References

• 1. 1)
     • 129. Lin, K.-P., Pai, P.-F.: ‘Solar power output forecasting using evolutionary seasonal decomposition least-square support vector regression’, J. Clean Prod., 2016, 134, pp. 456–462.
  2. 2)
     • 134. Zhu, H., Li, X., Sun, Q., et al: ‘A power prediction method for photovoltaic power plant based on wavelet decomposition and artificial neural networks’, Energies, 2015, 9, (1), p. 11.
  3. 3)
     • 142. Akarslan, E., Hocaoglu, F.O.: ‘A novel adaptive approach for hourly solar radiation forecasting’, Renew. Energy, 2016, 87, pp. 628–633.
  4. 4)
     • 94. Huang, G.-B., Zhu, Q.-Y., Siew, C.-K.: ‘Extreme learning machine: a new learning scheme of feedforward neural networks’. IEEE Proc., Int. Joint Conf. on Neural Networks, Budapest, Hungary, 2004, vol. 2, pp. 985–990.
  5. 5)
     • 79. Chiteka, K., Enweremadu, C.: ‘Prediction of global horizontal solar irradiance in Zimbabwe using artificial neural networks’, J. Clean Prod., 2016, 135, pp. 701–711.
  6. 6)
     • 31. Rafiei, M., Niknam, T., Aghaei, J., et al: ‘Probabilistic load forecasting using an improved wavelet neural network trained by generalized extreme learning machine’, IEEE Trans. Smart Grid, 2018, 9, (6), pp. 6961–6971.
  7. 7)
     • 144. Sözen, A., Arcaklioǧlu, E., Özalp, M., et al: ‘Use of artificial neural networks for mapping of solar potential in Turkey’, Appl. Energy, 2004, 77, (3), pp. 273–286.
  8. 8)
     • 65. Guo, J., You, S., Huang, C., et al: ‘An ensemble solar power output forecasting model through statistical learning of historical weather dataset’. Power and Energy Society General Meeting (PESGM), Boston, MA, USA, 2016, pp. 1–5.
  9. 9)
     • 12. Bulut, H., Büyükalaca, O.: ‘Simple model for the generation of daily global solar-radiation data in Turkey’, Appl. Energy, 2007, 84, (5), pp. 477–491.
  10. 10)
      • 6. Xin-gang, Z., You, Z.: ‘Technological progress and industrial performance: a case study of solar photovoltaic industry’, Renew. Sustain. Energy Rev., 2018, 81, pp. 929–936.
  11. 11)
      • 46. Huang, R., Huang, T., Gadh, R., et al: ‘Solar generation prediction using the ARMA model in a laboratory-level micro-grid’. IEEE Third Int. Conf. on Smart Grid Communications (SmartGridComm), Tainan, Taiwan, 2012, pp. 528–533.
  12. 12)
      • 25. Wolff, B., Kühnert, J., Lorenz, E., et al: ‘Comparing support vector regression for PV power forecasting to a physical modeling approach using measurement, numerical weather prediction, and cloud motion data’, Sol. Energy, 2016, 135, pp. 197–208.
  13. 13)
      • 18. Voyant, C., Notton, G., Kalogirou, S., et al: ‘Machine learning methods for solar radiation forecasting: a review’, Renew. Energy, 2017, 105, pp. 569–582.
  14. 14)
      • 35. Fliess, M., Join, C., Voyant, C.: ‘Prediction bands for solar energy: new short-term time series forecasting techniques’, Sol. Energy, 2018, 166, pp. 519–528.
  15. 15)
      • 8. Ramedani, Z., Omid, M., Keyhani, A., et al: ‘Potential of radial basis function based support vector regression for global solar radiation prediction’, Renew. Sustain. Energy Rev., 2014, 39, pp. 1005–1011.
  16. 16)
      • 131. Bouzerdoum, M., Mellit, A., Pavan, A.M.: ‘A hybrid model (SARIMA–SVM) for short-term power forecasting of a small-scale grid-connected photovoltaic plant’, Sol. Energy, 2013, 98, pp. 226–235.
  17. 17)
      • 115. Olatomiwa, L., Mekhilef, S., Shamshirband, S., et al: ‘Adaptive neuro-fuzzy approach for solar radiation prediction in Nigeria’, Renew. Sustain. Energy Rev., 2015, 51, pp. 1784–1791.
  18. 18)
      • 37. van der Meer, D., Munkhammar, J., Widén, J.: ‘Probabilistic forecasting of solar power, electricity consumption and net load: investigating the effect of seasons, aggregation and penetration on prediction intervals’, Sol. Energy, 2018, 171, pp. 397–413.
