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An Bayesian Learning and Nonlinear Regression Model for Photovoltaic Power Output Forecasting


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A. Tascikaraoglu, B. M. Sanandaji, G. Chicco, V. Cocina, F. Spertino, O. Erdinc, N. G. Paterakis, and J. P. S. Catalato (2016), Compressive spatio-temporal forecasting of meteorological quantities and photovoltaic power, IEEE Transactions on Sustainable Energy, vol. 7, no. 3, pp. 1295–1305.TascikaraogluA.SanandajiB. M.ChiccoG.CocinaV.SpertinoF.ErdincO.PaterakisN. G.CatalatoJ. P. S.2016Compressive spatio-temporal forecasting of meteorological quantities and photovoltaic powerIEEE Transactions on Sustainable Energy731295130510.1109/PTC.2017.7981257Search in Google Scholar

H. Sheng, J. Xiao, Y. Cheng, Q. Ni, and S. Wang (2018), Short-term solar power forecasting based on weighted gaussian process regression, IEEE Transactions on Industrial Electronics, vol. 65, no. 1, pp. 300–308.ShengH.XiaoJ.ChengY.NiQ.WangS.2018Short-term solar power forecasting based on weighted gaussian process regressionIEEE Transactions on Industrial Electronics65130030810.1109/TIE.2017.2714127Search in Google Scholar

S. Chai, M. Niu, Z. Xu, L. L. Lai, and K. P. Wong (2016), Nonparametric conditional interval forecasts for pv power generation considering the temporal dependence, in 2016 IEEE Power and Energy Society General Meeting (PESGM), pp. 1–5.ChaiS.NiuM.XuZ.LaiL. L.WongK. P.2016Nonparametric conditional interval forecasts for pv power generation considering the temporal dependencein2016 IEEE Power and Energy Society General Meeting (PESGM)15Search in Google Scholar

D. Pepe, G. Bianchini, and A. Vicino (2016), Model estimation for pv generation forecasting using cloud cover information, 2016 IEEE International Energy Conference (ENERGYCON), pp. 1–6.PepeD.BianchiniG.VicinoA.2016Model estimation for pv generation forecasting using cloud cover information2016 IEEE International Energy Conference (ENERGYCON)1610.1109/ENERGYCON.2016.7513967Search in Google Scholar

E. Nuao, M. Koivisto, N. Cutululis, and P. Sarensen (2017), Simulation of regional day-ahead pv power forecast scenarios, 2017 IEEE Manchester PowerTech, pp. 1–6.NuaoE.KoivistoM.CutululisN.SarensenP.2017Simulation of regional day-ahead pv power forecast scenarios2017 IEEE Manchester PowerTech1610.1109/PTC.2017.7981155Search in Google Scholar

Y. Zhang, M. Beaudin, R. Taheri, H. Zareipour, and D. Wood (2015), Day-ahead power output forecasting for small-scale solar photovoltaic electricity generators, IEEE Transactions on Smart Grid, vol. 6, no. 5, pp. 2253–2262.ZhangY.BeaudinM.TaheriR.ZareipourH.WoodD.2015Day-ahead power output forecasting for small-scale solar photovoltaic electricity generatorsIEEE Transactions on Smart Grid652253226210.1109/TSG.2015.2397003Search in Google Scholar

J. Vasilj, P. Sarajcev, and D. Jakus, Pv power forecast error simulation model (2015), in 2015 12th International Conference on the European Energy Market (EEM), pp. 1–5.VasiljJ.SarajcevP.JakusD.Pv power forecast error simulation model (2015)2015 12th International Conference on the European Energy Market (EEM)1510.1109/EEM.2015.7216680Search in Google Scholar

L. Oneto, F. Laureri, M. Robba, F. Delfino, and D. Anguita (2018), Data-driven photovoltaic power production now-casting and forecasting for polygeneration microgrids, IEEE Systems Journal, vol. 12, no. 3, pp. 2842–2853.OnetoL.LaureriF.RobbaM.DelfinoF.AnguitaD.2018Data-driven photovoltaic power production now-casting and forecasting for polygeneration microgridsIEEE Systems Journal1232842285310.1109/JSYST.2017.2688359Search in Google Scholar

