# Solar Power Forecasting Techniques and Metrics for Accuracy of Solar Forecasting: A Review

## ##plugins.themes.academic_pro.article.main##

## Abstract

## ##plugins.themes.academic_pro.article.details##

*How to Cite*

*Power Research - A Journal of CPRI*,

*12*(2), 261–296. Retrieved from https://cprijournal.in/index.php/pr/article/view/280

* * References

- V Kostyle V and A Pa Vlo Vski, Solar Power Forecasting Performance Towards Industry Standards, Proc 1st Int Workshop on Integration of Solar Power into Power Systems, Aarhus, Denmark
- H M Diagne, M Da Vid, P Lauret, J Boland and N Schmutz, Re View of solar irradiance forecasting methods and a proposition for smallscale insular grids, Renew Sustain Energy Re V, Vol. 27, pp. 65–76, No. V, 2013
- M Noia, C Ratto and R Festa,Solar irradiance estimation from geostationary satellite data: II Physical models,Solar Energy, Vol. 51, No. 6, pp. 457 – 465, 1993
- C Gautier, G Diak and S Masse,A Simple Physical Model to Estimate Incident Solar Radiation at the Surface from GOES Satellite Data, Journal of Applied Meteorology, Vol. 19, No. 8, pp. 1005–1012, 1980
- C Raphael, Models for estimating solar irradiance at the Earth’s surface from satellite data: An initial assessment, Technical Report, Atmospheric En Vironment Ser Vice (Unpublished Manucript), 1983
- C Raphael and J E Hay, An assessment of models which use satellite data to estimate solar irradiance at the earth’s surface, Joural of Climate and Applied Meteorology, Vol. 23, No 5, pp. 832–844, 1984
- M Noia, C Ratto, and R Festa, Solar irradiance estimation from geostationary satellite data: I statistical models,Solar Energy, Vol. 51, No. 6, pp Coulson, Characteristics of the radiation emerging from the top of a rayleigh atmosphere—I: Intensity and polarization, Planetary and Space Science, Vol. 1, No. 4, pp. 265 – 276, 1959
- K Coulson, Characteristics of the radiation emerging from the top of a Rayleigh atmosphere—II: Total upward ﬂux and albedo,Planetary and Space Science, Vol. 1, No. 4, pp. 277 – 284, 1959
- G W Paltridge, Direct measurements of water Vapour absorption of solar radiation in the three atmosphere, J of Frouin, Downward longwa Ve irradiance at the ocean surface using satellite data: Methodology and in situ Validation, J of Geophysical Research, Vol. 93, pp. 597–598, 1985
- D Tanre, M Herman, P Y Deschamps and A DeLee, Atmospheric modeling for space measurements of fround relectances, including bidirectional properties, Applied Optics, Vol. 18, pp. 3587–3594, 1979
- S Marullo, G Dalu and A Viola,Incident short-wa Ve radiation at the surface from METEOSAT data,” Il Nuo Vo cimento della Societ`a italiana di ﬁsica, Vol. 10, No. 1, pp. 77 – 90, 1987
- J Schmetz, On the parameterization of the radiati Ve properties of broken clouds,Tellus Series A, Dynamic meteorology and oceanography, Vol. 36A, No. 5, pp. 417– 417, 1984
- W Mo¨ser and E Raschke, Incident Solar Radiation o Ver Europe Estimated from METEOSAT Data, J of Climate and Applied Meteorology, Vol. 23, No 1, pp. 166–170, 1984
- M Kerschegens, U Pilz, and E Raschke, A modiﬁed two stream approcimation for computations of the solar radiation budget in a couldy atmosphere, Tellus Series A, Dynamic meteorology and oceanography, Vol. 30, pp. 429–429,1978
- G Dedieu, P Y Deschamps, and Y H Kerr, Satellite Estimation of Solar Irradiance at the Surface of the Earth and of Surface Albedo Using a Physical Model Applied to METEOSAT Data, J of Climate and Applied Meteorology, Vol. 26, No. 1, pp. 79–79, 1987
- A A Lacis and J Hansen, A Parameterization for the Absorption of Solar Radiation in the Earth’s Atmosphere, J of the Atmospheric Sciences, Vol. 31, No. 1, pp. 118–133, 1974
- P Ineichen, Comparison of eight clear sky broadband models against 16 independent data banks, Solar Energy, Vol. 80, No. 4, pp. 468–478, 2006