  19. 19)
      • 39. Yang, C., Thatte, A.A., Xie, L.: ‘Multitime-scale data-driven spatio-temporal forecast of photovoltaic generation’, IEEE Trans. Sustain. Energy, 2015, 6, (1), pp. 104–112.
  20. 20)
      • 104. Mojumder, J.C., Ong, H.C., Chong, W.T., et al: ‘Application of support vector machine for prediction of electrical and thermal performance in PV/T system’, Energy Build., 2016, 111, pp. 267–277.
  21. 21)
      • 2. Shafiee, S., Topal, E.: ‘When will fossil fuel reserves be diminished?’, Energy. Policy, 2009, 37, (1), pp. 181–189.
  22. 22)
      • 105. Kaba, K., Sarıgül, M., Avcı, M., et al: ‘Estimation of daily global solar radiation using deep learning model’, Energy, 2018, 162, pp. 126–135.
  23. 23)
      • 68. Huang, J., Perry, M.: ‘A semi-empirical approach using gradient boosting and k-nearest neighbors regression for GEFCom2014 probabilistic solar power forecasting’, Int. J. Forecast., 2016, 32, (3), pp. 1081–1086.
  24. 24)
      • 81. Mellit, A., Pavan, A.M.: ‘A 24-h forecast of solar irradiance using artificial neural network: application for performance prediction of a grid-connected PV plant at Trieste, Italy’, Sol. Energy, 2010, 84, (5), pp. 807–821.
  25. 25)
      • 11. Izadyar, N., Ong, H.C., Chong, W.T., et al: ‘Investigation of potential hybrid renewable energy at various rural areas in Malaysia’, J. Clean Prod., 2016, 139, pp. 61–73.
  26. 26)
      • 139. Ramsami, P., Oree, V.: ‘A hybrid method for forecasting the energy output of photovoltaic systems’, Energy Convers. Manage., 2015, 95, pp. 406–413.
  27. 27)
      • 23. Diagne, M., David, M., Lauret, P., et al: ‘Review of solar irradiance forecasting methods and a proposition for small-scale insular grids’, Renew. Sustain. Energy Rev., 2013, 27, pp. 65–76.
  28. 28)
      • 67. Persson, C., Bacher, P., Shiga, T., et al: ‘Multi-site solar power forecasting using gradient boosted regression trees’, Sol. Energy, 2017, 150, pp. 423–436.
  29. 29)
      • 146. Rubio, G., Pomares, H., Rojas, I., et al: ‘A heuristic method for parameter selection in LS-SVM: application to time series prediction’, Int. J. Forecast., 2011, 27, (3), pp. 725–739.
  30. 30)
      • 108. Bae, K.Y., Jang, H.S., Sung, D.K.: ‘Hourly solar irradiance prediction based on support vector machine and its error analysis’, IEEE Trans. Power Syst., 2017, 32, (2), pp. 935–945.
  31. 31)
      • 147. Jiang, H., Dong, Y.: ‘A nonlinear support vector machine model with hard penalty function based on glowworm swarm optimization for forecasting daily global solar radiation’, Energy Convers. Manage., 2016, 126, pp. 991–1002.
  32. 32)
      • 17. Bugała, A., Zaborowicz, M., Boniecki, P., et al: ‘Short-term forecast of generation of electric energy in photovoltaic systems’, Renew. Sustain. Energy Rev., 2018, 81, pp. 306–312.
  33. 33)
      • 103. Leva, S., Dolara, A., Grimaccia, F., et al: ‘Analysis and validation of 24 h ahead neural network forecasting of photovoltaic output power’, Math. Comput. Simul., 2017, 131, pp. 88–100.
  34. 34)
      • 57. Lucheroni, C.: ‘A resonating model for the power market and its calibration’. 6th Int. Conf. on the European, Energy Market, EEM, Camerino, Italy, 2009, pp. 1–5.
  35. 35)
      • 138. Malvoni, M., De Giorgi, M.G., Congedo, P.M.: ‘Photovoltaic forecast based on hybrid PCA–LSSVM using dimensionality reducted data’, Neurocomputing, 2016, 211, pp. 72–83.
  36. 36)
      • 61. Wang, G., Su, Y., Shu, L.: ‘One-day-ahead daily power forecasting of photovoltaic systems based on partial functional linear regression models’, Renew. Energy, 2016, 96, pp. 469–478.
  37. 37)
      • 53. Reikard, G.: ‘Predicting solar radiation at high resolutions: a comparison of time series forecasts’, Sol. Energy, 2009, 83, (3), pp. 342–349.
