Review on probabilistic forecasting of photovoltaic power production and electricity consumption

Renewable and Sustainable Energy Reviews

Volumn 81, Issue , 2018, Pages 1484-1512

  van der Meer, D W ^a   Widen J ^a   Munkhammar, J ^a  

^a UPPSALA UNIVERSITY   (Sweden)

Author keywords

Electricity consumption; Irradiance; Photovoltaic; Prediction interval; Probabilistic forecasting; Solar radiation

Indexed keywords

ELECTRIC POWER GENERATION; ELECTRIC POWER UTILIZATION; FORECASTING; SOLAR POWER GENERATION; STOCHASTIC SYSTEMS;

CASE-STUDIES; DATA SET; ELECTRICITY-CONSUMPTION; IRRADIANCE; LOAD FORECASTING; PHOTOVOLTAIC POWER; PHOTOVOLTAICS; POWER PRODUCTION; PREDICTION INTERVAL; PROBABILISTIC FORECASTING;

SOLAR ENERGY;

EID: 85020268535     PISSN: 13640321     EISSN: 18790690     Source Type: Journal    
DOI: 10.1016/j.rser.2017.05.212     Document Type: Review

References (140)

1. NOAA National Centers for Environmental Information, State of the Climate: Global Analysis for June 2016. URL 〈 http://www.ncdc.noaa.gov/sotc/global/201606〉; 2016.
2. NASA/GISS, Global Land-Ocean Temperature Index. URL 〈 http://data.giss.nasa.gov/gistemp/tabledata_v3/GLB.Ts+dSST.txt〉; 2016.
3. UNFCCC, Adoption of the Paris Agreement., Tech. Rep. December. www.FCCC.CP/2015/L9/Rev1. URL 〈 http://www.unfccc.int/resource/docs/2015/cop21/eng/l09r01.pdf〉; 2015.
4. International Energy Agency, Snapshot of global photovoltaic markets, Technical Report; 2016.
5. International Renewable Energy Agency, Renewable Power Generation Costs in 2014, Technical Report January; 2015.
6. Coimbra CF, Kleissl J, Marquez R, Chapter 8 - Overview of Solar-Forecasting Methods and a Metric for Accuracy Evaluation. In: Kleissl J, editor. Solar energy forecasting and resource assessment. Boston: Acadamic Press; 2013. p. 171–194.
7. Fraunhofer Institute for Solar Energy Systems ISE, Recent Facts about Photovoltaics in Germany, Technical Report, 2016.
8. Lorenz, E., Hurka, J., Heinemann, D., Beyer, H.G., Irradiance forecasting for the power prediction of grid-connected photovoltaic systems. IEEE J Sel Top Appl Earth Obs Remote Sens 2:1 (2009), 2–10.
9. Alessandrini, S., Delle Monache, L., Sperati, S., Cervone, G., An analog ensemble for short-term probabilistic solar power forecast. Appl Energy 157 (2015), 95–110, 10.1016/j.apenergy.2015.08.011.
10. Wang XWX, Gao JGJ, Hu WHW, Shi ZSZ, Tang BTB, Research of effect on distribution network with penetration of photovoltaic system. In: Univ. Power Eng. Conference (UPEC), 2010 45th International, 2010, pp. 1–4.
11. Aguero JR, Steffel SJ, Integration challenges of photovoltaic distributed generation on power distribution systems. In: 2011 IEEE Power Energy Soc. Gen. Meet., 2011, p. 1–6. http://dx.doi.org/10.1109/PES.2011.6039097.
12. Ropp M, Newmiller J, Whitaker C, Norris B, Review of potential problems and utility concerns arising from high penetration levels of photovoltaics in distribution systems. In: IEEE Photovolt. Spec. Conference, 2008. http://dx.doi.org/10.1109/PVSC.2008.4922861.
13. Hong, T., Pinson, P., Fan, S., Zareipour, H., Troccoli, A., Hyndman, R.J., Probabilistic energy forecasting: global energy forecasting competition 2014 and beyond. Int J Forecast 32:3 (2016), 896–913, 10.1016/j.ijforecast.2016.02.001.
14. Raza, Muhammad Qamar, Nadarajah, Mithulananthan, Ekanayake, Chandima, On recent advances in PV output power forecast. Sol Energy 136:136 (2016), 125–144, 10.1016/j.solener.2016.06.073.
15. Inman, R.H., Pedro, H.T., Coimbra, C.F., Solar forecasting methods for renewable energy integration. Prog Energy Combust Sci 39:6 (2013), 535–576, 10.1016/j.pecs.2013.06.002.