- B Molineaux, P Ineichen and N O’Neill, Equi Valence of pyrheliometric and monochromatic aerosol optical depths at a single key wa Velength, Applied Optics, Vol. 37, pp. 7008–7018, 1998
- J E Hay and K J Hanson, A satellitebased methodology for determining solar irradiance at the ocean surface durring GATE, Bulletin of the American Meteorological Society, Vol. 59, pp. 1549– 1549, 1978
- J D Tarpley, Estimating incident solar radiation at the surface from geostationary satellite data, J of Applied Meteorology, Vol. 18, No. 9, pp. 1172–1181,1979
- C Raphael and J E Hay, An assessment of models which use satellite data to estimate solar irradiance at the earth’s surface, J of Climate and Applied Meteorology, Vol. 23, No. 5, pp. 832–844,1984
- C G Justus, M V Paris, and J D Tarpley, Satellite-measured insolation in the united states, mexico, and south america,Remote Sensing of En Vironment, Vol. 20, pp. 57– 83, 1986
- D Cano, Etude de l’Ennuagement par Analyse de S´equences d’Images de Satellite Application `a l’E Valuation du Rayonnement Solaire Gloibal au Sol Ph D thesis, Ecole Nationale Sup´erieure des t´el´ecommunications, 1982
- C Rigollier, O Bauer, and L Wald, On the clear sky model of the ESRA - European Solar Radiation Atlas - With respect to the Heliosat method, Solar Energy, Vol. 68, pp. 33–48, 2000
- G Bourges, Courbes de Frequence Cumulees de l’Irradiation Solaire Globale Horaire Recue par une Surface Plane, tech rep, Centre d’Energetique de l’Ecole National Superieur des Mines de Paris, 1979
- C Rigollier, The method HELIOSAT-2 for deri Ving shortwa Ve solar radiation from satellite images,Solar Energy, Vol. 77, No. 2, pp. 159–169, 2004
- M Girodo, R W Mueller and D Heinemann, Inﬂuence of three dimensional cloud eects on satellite deri Ved solar irradiance estimation First approaches to impro Ve the Heliosat method, Solar Energy, Vol. 80, No. 9, pp. 1145 – 1159, 2006
- L F Zarzalejo, L Ramirez, and J Polo, Artiﬁcial intelligence techniques applied to hourly global irradiance estimation from satellite-deri Ved cloud index, Energy, Vol. 30, No. 9, pp. 1685 – 1697, 2005
- K F Dagestad and J A Olseth, A modiﬁed algorithm for calculating the cloud index, Solar Energy, Vol. 81, No. 2, pp. 280 – 289, 2007
- R Mueller, K Dagestad, P Ineichen, M Schroedter-Homscheidt, S Cros, D Dumortier, R Kuhlemann, J Olseth, G Pierna Vieja, C Reise, L Wald, and D Heinemann, Rethinking satellite-based solar irradiance modelling: The SOLIS clear-sky module, Remote Sensing of En Vironment, Vol. 91, No. 2, pp. 160 – 174, 2004
- R Perez, P Ineichen, K Moore, M Kmiecik, C Chain, R George and F Vignola, A new operational model for satellite-deri Ved irradiances: description and Validation, Solar Energy, Vol. 73, No. 5, pp. 307 – 317, 2002
- P Ineichen, and R Perez, A new airmass independent formulation for the Linke turbidity coecient, Solar Energy, Vol. 73, No. 3, pp. 151–157, 2002
- V Badescu, Modeling Solar Radiation at the Earth Surface, Berlin Heidelberg: SpringerVerlag, 2008
- R Perez, P Ineichen, M Kmiecik, K Moore, D Renne and R George, Producing satellitederi Ved irradiances in complex arid terrain, Solar Energy, Vol. 77, No. 4, pp. 367 – 371, 2004
- CW, Chow, B Urquhart, M La Ve, A Dominguez, J Kleissl, J Shields and B Washom, Intra-hour forecasting with a total sky imager at the UC San Diego solar energy testbed, Solar Energy, Vol. 85, No 11, pp. 2881-2893, Nov. 2011.
- Y E Systems, TSI-880 Automatic Total Sky Imager Airport Industrial Park 101 Industrial Bl Vd Turners Falls, MA 01376 USA 2012.
- Y Chu, H T C Pedro, L Nonnenmacher, R H Inman, Z Liao and CFM Coimbra, A smart image-based cloud detection system for intra hour solar irradiance forecasts, J Atmos Oceanic Technol, Vol. 31, No. 9, pp. 1995 – 2007, Sep. 2014.