  38. 38)
      • 125. Olatomiwa, L., Mekhilef, S., Shamshirband, S., et al: ‘A support vector machine–firefly algorithm-based model for global solar radiation prediction’, Sol. Energy, 2015, 115, pp. 632–644.
  39. 39)
      • 82. Paoli, C., Voyant, C., Muselli, M., et al: ‘Forecasting of preprocessed daily solar radiation time series using neural networks’, Sol. Energy, 2010, 84, (12), pp. 2146–2160.
  40. 40)
      • 54. Hussain, S., Al Alili, A.: ‘Day ahead hourly forecast of solar irradiance for Abu Dhabi, UAE’. Smart Energy Grid Engineering (SEGE), 2016, pp. 68–71.
  41. 41)
      • 7. Gueymard, C.A.: ‘The sun's total and spectral irradiance for solar energy applications and solar radiation models’, Sol. Energy, 2004, 76, (4), pp. 423–453.
  42. 42)
      • 22. Voyant, C., Motte, F., Notton, G., et al: ‘Prediction intervals for global solar irradiation forecasting using regression trees methods’, Renew. Energy, 2018, 126, pp. 332–340.
  43. 43)
      • 118. Ogliari, E., Gandelli, A., Grimaccia, F., et al: ‘Neural forecasting of the day-ahead hourly power curve of a photovoltaic plant’. Int. Joint Conf. on Neural Networks (IJCNN), Vancouver, Canada, 2016, pp. 654–659.
  44. 44)
      • 119. Capizzi, G., Napoli, C., Bonanno, F.: ‘Innovative second-generation wavelets construction with recurrent neural networks for solar radiation forecasting’, IEEE Trans. Neural Netw. Learn. Syst., 2012, 23, (11), pp. 1805–1815.
  45. 45)
      • 71. Benali, L., Notton, G., Fouilloy, A., et al: ‘Solar radiation forecasting using artificial neural network and random forest methods: application to normal beam, horizontal diffuse and global components’, Renew. Energy, 2019, 132, pp. 871–884.
  46. 46)
      • 20. Sobri, S., Koohi-Kamali, S., Rahim, N.A.: ‘Solar photovoltaic generation forecasting methods: a review’, Energy Convers. Manage., 2018, 156, pp. 459–497.
  47. 47)
      • 44. Voyant, C., Notton, G.: ‘Solar irradiation nowcasting by stochastic persistence: a new parsimonious, simple and efficient forecasting tool’, Renew. Sustain. Energy Rev., 2018, 92, pp. 343–352.
  48. 48)
      • 52. Hamilton, J.D.: ‘Time series analysis’ (Princeton University Press Princeton, USA, 1994).
  49. 49)
      • 19. Raza, M.Q., Khosravi, A.: ‘A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings’, Renew. Sustain. Energy Rev., 2015, 50, pp. 1352–1372.
  50. 50)
      • 114. Wu, J., Chan, C.K., Zhang, Y., et al: ‘Prediction of solar radiation with genetic approach combing multi-model framework’, Renew. Energy, 2014, 66, pp. 132–139.
  51. 51)
      • 98. De Giorgi, M., Malvoni, M., Congedo, P.: ‘Comparison of strategies for multi-step ahead photovoltaic power forecasting models based on hybrid group method of data handling networks and least square support vector machine’, Energy, 2016, 107, pp. 360–373.
  52. 52)
      • 92. Junior, J.G.D.S.F., Oozeki, T., Ohtake, H., et al: ‘Forecasting regional photovoltaic power generation-a comparison of strategies to obtain one-day-ahead data’, Energy Procedia, 2014, 57, pp. 1337–1345.
  53. 53)
      • 55. Li, Y., Su, Y., Shu, L.: ‘An ARMAX model for forecasting the power output of a grid connected photovoltaic system’, Renew. Energy, 2014, 66, pp. 78–89.
  54. 54)
      • 60. Oudjana, S.H., Hellal, A., Mahamed, I.H.: ‘Short term photovoltaic power generation forecasting using neural network’. 11th Int. Conf. on Environment and Electrical Engineering (EEEIC), Venice, Italy, 2012, pp. 706–711.
  55. 55)
      • 97. Dolara, A., Grimaccia, F., Leva, S., et al: ‘A physical hybrid artificial neural network for short term forecasting of PV plant power output’, Energies, 2015, 8, (2), pp. 1138–1153.
  56. 56)
      • 95. Li, Z., Zang, C., Zeng, P., et al: ‘Day-ahead hourly photovoltaic generation forecasting using extreme learning machine’. IEEE Int. Conf. on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), Shenyang, China, 2015, pp. 779–783.