X. G. Agoua, R. Girard, and G. Kariniotakis (2018), Short-term spatio-temporal forecasting of photovoltaic power production, IEEE Transactions on Sustainable Energy, vol. 9, no. 2, pp. 538–546,.AgouaX. G.GirardR.KariniotakisG.2018Short-term spatio-temporal forecasting of photovoltaic power productionIEEE Transactions on Sustainable Energy9253854610.1109/TSTE.2017.2747765Search in Google Scholar

B. Urquhart (2011), Intra-hour forecasting with a total sky imager at the UC San Diego solar energy testbed, ASES.UrquhartB.2011Intra-hour forecasting with a total sky imager at the UC San Diego solar energy testbedASESSearch in Google Scholar

R. Marquez and C. F. Coimbra (2013), Intra-hour dni forecasting based on cloud tracking image analysis, Solar Energy, vol. 91, pp. 327–336.MarquezR.CoimbraC. F.2013Intra-hour dni forecasting based on cloud tracking image analysisSolar Energy9132733610.1016/j.solener.2012.09.018Search in Google Scholar

H. Yang, B. Kurtz, D. Nguyen, B. Urquhart, C. W. Chow, M. Ghonima, and J. Kleissl (2014), Solar irradiance forecasting using a ground-based sky imager developed at uc san diego, Solar Energy, vol. 103, pp. 502–524.YangH.KurtzB.NguyenD.UrquhartB.ChowC. W.GhonimaM.KleisslJ.2014Solar irradiance forecasting using a ground-based sky imager developed at uc san diegoSolar Energy10350252410.1016/j.solener.2014.02.044Search in Google Scholar

S. Quesada-Ruiz, Y. Chu, J. Tovar-Pescador, H. Pedro, and C. Coimbra (2014), Cloud-tracking methodology for intra-hour dni forecasting, Solar Energy, vol. 102, pp. 267–275.Quesada-RuizS.ChuY.Tovar-PescadorJ.PedroH.CoimbraC.2014Cloud-tracking methodology for intra-hour dni forecastingSolar Energy10226727510.1016/j.solener.2014.01.030Search in Google Scholar

A. T. Lorenzo, W. F. Holmgren, M. Leuthold, C. K. Kim, A. D. Cronin, and E. A. Betterton (2014), Short-term pv power forecasts based on a real-time irradiance monitoring network, 2014 IEEE 40th Photovoltaic Specialist Conference (PVSC), pp. 0075–0079.LorenzoA. T.HolmgrenW. F.LeutholdM.KimC. K.CroninA. D.BettertonE. A.2014Short-term pv power forecasts based on a real-time irradiance monitoring network2014 IEEE 40th Photovoltaic Specialist Conference (PVSC)0075007910.1109/PVSC.2014.6925212Search in Google Scholar

R. Perez, S. Kivalov, J. Schlemmer, K. Hemker Jr, D. Renné, and T. E. Hoff (2010), Validation of short and medium term operational solar radiation forecasts in the us, Solar Energy, vol. 84, no. 12, pp. 2161–2172.PerezR.KivalovS.SchlemmerJ.HemkerK.JrRennéD.HoffT. E.2010Validation of short and medium term operational solar radiation forecasts in the usSolar Energy84122161217210.1016/j.solener.2010.08.014Search in Google Scholar

J. Liu, W. Fang, X. Zhang, and C. Yang (2015), An improved photovoltaic power forecasting model with the assistance of aerosol index data, IEEE Transactions on Sustainable Energy, vol. 6, no. 2, pp. 434–442.LiuJ.FangW.ZhangX.YangC.2015An improved photovoltaic power forecasting model with the assistance of aerosol index dataIEEE Transactions on Sustainable Energy6243444210.1109/TSTE.2014.2381224Search in Google Scholar

W. Fei, M. Zengqiang, S. Shi, and Z. Chengcheng (2011), A practical model for single-step power prediction of grid-connected pv plant using artificial neural network,’ 2011 IEEE PES Innovative Smart Grid Technologies, pp. 1–4.FeiW.ZengqiangM.ShiS.ChengchengZ.2011A practical model for single-step power prediction of grid-connected pv plant using artificial neural network,’2011 IEEE PES Innovative Smart Grid Technologies14Search in Google Scholar