- R Marquez, H T C Pedro and C F M Coimbra, Hybrid solar forecasting method uses satellite imaging and ground telemetry as inputs to ANNs, Solar Energy, Vol. 92, pp. 176–188, Jun. 2013.
- S Quesada-Ruiz, Y Chu, J To VarPescador, H T C Pedro and CFM Coimbra, “Cloudtracking methodology for intra-hour {DNI} forecasting, Solar Energy, Vol. 102, pp. 267 – 275, Apr. 2014.
- D Bernecker, C Riess, E Angelopoulou and J Hornegger, Continuous short-term irradiance forecasts using sky images, Solar Energy, Vol. 110, pp.303 – 315, Dec. 2014.
- S RWest Rowe, D Sayeef S, and A Berry, Short-term irradiance forecasting using skycams: Moti Vation and de Velopment, Solar Energy, Vol. 110, pp.188 – 207, Dec 2014
- Y Chu, H T C Pedro and CFM Coimbra, Hybrid intra-hour DNI forecasts with sky image processing enhanced by stochastic learning, Solar Energy, Vol. 98, part C, pp. 592 – 603, Dec.2013.
- H Yang, B Kurtz, D Nguyen, B Urquhart, C W Chow, M Ghonima and J Kleissl, Solar irradiance forecasting using a ground-based sky imager de Veloped at UC San Diego, Solar Energy, Vol. 103, pp. 502 – 524, May 2014.
- R Li, B Zeng, and M L Liou, A new threestep search algorithm for block motion estimation, IEEE Trans on Circuits and Systems for Video Technology, Vol. 4, No. 4, pp. 438-442, Aug 1994.
- M Sayed, A fast architecture for exhausti Ve search block matching algorithm with MPEG4 applications, 16th IEEE Int Conf on Electronics, Circuits, and Systems, Yasmine Hammamet, pp. 787-790, Dec 2009.
- Q X Wu , A correlation-relaxation-labeling framework for computing optical flowtemplate matching from a new perspecti Ve, IEEE Trans Pattern Anal Mach Intell, Vol. 17, No. 9, pp. 843 – 853, Sep
- J R Stroud, M L Stein, B M Lesht , D J Schwab, and D Beletsky, An ensemble Kalman filter and smoother for satellite data assimilation, J of the American Statistical Association, Vol. 105, No. 491, pp. 978–990 2010.
- Y Chu, B Urquhart, S M I Gohari, H T C Pedro, J Kleissl and CFM Coimbra, Shortterm reforecasting of power output from a 48 MWe solar P V plant, Solar Energy, Vol. 112, pp. 68 – 77, Feb, 2015.
- C L Fu and H-Y Cheng, Predicting solar irradiance with all-sky image features Via regression, Solar Energy, Vol. 97, pp. 537 – 550, Nov, 2013
- A Kazantzidis, P Tzoumanikas, A F Bais, S Fotopoulos and G Economou, Cloud detection and classification with the use of wholesky ground-based images, Atmospheric Research, Vol. 113, pp. 80 – 88, Sep, 2012
- S Cros, O Liandrat, N Sebastien and N Schmutz, Extracting cloud motion Vectors from satellite images for solar power forecasting, IEEE Int Geoscience and Remote Sensing Symp, Quebec City, pp 4123 – 4126, 2014
- A Hammer, D Heinemann, E Lorenz and B Lückehe, Shortterm forecasting of solar radiation: a statistical approach using satellite data, Solar Energy, Vol. 67, No. 1-3, pp. 139 – 150, Jul, 1999
- R Perez, P Ineichen, K Moore, M Kmiecik, C Chain , R George and F Vignola, A new operational model for satellite-deri Ved irradiances: description and Validation” Solar Energy, Vol. 73, No. 5, pp. 307 – 317, Nov, 2002
- R Perez, P Ineichen, M Kmiecik, K Moore, D Renne and R George, Producing satellitederi Ved irradiances in complex arid terrain, Solar Energy, Vol. 77, No. 4, pp. 367 – 371, Oct, 2004
- R Marquez and C F M Coimbra, “Intra-Hour DNI Forecasting Methodology Based on Cloud Tracking Image Analysis,” submitted to Solar Energy, 2012
- M La Ve, J Kleissl, and E Arias-Castro, “High-frequency irradiance ﬂuctuations and geographic smoothing,” Solar Energy, pp. 1–17, 2011
- A Kamthe, R Marquez, C F M Coimbra, and A Cerpa, “Sub-Minute Solar Irradiance Forecasting Using Wireless Sensor Networks” Uni Versity of California, Merced, 2011