  57. 57)
      • 49. Blaga, R., Sabadus, A., Stefu, N., et al: ‘A current perspective on the accuracy of incoming solar energy forecasting’, Prog. Energy Combust. Sci., 2019, 70, pp. 119–144.
  58. 58)
      • 69. Fouilloy, A., Voyant, C., Notton, G., et al: ‘Solar irradiation prediction with machine learning: forecasting models selection method depending on weather variability’, Energy, 2018, 165, pp. 620–629.
  59. 59)
      • 42. Nonnenmacher, L., Kaur, A., Coimbra, C.F.: ‘Day-ahead resource forecasting for concentrated solar power integration’, Renew. Energy, 2016, 86, pp. 866–876.
  60. 60)
      • 34. Ni, Q., Zhuang, S., Sheng, H., et al: ‘An ensemble prediction intervals approach for short-term PV power forecasting’, Sol. Energy, 2017, 155, pp. 1072–1083.
  61. 61)
      • 78. Mellit, A., Pavan, A.M., Lughi, V.: ‘Short-term forecasting of power production in a large-scale photovoltaic plant’, Sol. Energy, 2014, 105, pp. 401–413.
  62. 62)
      • 122. Asrari, A., Wu, T.X., Ramos, B.: ‘A hybrid algorithm for short-term solar power prediction – sunshine state case study’, IEEE Trans. Sustain. Energy, 2017, 8, (2), pp. 582–591.
  63. 63)
      • 62. Massidda, L., Marrocu, M.: ‘Use of multilinear adaptive regression splines and numerical weather prediction to forecast the power output of a PV plant in Borkum, Germany’, Sol. Energy, 2017, 146, pp. 141–149.
  64. 64)
      • 33. Jiang, H., Zhang, Y., Muljadi, E., et al: ‘A short-term and high-resolution distribution system load forecasting approach using support vector regression with hybrid parameters optimization’, IEEE Trans. Smart Grid, 2018, 9, (4), pp. 3341–3350.
  65. 65)
      • 27. Verbois, H., Huva, R., Rusydi, A., et al: ‘Solar irradiance forecasting in the tropics using numerical weather prediction and statistical learning’, Sol. Energy, 2018, 162, pp. 265–277.
  66. 66)
      • 40. Azimi, R., Ghayekhloo, M., Ghofrani, M.: ‘A hybrid method based on a new clustering technique and multilayer perceptron neural networks for hourly solar radiation forecasting’, Energy Convers. Manage., 2016, 118, pp. 331–344.
  67. 67)
      • 43. Lipperheide, M., Bosch, J., Kleissl, J.: ‘Embedded nowcasting method using cloud speed persistence for a photovoltaic power plant’, Sol. Energy, 2015, 112, pp. 232–238.
  68. 68)
      • 140. Li, Y., He, Y., Su, Y., et al: ‘Forecasting the daily power output of a grid-connected photovoltaic system based on multivariate adaptive regression splines’, Appl. Energy, 2016, 180, pp. 392–401.
  69. 69)
      • 45. Yang, X., Ren, J., Yue, H.: ‘Photovoltaic power forecasting with a rough set combination method’. 11th UKACC Int. Conf. on Control (CONTROL 2016), 2016, pp. 1–6.
  70. 70)
      • 83. Gupta, M., Jin, L., Homma, N.: ‘Static and dynamic neural networks: from fundamentals to advanced theory’ (John Wiley & Sons, USA, 2004).
  71. 71)
      • 56. Huang, J., Korolkiewicz, M., Agrawal, M., et al: ‘Forecasting solar radiation on an hourly time scale using a Coupled AutoRegressive and Dynamical System (CARDS) model’, Sol. Energy, 2013, 87, pp. 136–149.
  72. 72)
      • 84. Mandal, P., Madhira, S.T.S., Meng, J., et al: ‘Forecasting power output of solar photovoltaic system using wavelet transform and artificial intelligence techniques’, Procedia Comput. Sci., 2012, 12, pp. 332–337.
  73. 73)
      • 73. Mellit, A., Kalogirou, S.A., Drif, M.: ‘Application of neural networks and genetic algorithms for sizing of photovoltaic systems’, Renew. Energy, 2010, 35, (12), pp. 2881–2893.
  74. 74)
      • 28. Netsanet, S., Zhang, J., Zheng, D., et al: ‘An aggregative machine learning approach for output power prediction of wind turbines’. Texas Power and Energy Conf. (TPEC), College Station, TX, USA, 2018, pp. 1–6.
  75. 75)
      • 141. Monjoly, S., André, M., Calif, R., et al: ‘Hourly forecasting of global solar radiation based on multiscale decomposition methods: a hybrid approach’, Energy, 2017, 119, pp. 288–298.