T.-C. Yu and H.-T. Chang (2012), The forecast of the electrical energy generated by photovoltaic systems using neural network method, Electric Information and Control Engineering (ICEICE), 2011 International Conference on. IEEE, pp. 2758–2761.YuT.-C.ChangH.-T.2012The forecast of the electrical energy generated by photovoltaic systems using neural network methodElectric Information and Control Engineering (ICEICE), 2011 International Conference on. IEEE27582761Search in Google Scholar

J. Shi, W. J. Lee, Y. Liu, Y. Yang, and P. Wang (2012), Forecasting power output of photovoltaic systems based on weather classification and support vector machines, IEEE Transactions on Industry Applications, vol. 48, no. 3, pp. 1064–1069.ShiJ.LeeW. J.LiuY.YangY.WangP.2012Forecasting power output of photovoltaic systems based on weather classification and support vector machinesIEEE Transactions on Industry Applications4831064106910.1109/TIA.2012.2190816Search in Google Scholar

S. Qijun, L. Fen, Q. Jialin, Z. Jinbin, and C. Zhenghong (2016), Photovoltaic power prediction based on principal component analysis and support vector machine, 2016 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia), pp. 815–820.QijunS.FenL.JialinQ.JinbinZ.ZhenghongC.2016Photovoltaic power prediction based on principal component analysis and support vector machine2016 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia)81582010.1109/ISGT-Asia.2016.7796490Search in Google Scholar

Y. Liu, J. Zhao, M. Zhang, F. Liu, H. Ouyang, H. Fang, Q. Hao, and Y. Lu (2016), A novel photovoltaic power output forecasting method based on weather type clustering and wavelet support vector machines regression, 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), pp. 29–34.LiuY.ZhaoJ.ZhangM.LiuF.OuyangH.FangH.HaoQ.LuY.2016A novel photovoltaic power output forecasting method based on weather type clustering and wavelet support vector machines regression2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)293410.1109/FSKD.2016.7603146Search in Google Scholar

S. Liu and M. Dong (2016), Quantitative research on impact of ambient temperature and module temperature on short-term photovoltaic power forecasting, 2016 International Conference on Smart Grid and Clean Energy Technologies (ICSGCE), pp. 262–266.LiuS.DongM.2016Quantitative research on impact of ambient temperature and module temperature on short-term photovoltaic power forecasting2016 International Conference on Smart Grid and Clean Energy Technologies (ICSGCE)26226610.1109/ICSGCE.2016.7876066Search in Google Scholar

H. K. Elminir, Y. A. Azzam, and F. I. Younes (2007), Prediction of hourly and daily diffuse fraction using neural network, as compared to linear regression models, Energy, vol. 32, no. 8, pp. 1513–1523.ElminirH. K.AzzamY. A.YounesF. I.2007Prediction of hourly and daily diffuse fraction using neural network, as compared to linear regression modelsEnergy3281513152310.1016/j.energy.2006.10.010Search in Google Scholar

S. I. Sulaiman, T. K. A. Rahman, I. Musirin, and S. Shaari (2015), Artificial neural network versus linear regression for predicting grid-connected photovoltaic system output, Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), 2012 IEEE International Conference on. IEEE, pp. 170–174.SulaimanS. I.RahmanT. K. A.MusirinI.ShaariS.2015Artificial neural network versus linear regression for predicting grid-connected photovoltaic system outputCyber Technology in Automation, Control, and Intelligent Systems (CYBER), 2012 IEEE International Conference on. IEEE17017410.1109/CYBER.2012.6392548Search in Google Scholar

B. Saghafian, S. Anvari, and S. Morid (2013), Effect of southern oscillation index and spatially distributed climate data on improving the accuracy of artificial neural network, adaptive neuro-fuzzy inference system and k-nearest neighbour streamflow forecasting models, Expert Systems, vol. 30, no. 4, pp. 367–380.SaghafianB.AnvariS.MoridS.2013Effect of southern oscillation index and spatially distributed climate data on improving the accuracy of artificial neural network, adaptive neuro-fuzzy inference system and k-nearest neighbour streamflow forecasting modelsExpert Systems30436738010.1111/exsy.12009Search in Google Scholar