- V Bjerknes, E Volken, and S Bronnimann, The problem of weather prediction,considered from the Viewpoints of mechanics and physics, Meteorologische Zeitschrift, Vol. 18, No. 6, pp. 663-667, Dec, 2009
- E Lorenz, J Hurka, D Heinemann and H G Beyer, Irradiance forecasting for the power prediction of grid connected photo Voltaic systems”, IEEE J of Selected Topics in Applied Earth Obser Vations and Remote Sensing, Vol. 2, No.1, pp. 2-10, Apr, 2009
- P Mathiesen, C Collier, and J Kleissl, A high-resolution, cloudassimilating numerical weather prediction model for solar irradiance forecasting, Solar Energy, Vol. 92, No. 0, pp. 47 – 61, 2013
- R Perez, E Lorenz, S Pelland, M Beauharnois, G V Knowe, K H Jr, D Heinemann, J Remund, S C Muller, W Traunmuller, G Steinmauer, D Pozo, J A Ruiz-Arias, V Lara-Fanego, L RamirezSantigosa, M Gaston Romero, and L M Pomares, Comparison of numerical weather prediction solar irradiance forecasts in the US, Canada and Europe, Solar Energy, Vol. 94, No. 0, pp. 305-326, Aug, 2013
- H M Diagne, M Da Vid, J Boland, N Schmutz and P Lauret, Post-processing of solar irradiance forecasts from WRF model at Reunion Island, Solar Energy, Vol. 105, No. 0, pp. 99 – 108, Jul, 2014
- P Mathiesen and J Kleissl, E Valuation of numerical weather prediction for intraday solar forecasting in the continental united states, Solar Energy, Vol. 85, No. 5, pp. 967 – 977, May, 2011
- J Kleissl, Solar Energy Forecasting and Resource Assessment, Academic Press, 2013
- E Lorenz, D Heinemann, H Wickramarathne, H G Beyer and S Boﬁnger, Forecast of ensemble power production by gridconnected P V systems, Proc 20th European P V Conf, Milano, Italy, Sep, 2007
- E Lorenz, J Hurka,D Heinemann and H G Beyer, Irradiance forecasting for the power prediction of grid connected photo Voltaic systems, IEEE J of Selected Topics in Applied Earth Obser Vations and Remote Sensing, Vol. 2, No. 1, pp. 2-10, Apr, 2009
- D Heinemann, E Lorenz, and M Girodo, Forecasting of solar radiation In Solar Energy Resource Management for Electricity Generation from Local Le Vel to Global Scale, ED Dunlop, L Wald, and M Šúri (Eds), No Va Science Publishers, Hauppauge, NY, pp. 83-94, 2006
- R Perez, K Moore, S Wilcox, D Renné and A Zelenka, Forecasting solar radiation– preliminary e Valuation of an approach based upon the national forecast database, Solar Energy, Vol. 81, No. 6, pp. 809– 812, June, 2007
- J Remund, R Perez, and E Lorenz, Comparison of solar radiation forecasts for the USA, Proc 23rd European Photo Voltaic Solar Energy Conference, Valencia, Spain, pp. 19–49, 2008
- R Perez, S Ki Valo V, J Schlemmer, K H Jr, D Renne´ and TE Hoff, Validation of short and medium term operational solar radiation forecasts in the US, Solar Energy, Vol. 84, No 12, pp. 2161-2172, Dec, 2010
- S Bofinger and G Heilscher, Solar Electricity Forecast – Approaches and first results, Proc 21st European Photo Voltaic Solar Energy Conf, Dresden, Germany, 2006
- F Yang, H L Pan, and S K Krueger, E Valuation of the NCEP Global Forecast System at the ARM SGP Site, Monthly Weather Re View, Vol. 134, pp. 3668–3690, 2006
- F Yang, K Mitchell, Y T Hou, Y Dai, X Zeng, Z Wang and X-Z Liang, Dependence of Land Surface Albedo on Solar Zenith Angle: Obser Vations and Model Parameterization, Journal of Applied Meteorology and Climatology, Vol. 47, No. 11, pp. 2963– 2982, 2008
- E Lorenz, T Scheidsteger, J Hurka,D Heinemann and C Kurz, Regional P V power prediction for impro Ved grid integration, Progress in Photo Voltaics: Research and Applications, Vol. 19, No. 7, pp. 757-771, 2011
- E Lorenz, D Heinemann, and C Kurz, Local and regional photo Voltaic power prediction for large scale grid integration: assessment of a new algorithm for snow detection, Progress in Photo Voltaics: Research and Applications, Vol. 20, No. 6, pp. 760-769, 2011.