  76. 76)
      • 9. Shamshirband, S., Mohammadi, K., Khorasanezadeh, H., et al: ‘Estimating the diffuse solar radiation using a coupled support vector machine–wavelet transform model’, Renew. Sustain. Energy Rev., 2016, 56, pp. 428–435.
  77. 77)
      • 24. Ghimire, S., Deo, R.C., Downs, N.J., et al: ‘Self-adaptive differential evolutionary extreme learning machines for long-term solar radiation prediction with remotely-sensed MODIS satellite and reanalysis atmospheric products in solar-rich cities’, Remote Sens. Environ., 2018, 212, pp. 176–198.
  78. 78)
      • 3. Das, U.K., Tey, K., Seyedmahmoudian, M., et al: ‘Forecasting of photovoltaic power generation and model optimization: a review’, Renew. Sustain. Energy Rev., 2018, 81, pp. 912–928.
  79. 79)
      • 15. Hepbasli, A., Alsuhaibani, Z.: ‘A key review on present status and future directions of solar energy studies and applications in Saudi Arabia’, Renew. Sustain. Energy Rev., 2011, 15, (9), pp. 5021–5050.
  80. 80)
      • 64. Yang, D., Ye, Z., Lim, L.H.I., et al: ‘Very short term irradiance forecasting using the lasso’, Sol. Energy, 2015, 114, pp. 314–326.
  81. 81)
      • 38. Rawat, R., Vora, K., Manry, M., et al: ‘Multi-variable neural network forecasting using two stage feature selection’. 13th Int. Conf. on Machine Learning and Applications (ICMLA), Detroit, MI, USA, 2014, pp. 243–250.
  82. 82)
      • 135. Ghayekhloo, M., Ghofrani, M., Menhaj, M., et al: ‘A novel clustering approach for short-term solar radiation forecasting’, Sol. Energy, 2015, 122, pp. 1371–1383.
  83. 83)
      • 51. Boland, J., David, M., Lauret, P.: ‘Short term solar radiation forecasting: Island versus continental sites’, Energy, 2016, 113, pp. 186–192.
  84. 84)
      • 113. Tao, Y., Chen, Y.: ‘Distributed PV power forecasting using genetic algorithm based neural network approach’. Int. Conf. on Advanced Mechatronic Systems (ICAMechS), Kumamoto, Japan, 2014, pp. 557–560.
  85. 85)
      • 75. Chen, C., Duan, S., Cai, T., et al: ‘Online 24-h solar power forecasting based on weather type classification using artificial neural network’, Sol. Energy, 2011, 85, (11), pp. 2856–2870.
  86. 86)
      • 29. Negnevitsky, M., Mandal, P., Srivastava, A.K.: ‘Machine learning applications for load, price and wind power prediction in power systems’. 2009 15th Int. Conf. on Intelligent System Applications to Power Systems, Curitiba, Brazil, 2009, pp. 1–6.
  87. 87)
      • 36. Quan, H., Srinivasan, D., Khosravi, A.: ‘Particle swarm optimization for construction of neural network-based prediction intervals’, Neurocomputing, 2014, 127, pp. 172–180.
  88. 88)
      • 48. Ji, W., Chee, K.C.: ‘Prediction of hourly solar radiation using a novel hybrid model of ARMA and TDNN’, Sol. Energy, 2011, 85, (5), pp. 808–817.
  89. 89)
      • 16. Besharat, F., Dehghan, A.A., Faghih, A.R.: ‘Empirical models for estimating global solar radiation: a review and case study’, Renew. Sustain. Energy Rev., 2013, 21, pp. 798–821.
  90. 90)
      • 59. Keshtegar, B., Mert, C., Kisi, O.: ‘Comparison of four heuristic regression techniques in solar radiation modeling: kriging method vs RSM, MARS and M5 model tree’, Renew. Sustain. Energy Rev., 2018, 81, pp. 330–341.
  91. 91)
      • 136. Jiang, H., Dong, Y., Xiao, L.: ‘A multi-stage intelligent approach based on an ensemble of two-way interaction model for forecasting the global horizontal radiation of India’, Energy Convers. Manage., 2017, 137, pp. 142–154.
  92. 92)
      • 63. Trapero, J.R., Kourentzes, N., Martin, A.: ‘Short-term solar irradiation forecasting based on dynamic harmonic regression’, Energy, 2015, 84, pp. 289–295.
  93. 93)
      • 106. Torres, J.F., Troncoso, A., Koprinska, I., et al: ‘Deep learning for Big data time series forecasting applied to solar power’. The 13th Int. Conf. on Soft Computing Models in Industrial and Environmental Applications, Springer, 2018, pp. 123–133.