16. Diagne, M., David, M., Lauret, P., Boland, J., Schmutz, N., Review of solar irradiance forecasting methods and a proposition for small-scale insular grids. Renew Sustain Energy Rev 27 (2013), 65–76, 10.1016/j.rser.2013.06.042.
17. International Energy Agency (IEA), Photovoltaic and solar forecasting: State of the Art.
18. Widén, J., Carpman, N., Castellucci, V., Lingfors, D., Olauson, J., Remouit, F., et al. Variability assessment and forecasting of renewables: a review for solar, wind, wave and tidal resources. Renew Sustain Energy Rev 44 (2015), 356–375, 10.1016/j.rser.2014.12.019.
19. Ren, Y., Suganthan, P., Srikanth, N., Ensemble methods for wind and solar power forecasting – a state-of-the-art review. Renew Sustain Energy Rev 50 (2015), 82–91, 10.1016/j.rser.2015.04.081.
20. Wan, C., Zhao, J., Member, S., Song, Y., Photovoltaic and solar power forecasting for smart grid energy management. J Power Energy Syst 1:4 (2015), 38–46, 10.17775/CSEEJPES.2015.00046.
21. Antonanzas, J., Osorio, N., Escobar, R., Urraca, R., Martinez-de pison, F.J., Antonanzas-torres, F., Review of photovoltaic power forecasting. Sol Energy 136 (2016), 78–111, 10.1016/j.solener.2016.06.069.
22. Wang, L., Kisi, O., Zounemat-Kermani, M., Salazar, G.A., Zhu, Z., Gong, W., Solar radiation prediction using different techniques: model evaluation and comparison. Renew Sustain Energy Rev 61 (2016), 384–397, 10.1016/j.rser.2016.04.024.
23. Voyant, C., Notton, G., Kalogirou, S., Nivet, M.-L., Paoli, C., Motte, F., et al. Machine Learning methods for solar radiation forecasting: a review, renew. Energy 105 (2017), 569–582, 10.1016/j.renene.2016.12.095.
24. Kleissl, J., Solar energy forecasting and resource assessment. 2013, Academic Press, Boston.
25. Hong, T., Energy forecasting: past, present and future. Foresight Int J Forecast(32), 2014, 43–49.
26. Hong, T., Fan, S., Probabilistic electric load forecasting: a tutorial review. Int J Forecast 32:3 (2016), 914–938, 10.1016/j.ijforecast.2015.11.011.
27. Aman S, Frincu M, Chelmis C, Noor M, Simmhan Y, Prasanna VK, Prediction models for dynamic demand response: Requirements, challenges, and insights. In: 2015 IEEE International Conference Smart Grid Commun., 2015, p. 338–343. http://dx.doi.org/10.1109/SmartGridComm.2015.7436323.
28. Hong T, Short Term Electric Load Forecasting, [Ph.D. thesis], North Carolina State University arXiv:1011.1669v3, http://dx.doi.org/10.1017/CBO9781107415324.004; 2010.
29. Singh AK, Ibraheem, Khatoon S, Muazzam M, Chaturvedi DK, Load Forecasting techniques and methodologies: a review. In: International Conference Power, Control Embed. Syst., 2012.
30. Takiyar S, Singh V, Trend analysis and evolution of Short Term Load Forecasting Techniques. In: 2015 Proceedings of the 4th International Conference Reliab. Infocom Technol. Optim. (Trends Futur. Dir., IEEE, 2015, pp. 1–6. http://dx.doi.org/10.1109/ICRITO.2015.7359233.
31. Raza, M.Q., Khosravi, A., A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings. Renew Sustain Energy Rev 50 (2015), 1352–1372, 10.1016/j.rser.2015.04.065.
32. EPBD, On the energy performance of buildings, Tech. rep., EPBD (2010).
33. Foucquier, A., Robert, S., Suard, F., Stéphan, L., Jay, A., State of the art in building modelling and energy performances prediction: a review. Renew Sustain Energy Rev 23 (2013), 272–288, 10.1016/j.rser.2013.03.004.
34. Fumo, N., A review on the basics of building energy estimation. Renew Sustain Energy Rev 31 (2014), 53–60, 10.1016/j.rser.2013.11.040.