- S G Benjamin, W R Moninger W, S S Weygandt, M Hu, D De Venyi, J M Brown, T Smirno Va, J Olson, C Alexander, K Brundage, G Grell, S Peckham, T L Smith, S R Sahm and B Jamison,Technical Re View of Rapid Refresh/RUC Project, tech rep, NOAA/ESRL/GSD internal re View, Nov, 2009
- A Wold, Analysis of stationary time series, Uppsala: Almquist and Wicksell, 1938
- M Sulaiman, W Hlaing, M Wahab and Z Sulaiman, Analysis of residuals in daily solar radiation time series,Renewable Energy, Vol. 11, pp. 97–105, May 1997
- G E P Boxand G M Jenkins, Time series analysis, forecasting and control, Wiley, 1998
- L Ljung, System Identiﬁcation: Theory for the User, Prentice-Hall, 1987
- T N Goh and K J Tan, Stochastic modeling and forecasting of solar radiation data,Solar Energy, Vol. 19, No 6, pp. 755–757, 1977
- J A K Suykens, J P L Vandewalle and B L R Moor, Artiﬁcial neural networks for modelling and control of non-linear systems, The Netherlands: Kluwer Academic, 1996
- S M Al-Alawi and HA Al-Hinai, An ANN-based approach for predicting global radiation in locations with no direct measurement instrumentation,Renewable Energy, Vol. 14, No. 1-4, pp. 199 – 204, May-Aug, 1998
- G Capizzi, C Napoli, and F Bonanno, Inno Vati Ve secondgeneration wa Velets construction with recurrent neural networks for solar radiation forecasting, IEEE Trans on Neural Networks and Learning Systems, Vol. 23, No. 11, pp. 1805–1815, Nov, 2012
- S K Chow, E W Lee and DH Li, Short term prediction of photo Voltaic energy generation by intelligent approach, Energy andBuildings, Vol. 55, pp. 660–667, 2012
- A Sozen, E Arcaklioglu, M Ozalp and N Caglar, Forecasting based on neural network approach of solar potential in Turkey, Renewable Energy, Vol. 30, No. 7, pp. 1075 – 1090, 2005
- J Wu, and KC Chee, Prediction of hourly solar radiation using a no Vel hybrid model of ARMA and TDNN, Solar Energy, Vol. 85, No. 5, pp. 808 – 817, 2011
- J Cao, and X Lin, Study of hourly and daily solar irradiation forecast using diagonal recurrent wa Velet neural networks, Energy Con Version and Management, Vol. 49, No. 6, pp. 1396 – 1406, 2008
- F Almonacid, P Pérez-Higueras, E F Fernández, and L Hontoria, A methodology based on dynamic artificial neural network for short-term forecasting of the power output of a P V generator, Energy Con Version and Management, Vol. 85, pp. 389 – 398, 2014
- C S Ioakimidis, S Lopez, K N Genikomsakis, P Rycerski and D Simic, Solar production forecasting based on irradiance forecasting using artificial neural networks, 39th Annu Conf of Industrial Electronics Society (IECON), Vienna, pp. 8121-8126, Nov, 2013
- RM Ehsan, S P Simon and PR Venkateswaran, Day-ahead prediction of solar power output for grid-connected solar photo Voltaic installations using Artificial Neural Networks, IEEE 2nd Int Conf on Emerging Electronics (ICEE), Bangalore, pp. 1-4, Dec,2014
- A Yona , T Senjyu, A Saber, T Funabashi, H Sekine, and C H Kim, Application of neural network to 24-hour-ahead generating power forecasting for P V system, IEEE Proc in Power and Energy Society General Meeting - Con Version and Deli Very of Electrical Energy in the 21st Century, pp. 1 – 6, Jul, 2008
- Y K Wu, C R Chenand H Abdul-Rahman, A no Vel hybrid model for short-term forecasting in P V power generation, Int J of Photoenergy, Vol. 2014, pp. 1– 9, 2014
- F O G Hocaoglu, N Omer and M Kurba, Hourly solar radiation forecasting using optimal coefficient 2-D linear filters and feed forward neural networks, Solar Energy, Vol. 82, pp. 714–726, 2008
- A Azadeh, A Maghsoudi, and S Sohrabkhani, An integrated artificial neural networks approach for predicting global radiation, Energy Con Version and Management, Vol. 50, No. 6, pp. 1497 – 1505, 2009