  94. 94)
      • 88. Quej, V.H., Almorox, J., Arnaldo, J.A., et al: ‘ANFIS, SVM and ANN soft-computing techniques to estimate daily global solar radiation in a warm sub-humid environment’, J. Atmos. Sol.-Terr. Phys., 2017, 155, pp. 62–70.
  95. 95)
      • 66. Verbois, H., Rusydi, A., Thiery, A.: ‘Probabilistic forecasting of day-ahead solar irradiance using quantile gradient boosting’, Sol. Energy, 2018, 173, pp. 313–327.
  96. 96)
      • 124. Mohammadi, K., Shamshirband, S., Tong, C.W., et al: ‘A new hybrid support vector machine–wavelet transform approach for estimation of horizontal global solar radiation’, Energy Convers. Manage., 2015, 92, pp. 162–171.
  97. 97)
      • 96. Huang, G.-B., Zhu, Q.-Y., Siew, C.-K.: ‘Extreme learning machine: theory and applications’, Neurocomputing, 2006, 70, (1–3), pp. 489–501.
  98. 98)
      • 127. Jiménez-Pérez, P.F., Mora-López, L.: ‘Modeling and forecasting hourly global solar radiation using clustering and classification techniques’, Sol. Energy, 2016, 135, pp. 682–691.
  99. 99)
      • 14. Halawa, E., GhaffarianHoseini, A., Li, D.H.W.: ‘Empirical correlations as a means for estimating monthly average daily global radiation: a critical overview’, Renew. Energy, 2014, 72, pp. 149–153.
  100. 100)
       • 26. Ogliari, E., Dolara, A., Manzolini, G., et al: ‘Physical and hybrid methods comparison for the day ahead PV output power forecast’, Renew. Energy, 2017, 113, pp. 11–21.
  101. 101)
       • 76. Yona, A., Senjyu, T., Saber, A.Y., et al: ‘Application of neural network to one-day-ahead 24 h generating power forecasting for photovoltaic system’. Int. Conf. on Intelligent Systems Applications to Power Systems, ISAP, Niigata, Japan, 2007, pp. 1–6.
  102. 102)
       • 47. Rajagopalan, S., Santoso, S.: ‘Wind power forecasting and error analysis using the autoregressive moving average modeling’. IEEE Power & Energy Society General Meeting, PES, Calgary, Canada, 2009, pp. 1–6.
  103. 103)
       • 21. Antonanzas, J., Osorio, N., Escobar, R., et al: ‘Review of photovoltaic power forecasting’, Sol. Energy, 2016, 136, pp. 78–111.
  104. 104)
       • 90. Hossain, M., Mekhilef, S., Olatomiwa, L.: ‘Performance evaluation of a stand-alone PV-wind-diesel-battery hybrid system feasible for a large resort center in South China Sea, Malaysia’, Sustain. Cities Soc., 2017, 28, pp. 358–366.
  105. 105)
       • 87. Din, G.M.U., Marnerides, A.K.: ‘Short term power load forecasting using deep neural networks’. Int. Conf. on Computing, Networking and Communications (ICNC), Santa Clara, CA, USA, 2017, pp. 594–598.
  106. 106)
       • 100. Sharma, A.A.: ‘Univariate short term forecasting of solar irradiance using modified online backpropagation through time’. Int., Computer Science and Engineering Conf. (ICSEC), Chiang Mai, Thailand, 2016, pp. 1–6.
  107. 107)
       • 13. GhaffarianHoseini, A., Dahlan, N.D., Berardi, U., et al: ‘Sustainable energy performances of green buildings: a review of current theories, implementations and challenges’, Renew. Sustain. Energy Rev., 2013, 25, pp. 1–17.
  108. 108)
       • 117. Bigdeli, N., Borujeni, M.S., Afshar, K.: ‘Time series analysis and short-term forecasting of solar irradiation, a new hybrid approach’, Swarm. Evol. Comput., 2017, 34, pp. 75–88.
  109. 109)
       • 128. Mao, M., Gong, W., Chang, L.: ‘Short-term photovoltaic output forecasting model for economic dispatch of power system incorporating large-scale photovoltaic plant’. Energy Conversion Congress and Exposition (ECCE), Denver, CO, USA, 2013, pp. 4540–4545.
  110. 110)
       • 86. Rana, M., Chandra, R., Agelidis, V.G.: ‘Cooperative neuro-evolutionary recurrent neural networks for solar power prediction’. IEEE Congress on Evolutionary Computation (CEC), Vancouver, Canada, 2016, pp. 4691–4698.
