Energy Syst. doi: 10.1016/j.ijepes.2015.02.006 – volume: 42 start-page: 1637 year: 2010 ident: ref_43 article-title: A decision tree method for building energy demand modeling publication-title: Energy Build. doi: 10.1016/j.enbuild.2010.04.006 – volume: 86 start-page: 2017 year: 2012 ident: ref_29 article-title: Assessment of forecasting techniques for solar power production with no exogenous inputs publication-title: Sol. Energy doi: 10.1016/j.solener.2012.04.004 – volume: 181 start-page: 367 year: 2016 ident: ref_7 article-title: Prediction of diffuse solar irradiance using machine learning and multivariable regression publication-title: Appl. Energy doi: 10.1016/j.apenergy.2016.08.093 – volume: 162 start-page: 1665 year: 2020 ident: ref_39 article-title: Hourly forecasting of solar irradiance based on CEEMDAN and multi-strategy CNN-LSTM neural networks publication-title: Renew. Energy doi: 10.1016/j.renene.2020.09.141 – volume: 46 start-page: 50 year: 1996 ident: ref_18 article-title: Bagging Predictors publication-title: Mach. Learn. – volume: 14 start-page: 1733 year: 2021 ident: ref_15 article-title: PV power forecasting based on data driven models: A review publication-title: Int. J. Sustain. Eng. doi: 10.1080/19397038.2021.1986590 – volume: 45 start-page: 5 year: 2001 ident: ref_51 article-title: Random forests publication-title: Mach. Learn. doi: 10.1023/A:1010933404324 – volume: 203 start-page: 897 year: 2017 ident: ref_8 article-title: Exploring the potential of tree-based ensemble methods in solar radiation modeling publication-title: Appl. Energy doi: 10.1016/j.apenergy.2017.06.104 – volume: 257 start-page: 114001 year: 2020 ident: ref_40 article-title: Time series prediction for output of multi-region solar power plants publication-title: Appl. Energy doi: 10.1016/j.apenergy.2019.114001 – ident: ref_16 doi: 10.1145/2939672.2939785 – volume: 9 start-page: 1735 year: 1997 ident: ref_37 article-title: Long Short-Term Memory publication-title: Neural Comput. doi: 10.1162/neco.1997.9.8.1735 – ident: ref_42 doi: 10.1007/3-540-45014-9_1 – volume: 118 start-page: 357 year: 2017 ident: ref_34 article-title: Short-term photovoltaic solar power forecasting using a hybrid Wavelet-PSO-SVM model based on SCADA and Meteorological information publication-title: Renew. Energy doi: 10.1016/j.renene.2017.11.011 – volume: 167 start-page: 333 year: 2021 ident: ref_13 article-title: Machine Learning for Site-adaptation and Solar Radiation Forecasting publication-title: Renew. Energy doi: 10.1016/j.renene.2020.11.089 – volume: 5 start-page: 917 year: 2014 ident: ref_33 article-title: A Weather-Based Hybrid Method for 1-Day Ahead Hourly Forecasting of PV Power Output publication-title: IEEE Trans. Sustain. Energy doi: 10.1109/TSTE.2014.2313600 – volume: 132 start-page: 55 year: 2019 ident: ref_21 article-title: Short-term and regionalized photovoltaic power forecasting, enhanced by reference systems, on the example of Luxembourg publication-title: Renew. Energy doi: 10.1016/j.renene.2018.08.005 – volume: 10 start-page: 26404 year: 2022 ident: ref_2 article-title: A Review of Machine Learning-Based photovoltaic Output Power Forecasting: Nordic Context publication-title: IEEE Access doi: 10.1109/ACCESS.2022.3156942 – volume: 66 start-page: 78 year: 2014 ident: ref_6 article-title: An ARMAX model for forecasting the power output of a grid connected photovoltaic system publication-title: Renew. Energy doi: 10.1016/j.renene.2013.11.067 – volume: 121 start-page: 380 year: 2016 ident: ref_24 article-title: Univariate and multivariate methods for very short-term solar photovoltaic power forecasting publication-title: Energy Convers. Manag. doi: 10.1016/j.enconman.2016.05.025 – volume: 198 start-page: 81 year: 2020 ident: ref_12 article-title: Random forest regression for improved mapping of solar irradiance at high latitudes publication-title: Sol. Energy doi: 10.1016/j.solener.2020.01.034 – ident: ref_22 – volume: 83 start-page: 1772 year: 2009 ident: ref_3 article-title: Online short-term solar power forecasting publication-title: Sol. Energy doi: 10.1016/j.solener.2009.05.016 – volume: 105 start-page: 487 year: 2019 ident: ref_11 article-title: Automatic hourly solar forecasting using machine learning models publication-title: Renew. Sustain. Energy Rev. doi: 10.1016/j.rser.2019.02.006 |
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