- A Mellit and A M Pa Van, A 24-h forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid connected P Vplant at Trieste, Italy, Solar Energy, Vol. 84, No. 5, pp. 807 – 821, 2010
- W K Yap and V Karri, Comparati Ve study in predicting the global solar radiation for Darwin, Australia, J of Solar Energy Engineering, Vol. 134, No. 3, 2012
- R Guarnieri, F Martins, E Oereira and S Chuo, Solar radiation forecasting using artificial neural networks, National Institute for Space Research, Vol. 1, pp. 1–34, 2008
- Y Kemmoku, S Orita, S Nakagawa and T Sakakibara, Daily insolation forecasting using a multi-stage neural network, Solar Energy, Vol. 66, No. 3, pp. 193–199, 1999
- G Mihalakakou , M Santamouris and D N Asimakopoulos, The total solar radiation time series simulation in Athens, using neural networks, Theoretical and Applied Climatology, Vol. 66, pp 185–197, 2000
- S fetsos, and AH Coonick, Uni Variate and Multi Variate forecasting of hourly solar radiation with artificial intelligence techniques, Solar Energy, Vol. 68, pp. 169– 178, 2000
- S Cao , and J Cao, Forecast of solar irradiance using recurrent neural networks combined with wa Velet analysis, Applied Thermal Engineering, Vol. 25, No. 2-3, pp. 161 – 172, 2005
- J Cao, and S Cao, Study of forecasting solar irradiance using neural networks with preprocessing sample data by wa Velet analysis, Energy, Vol. 31, No. 15, pp. 3435 – 3445, 2006
- P Mandal , S T SMadhira, A U Haque, J Meng and R L Pineda, Forecasting power output of solar photo Voltaic system using wa Velet transform and artiﬁcial intelligence techniques, Proc Computer Science, Vol. 12, pp. 332 – 337, 2012
- A Mellit, M Benghanem and SA Kalogirou, An adapti Ve wa Velet-network model for forecasting daily total solar radiation, Applied Energy, Vol. 83, pp. 705– 722, 2006
- C Shuanghua, W Wenbing, C Jianbo, L Weidong, Y Guoging and C Jiacong, Forecast of Solar Irradiance using chaos optimization neural networks, Asia-Pacific Power and Energy Engineering Conf
- (APPEEC), Wuhan, pp. 1-4, Mar, 2009
- D K Chatur Vedi, Forecasting of Solar Power using Quantum GAGNN, Int J of Computer Applications, Vol. 128, No 3, pp. 15-19,2015
- W Jianping, X Yunlin, Z Chenghui and X Xiaobing, Daily solar radiation prediction based on Genetic Algorithm Optimization of Wa Velet Neural Network, Int Conf on Electrical and Control Engineering (ICECE), Yichang, pp. 602-605, Sep, 2011
- Y Zhengque, C Yapei, and X Jiapeng, Power generation forecasting model for photo Voltaic array based on generic algorithm and BP neural network, IEEE 3rd Int Conf on Cloud Computing and Intelligence Systems (CCIS), Shenzhen, pp. 380-383, Nov, 2014
- S Hussain and A Al Alili, Soft Computing approach for solar radiation prediction o Ver Abu Dhabi, UAE: A comparati Ve analysis, IEEE Conf on Smart Energy Grid Engineering (SEGE), Oshawa, ON, pp.1-6, Aug, 2015
- M Farhadi and M Farshad, A fuzzy inference self-organizing-map based model for short term load forecasting, Proc of 17th Conf on Electrical Power Distribution Network (EPDC), Tehran, pp. 1-9, May, 2012
- A Yona, T Senjyu, T Funabashi and C-H Kim, Neural Network for Long-Term Ahead P V Power Output Correction, IEEE Trans on Sustainable Energy, Vol. 4, No. 2, pp. 527-533, Apr, 2013
- V P Singh, V Vijay, M S Bhatt and D K Chatur Vedi, Generalized neural network methodology for short term solar power forecasting, 13th Int Conf on En Vironmental and Electrical Engineering (EEEIC), Wroclaw, pp. 58-62, Nov, 2013
- L A Fernandez-Jimenez, A Muoz-Jimenez, M Mendoza- Villena, A Falces, E GarciaGarrido, P M Lara-Santillan, E ZorzanoAlba, and P J Zorzano Santamaria, Shortterm power forecasting system for photo Voltaic plants, Renewable Energy, Vol. 44, No. 0, pp. 311 – 317, 2012
- E Grimaccia, M Mussetta and R Zich, Neuro-fuzzy predicti Ve model for P V energy production based on weather forecast, IEEE Int Conf on Fuzzy Systems (FUZZ), Taipei, pp. 2454-2457, Jun, 2011