  111. 111)
       • 32. Sangrody, H., Zhou, N., Tutun, S., et al: ‘Long term forecasting using machine learning methods’. Power and Energy Conf. at Illinois (PECI), Champaign, IL, USA, 2018, pp. 1–5.
  112. 112)
       • 130. Fentis, A., Bahatti, L., Mestari, M., et al: ‘Short-term solar power forecasting using support vector regression and feed-forward NN’. 15th IEEE Int. New Circuits and Systems Conf. (NEWCAS), Strasbourg, France, 2017, pp. 405–408.
  113. 113)
       • 80. Yun, Z., Quan, Z., Caixin, S., et al: ‘RBF neural network and ANFIS-based short-term load forecasting approach in real-time price environment’, IEEE Trans. Power Syst., 2008, 23, (3), pp. 853–858.
  114. 114)
       • 111. Pedro, H.T., Coimbra, C.F.: ‘Assessment of forecasting techniques for solar power production with no exogenous inputs’, Sol. Energy, 2012, 86, (7), pp. 2017–2028.
  115. 115)
       • 99. Renno, C., Petito, F., Gatto, A.: ‘ANN model for predicting the direct normal irradiance and the global radiation for a solar application to a residential building’, J. Clean Prod., 2016, 135, pp. 1298–1316.
  116. 116)
       • 110. dos Santos, C.M., Escobedo, J.F., Teramoto, É.T., et al: ‘Assessment of ANN and SVM models for estimating normal direct irradiation (Hb)’, Energy Convers. Manage., 2016, 126, pp. 826–836.
  117. 117)
       • 133. Cristaldi, L., Leone, G., Ottoboni, R.: ‘A hybrid approach for solar radiation and photovoltaic power short-term forecast’. IEEE Int. Instrumentation and Measurement Technology Conf. (I2MTC), Turin, Italy, 2017, pp. 1–6.
  118. 118)
       • 126. Niu, D., Wang, Y., Wu, D.D.: ‘Power load forecasting using support vector machine and ant colony optimization’, Expert Syst. Appl., 2010, 37, (3), pp. 2531–2539.
  119. 119)
       • 85. Yona, A., Senjyu, T., Funabashi, T., et al: ‘Determination method of insolation prediction with fuzzy and applying neural network for long-term ahead PV power output correction’, IEEE Trans. Sustain. Energy, 2013, 4, (2), pp. 527–533.
  120. 120)
       • 107. Chen, J.-L., Li, G.-S.: ‘Evaluation of support vector machine for estimation of solar radiation from measured meteorological variables’, Theor. Appl. Climatol., 2014, 115, (3–4), pp. 627–638.
  121. 121)
       • 123. AlHakeem, D., Mandal, P., Haque, A.U., et al: ‘A new strategy to quantify uncertainties of wavelet-GRNN-PSO based solar PV power forecasts using bootstrap confidence intervals’. Power & Energy Society General Meeting, Denver, CO, USA, 2015, pp. 1–5.
  122. 122)
       • 10. Halabi, L.M., Mekhilef, S., Olatomiwa, L., et al: ‘Performance analysis of hybrid PV/diesel/battery system using HOMER: a case study Sabah, Malaysia’, Energy Convers. Manage., 2017, 144, pp. 322–339.
  123. 123)
       • 109. Lauret, P., Voyant, C., Soubdhan, T., et al: ‘A benchmarking of machine learning techniques for solar radiation forecasting in an insular context’, Sol. Energy, 2015, 112, pp. 446–457.
  124. 124)
       • 137. Raza, M.Q., Nadarajah, M., Ekanayake, C.: ‘An improved neural ensemble framework for accurate PV output power forecast’. Australasian Universities Power Engineering Conf. (AUPEC), Brisbane, Australia, 2016, pp. 1–6.
  125. 125)
       • 58. Kostylev, V., Pavlovski, A.: ‘Solar power forecasting performance–towards industry standards’. 1st Int. Workshop on the Integration of Solar Power into Power Systems, Aarhus, Denmark, 2011.
  126. 126)
       • 121. Wang, H., yi, H., Ping, J., et al: ‘Deterministic and probabilistic forecasting of photovoltaic power based on deep convolutional neural network’, Energy Convers. Manage., 2017, 153, pp. 409–422.
  127. 127)
       • 77. Bou-Rabee, M., Sulaiman, S.A., Saleh, M.S., et al: ‘Using artificial neural networks to estimate solar radiation in Kuwait’, Renew. Sustain. Energy Rev., 2017, 72, pp. 434–438.
  128. 128)
       • 5. Memon, M.A., Mekhilef, S., Mubin, M., et al: ‘Selective harmonic elimination in inverters using bio-inspired intelligent algorithms for renewable energy conversion applications: a review’, Renew. Sustain. Energy Rev., 2017, 82, pp. 2235–2253.