H. Long, Z. Zhang, and Y. Su (2014), Analysis of daily solar power prediction with data-driven approaches, Applied Energy, vol. 126, pp. 29–37.LongH.ZhangZ.SuY.2014Analysis of daily solar power prediction with data-driven approachesApplied Energy126293710.1016/j.apenergy.2014.03.084Search in Google Scholar

M. J. Sanjari and H. B. Gooi (2017), Probabilistic forecast of pv power generation based on higher order markov chain, IEEE Transactions on Power Systems, vol. 32, no. 4, pp. 2942–2952.SanjariM. J.GooiH. B.2017Probabilistic forecast of pv power generation based on higher order markov chainIEEE Transactions on Power Systems3242942295210.1109/TPWRS.2016.2616902Search in Google Scholar

W. Wu, K. Wang, B. Han, G. Li, X. Jiang, and M. L. Crow (2015), A versatile probability model of photovoltaic generation using pair copula construction, IEEE Transactions on Sustainable Energy, vol. 6, no. 4, pp. 1337–1345.WuW.WangK.HanB.LiG.JiangX.CrowM. L.2015A versatile probability model of photovoltaic generation using pair copula constructionIEEE Transactions on Sustainable Energy641337134510.1109/PESGM.2016.7741477Search in Google Scholar

Z. Ren, W. Yan, X. Zhao, W. Li, and J. Yu (2014), Chronological probability model of photovoltaic generation, IEEE Transactions on Power Systems, vol. 29, no. 3, pp. 1077–1088.RenZ.YanW.ZhaoX.LiW.YuJ.2014Chronological probability model of photovoltaic generationIEEE Transactions on Power Systems2931077108810.1109/TPWRS.2013.2293173Search in Google Scholar

M. Yang, S. Fan, and W. J. Lee (2012), Probabilistic short-term wind power forecast using componential sparse bayesian learning, in 48th IEEE Industrial Commercial Power Systems Conference, pp. 1–8.YangM.FanS.LeeW. J.2012Probabilistic short-term wind power forecast using componential sparse bayesian learningin48th IEEE Industrial Commercial Power Systems Conference1810.1109/TIA.2013.2265292Search in Google Scholar

Y. Sun, Y. Yuan, and G. Wang (2014), Extreme learning machine for classification over uncertain data, Neurocomputing, vol. 128, pp. 500–506.SunY.YuanY.WangG.2014Extreme learning machine for classification over uncertain dataNeurocomputing12850050610.1016/j.neucom.2013.08.011Search in Google Scholar

J. Moon, J. Park, E. Hwang, and S. Jun (2017), Forecasting power consumption for higher educational institutions based on machine learning, The Journal of Supercomputing, pp. 1–23.MoonJ.ParkJ.HwangE.JunS.2017Forecasting power consumption for higher educational institutions based on machine learningThe Journal of Supercomputing12310.1007/s11227-017-2022-xSearch in Google Scholar

M. E. Tipping (2001), Sparse bayesian learning and the relevance vector machine, Journal of machine learning research, vol. 1, no. Jun, pp. 211–244.TippingM. E.2001Sparse bayesian learning and the relevance vector machineJournal of machine learning research1Jun211244Search in Google Scholar

A. B. Chan and N. Vasconcelos (2012), Counting people with low-level features and bayesian regression, (2017) IEEE Transactions on Image Processing, vol. 21, no. 4, pp. 2160–2177, April 2012.ChanA. B.VasconcelosN.2012Counting people with low-level features and bayesian regression, (2017)IEEE Transactions on Image Processing21421602177April201210.1109/TIP.2011.217280022020684Search in Google Scholar

Y. Jia, S. Kwong, W. Wu, R. Wang, and W. Gao (2017), Sparse bayesian learning-based kernel poisson regression, IEEE Transactions on Cybernetics, pp. 1–13.JiaY.KwongS.WuW.WangR.GaoW.2017Sparse bayesian learning-based kernel poisson regressionIEEE Transactions on Cybernetics11310.1109/TCYB.2017.276409929990073Search in Google Scholar

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