- R Xu, H Chen, and X Sun, Short-term photo Voltaic power forecasting with weighted support Vector machine, IEEE Int Conf on Automation and Logistics (ICAL), pp. 248– 253,2012
- J Shi, W J Lee, Y Liu, Y Yang, and P Wang, Forecasting power output of photo Voltaic systems based on weather classiﬁcation and support Vector machines, IEEE Transon Industry Applications, Vol. 48, No. 3, pp. 1064 –1069, May-Jun, 2012
- A U Haque, M H Nehrir, and P Mandal, Solar P V power generation forecast using a hybrid intelligent approach, IEEE Conf on Power and Energy Society General Meeting (PES), Vancou Ver, BC, pp. 1-5, Jul, 2013
- Y Hong-Tzer, H Chao-Ming, H YannChang and P Yi-Shiang, A Weather-Based Hybrid Method for 1-Day Ahead Hourly Forecasting of P V Power Output, IEEE Trans on Sustainable Energy, Vol. 5, No. 3, pp. 917926, Jul,2014
- A Mellit, A Hadjarab, N Khorissi and H Salhi, An ANFISbased Forecasting for solar radiation data from sunshine duration and ambient temperature, IEEE Power Engineering Society General Meeting, Tampa, FL, pp. 1-6, Jun 2007
- Y Cheng, X Jiapeng, L Chen and Y Zhengqiu, A high concentrated photo Voltaic output power predicti Ve model based on Fuzzy Clustering and RBF neural network, IEEE 3rd Int Conf on Cloud Computing and Intelligence Systems (CCIS), Shenzhen, pp. 384-388, Nov, 2014
- M Cococcioni,E D’Andrea and B Lazzerini, 24-hour-ahead forecasting of energy production in solar P V systems, 11th Int Confon Intelligent Systems Design and Application (ISDA), Cordoba, pp. 12761281, Nov, 2011
- G Reikard, Predicting solar radiation at high resolutions: A comparison of time series forecasts, Solar Energy, Vol. 83, No. 3, pp. 342 – 349, Mar, 2009
- C Voyant, M Muselli, C Paoli, and M-L Ni Vet, Optimization of an artificial neural network dedicated to the multi Variate forecasting of daily global radiation, Energy, Vol. 36, Vol. 1, pp. 348 – 359, Jan, 2011
- C Chen, S Duan, T Cai , and B Liu, Online 24-h solar power forecasting based on weather type classiﬁcation using artiﬁcial neural network, Solar Energy, Vol. 85, No. 11, pp. 2856 – 2870, 2011
- Pedro H T and CFM Coimbra, (2012), “Assessment of forecasting techniques for solar power production with no exogenous inputs, Solar Energy, Vol. 86, No. 7, pp. 2017 – 2028
- R Marquez, H T C Pedro and C F M Coimbra Hybrid solar forecasting method uses satellite imaging and ground telemetry as inputs to ANNs, Solar Energy, Vol. 92, pp. 176–188, Jun, 2013
- Z Dong, D Yang, T Reindl and WM Walsh, Satellite image analysis and a hybrid ESSS/ ANN model to forecast solar irradiance in the tropics, Energy Con Version and Management, Vol. 79, pp. 66 – 73, 2014
- C Cornaro, M Pierro, F Bucci, Master optimization process based on neural networks ensemble for 24-h solar irradiance forecast, Solar Energy, Vol. 111, pp. 297 – 312, 2015
- J Zhang, B M Hodge, A Florita, S Lu, H F Hamann and V Banunarayanan, Metrics for E Valuating the Accuracy of Solar Power Forecasting, 3rd Int Workshop on Integration of Solar Power into Power Systems, London, Oct, 2013
- B Espinar, L Ramirez, A Drews, H G Bayer, L F Zarzalejo, J Polo and L Martin, Analysis of different comparison parameters applied to solar radiation data from satellite and German radiometric stations, Solar Energy, Vol. 83, No. 1, pp.118-125, 2009
- B M Hodge, K Orwig, and M Milligan, Examining Information Entropy Approaches as Wind Power Forecasting Performance Metrics, 12th Int Conf on Probabilistic Methods Applied to Power Systems, Istanbul, Turkey, pp. 1-6, June, 2012
- R J Bessa, V Miranda, A Botterud and J Wang, ‘Good’ or ‘bad’ wind power forecasts: A relati Ve concept, Int J on Wind Energy, Vol. 14, No. 5, pp. 625-636, July, 2011