  129. 129)
       • 74. Ghofrani, M., Ghayekhloo, M., Azimi, R.: ‘A novel soft computing framework for solar radiation forecasting’, Appl. Soft Comput., 2016, 48, pp. 207–216.
  130. 130)
       • 93. Tang, P., Chen, D., Hou, Y.: ‘Entropy method combined with extreme learning machine method for the short-term photovoltaic power generation forecasting’, Chaos, Solitons Fractals, 2016, 89, pp. 243–248.
  131. 131)
       • 70. Ahmad, M.W., Mourshed, M., Rezgui, Y.: ‘Tree-based ensemble methods for predicting PV power generation and their comparison with support vector regression’, Energy, 2018, 164, pp. 465–474.
  132. 132)
       • 1. Adaramola, M.S., Paul, S.S., Oyewola, O.M.: ‘Assessment of decentralized hybrid PV solar-diesel power system for applications in Northern part of Nigeria’, Energy. Sustain. Dev., 2014, 19, pp. 72–82.
  133. 133)
       • 4. Mellit, A., Kalogirou, S.A., Hontoria, L., et al: ‘Artificial intelligence techniques for sizing photovoltaic systems: a review’, Renew. Sustain. Energy Rev., 2009, 13, (2), pp. 406–419.
  134. 134)
       • 102. Chupong, C., Plangklang, B.: ‘Forecasting power output of PV grid connected system in Thailand without using solar radiation measurement’, Energy Procedia, 2011, 9, pp. 230–237.
  135. 135)
       • 143. Malki, H.A., Karayiannis, N.B., Balasubramanian, M.: ‘Short-term electric power load forecasting using feedforward neural networks’, Expert Syst., 2004, 21, (3), pp. 157–167.
  136. 136)
       • 91. Jang, H.S., Bae, K.Y., Park, H.-S., et al: ‘Solar power prediction based on satellite images and support vector machine’, IEEE Trans. Sustain. Energy, 2016, 7, (3), pp. 1255–1263.
  137. 137)
       • 50. David, M., Ramahatana, F., Trombe, P.-J., et al :‘Probabilistic forecasting of the solar irradiance with recursive ARMA and GARCH models’, Sol. Energy, 2016, 133, pp. 55–72.
  138. 138)
       • 101. Wu, J., Chan, C.K.: ‘Prediction of hourly solar radiation with multi-model framework’, Energy Convers. Manage., 2013, 76, pp. 347–355.
  139. 139)
       • 112. Wu, Y.-K., Chen, C.-R., Abdul Rahman, H.: ‘A novel hybrid model for short-term forecasting in PV power generation’, Int. J. Photoenergy, 2014, 2014, Article ID: 569249.
  140. 140)
       • 116. Yang, H.-T., Huang, C.-M., Huang, Y.-C., et al: ‘A weather-based hybrid method for 1-day ahead hourly forecasting of PV power output’, IEEE Trans. Sustain. Energy, 2014, 5, (3), pp. 917–926.
  141. 141)
       • 145. Ding, M., Wang, L., Bi, R.: ‘An ANN-based approach for forecasting the power output of photovoltaic system’, Proc. Environ. Sci., 2011, 11, pp. 1308–1315.
  142. 142)
       • 120. Mori, H., Takahashi, M.: ‘Development of GRBFN with global structure for PV generation output forecasting’. Power and Energy Society General Meeting, San Diego, CA, USA, 2012, pp. 1–7.
  143. 143)
       • 72. Ibrahim, I.A., Khatib, T.: ‘A novel hybrid model for hourly global solar radiation prediction using random forests technique and firefly algorithm’, Energy Convers. Manage., 2017, 138, pp. 413–425.
  144. 144)
       • 89. Gorunescu, F.: ‘Data mining: concepts, models and techniques’ (Springer Science & Business Media, India, 2011).
  145. 145)
       • 30. Mahmoud, T., Dong, Z., Ma, J.: ‘An advanced approach for optimal wind power generation prediction intervals by using self-adaptive evolutionary extreme learning machine’, Renew. Energy, 2018, 126, pp. 254–269.
  146. 146)
       • 41. Voyant, C., Muselli, M., Paoli, C., et al: ‘Numerical weather prediction (NWP) and hybrid ARMA/ANN model to predict global radiation’, Energy, 2012, 39, (1), pp. 341–355.
  147. 147)
       • 132. Cherkassky, V., Ma, Y.: ‘Practical selection of SVM parameters and noise estimation for SVM regression’, Neural Netw., 2004, 17, (1), pp. 113–126.