- A Mills, and R Wiser, Implications of WideArea Geographic Di Versity for Short-Term Variability of Solar Power, Report on Environmental Energy Technologies Di Vision, Ernest Orlando Lawrence Berkeley National Laboratory, 2010
- K Orwig, B M Hodge, G Brinkman, E Ela, and M Milligan, Economic E Valuation of Short-Term Wind Power Forecasts in ERCOT: Preliminary Results”, 11th Annu Int Workshop on Large-Scale Integration of Wind Power into Power Systems as well as on Transmission Networks for Offshore Wind Power Plants Conference, Lisbon, Portugal, Nov 2012
- F O Hacaoglu, O N Gerek , and M Kurban, Hourly solar radiation forecasting using optimal coefficient 2-d linear filters and feed-forward neural networks, Solar Energy, Vol. 82, No. 8, pp. 714-726,2008
- Bacher, H Madsen and H A Nielsen, Online short-term solar power forecasting, Solar Energy, Vol. 83, No. 10, pp. 1772 – 1783, 2009
- L Martin, L F Zarzalejo, J Polo, A Na Varro, R Marchante, and M Cony, Prediction of global solar irradiance based on time series analysis: Application to solar thermal power plants energy production planning, Solar Energy, Vol. 84, No. 10, pp. 1772-1781, 2010
- A Mellit, H Eleuch, M Benghanem, C Elaoun and A M Pa Van, An adapti Ve model for predicting of global, direct and diffuse hourly solar irradiance, Energy Con Version and Management, Vol. 51, No. 4, pp. 771782, 2010
- C Paoli, C Voyant, M Muselli, and M–L Ni Vet, Forecasting of pre-processed daily solar radiation time series using neural networks, Solar Energy, Vol. 84, No 12, pp. 2146-2160, 2010
- R Marquez and C F M CoimbraForecasting of global and direct solar irradiance using stochastic learning methods, ground experiments and the NWS database, Solar Energy, Vol. 85, No. 5, pp. 746-56, 2011
- C Voyant , M Muselli, C Paoli and M –L Ni Vet, Numerical Weather Prediction (NWP) and hybrid ARMA/ANN model to predict global radiation, Energy, Vol. 39, No. 1, pp. 341-355, 2012
- R Marquez, V Gueorguie V, and C F M Coimbra, Forecasting of global horizontal irradiance using sky co Ver indices, ASME J of Solar Energy Engineering, Vol. 135, No. 1, pp. 61-69, 2013
- R Marquez and C F M Coimbra, Intra-hour DNI forecasting based on cloud tracking image analysis, Solar Energy, Vol. 91, pp. 327 – 336, 2013
- J L Bosch, Y Zheng and J Kleissl, (2013), Deri Ving cloud Velocity from an array of solar radiation measurements, Solar Energy, Vol. 87, pp. 196-203, 2013
- C Voyant, P Randimbi Vololona, M L Ni Vet, C Paoli and M Muselli, Twenty four hours ahead global irradiation forecasting using multi-layer perceptron, Meteorological Applications, Vol. 21, No. 3, pp. 644-655, 2014
- S Haykin, Neural Networks and Learning Machines”, Prentice Hall, 2008
- B Amrouche, and XL Pi Vert, Artificial Neural Network based daily local forecasting for global solar radiation, Applied Energy, Vol. 130, pp. 333-341,2014
- M Ghayekhloo , M Ghofrani, M B Menhaj, and R Azimi, A no Vel clustering approach for short-term solar radiation forecasting, Solar Energy, Vol. 122, pp. 1371-1383, 2015
- E Akarslan and F O Hacaoglu, A no Vel adapti Ve approach for hourly solar radiation forecasting”, Renewable Energy, Vol. 87, pp. 628-633, 2016
- Y Gala, A Fernandez, J Diaz , and J R Dorronsoro, Hybrid machine learning forecasting of solar radiation Values, Neurocomputing, Vol. 176, pp. 48-59, 2016
- VSharma,D Yang, W Walsh , T Reindl, Short term solar irradiance forecasting using a mixed wa Velet neural network,Renewable Energy, Vol. 90, pp. 481-492,2016
- R H Inman, H T CPedro and CFMCoimbra, Solar forecasting methods for renewable energy integration, Progress in Energy and Combustion Science, Vol. 39, pp. 535-576, 2013
- A Kamtha, R Marquez, C F M Coimbra and A Cerpa , Sub-minute solar irradiation forecasting using wireless sensor network, 2011