35. Wang Z, Srinivasan RS, A review of artificial intelligence based building energy prediction with a focus on ensemble prediction models. In: 2015 Winter Simulation Conference, IEEE, 2015, p. 3438–3448. http://dx.doi.org/10.1109/WSC.2015.7408504.
36. Yildiz B, Bilbao J, Sproul A, A review and analysis of regression and machine learning models on commercial building electricity load forecasting, Renew Sustain Energy Rev. 73, December 2016, p. 1104–1122. http://dx.doi.org/10.1016/j.rser.2017.02.023; 2017.
37. Lorenz E, Hurka J, Karampela G, Heinemann D, Beyer HG, Schneider M, Qualified forecast of ensemble power production by spatially dispersed grid-connected PV systems. In: 23rd European Photovoltatic Solar Energy Conference Exhib. Val. Spain, 1–5 Sept. arXiv:1011.1669v3, http://dx.doi.org/10.1017/CBO9781107415324.004; 2008.
38. Widén, J., Lundh, M., Vassileva, I., Dahlquist, E., Ellegård, K., Wäckelgård, E., Constructing load profiles for household electricity and hot water from time-use data-Modelling approach and validation. Energy Build 41:7 (2009), 753–768, 10.1016/j.enbuild.2009.02.013.
39. Widen, J., Wackelgard, E., A high-resolution stochastic model of domestic activity patterns and electricity demand. Appl Energy 87:6 (2010), 1880–1892, 10.1016/j.apenergy.2009.11.006.
40. Munkhammar, J., Bishop, J.D.K., Sarralde, J.J., Tian, W., Choudhary, R., Household electricity use, electric vehicle home-charging and distributed photovoltaic power production in the city of Westminster. Energy Build 86 (2015), 439–448, 10.1016/j.enbuild.2014.10.006.
41. Lazos, D., Sproul, A.B., Kay, M., Optimisation of energy management in commercial buildings with weather forecasting inputs: a review. Renew Sustain Energy Rev 39 (2014), 587–603, 10.1016/j.rser.2014.07.053.
42. Dong, B., Li, Z., Rahman, S.M., Vega, R., A hybrid model approach for forecasting future residential electricity consumption. Energy Build 117 (2016), 341–351, 10.1016/j.enbuild.2015.09.033.
43. Hodge Bm, Hummon M, Orwig K, Solar Ramping Distributions over Multiple Timescales and Weather Patterns. In: 10th International Work. Large-Scale Integr. Wind Power into Power Syst., 2011.
44. Golestaneh, F., Pinson, P., Gooi, H.B., Very short-term nonparametric probabilistic forecasting of renewable forecast renew energy generation; with application to solar energy. Power Syst. IEEE Trans. PP 99 (2016), 1–14, 10.1109/TPWRS.2015.2502423.
45. Ineichen, P., Comparison of eight clear sky broadband models against 16 independent data banks. Sol Energy 80:4 (2006), 468–478, 10.1016/j.solener.2005.04.018.
46. Pinson, P., Hagedorn, R., Verification of the ECMWF ensemble forecasts of wind speed against analyses and observations. Meteorol Appl 19:4 (2012), 484–500, 10.1002/met.283.
47. Perez R, Kankiewicz A, Schlemmer J, Hemker K, Kivalov S, A new operational solar resource forecast model service for PV fleet simulation. In: 2014 IEEE 40th Photovolt. Spec. Conference, IEEE, 2014, pp. 0069–0074. http://dx.doi.org/10.1109/PVSC.2014.6925204.
48. Taylor, J.W., Short-term double electricity demand. J Oper Res Soc 54:8 (2003), 799–805, 10.1057/palgrave.jors.2601589.
49. Liu B, Nowotarski J, Hong T, Weron R, Probabilistic Load Forecasting via Quantile Regression Averaging on Sister Forecasts, IEEE Trans. Smart Grid.
50. Box GEP, Jenkins GM, Reinsel GC, Time series analysis: forecasting and control, Englewood Cliffs, N.J.:, Englewood Cliffs, N.J.; 1994.
51. Chatfield C, Time-Series Forecasting, Chapman & Hall / CRC, Bath, 2000.
52. Zamo, M., Mestre, O., Arbogast, P., Pannekoucke, O., A benchmark of statistical regression methods for short-term forecasting of photovoltaic electricity production. Part II: probabilistic forecast of daily production. Sol Energy 105 (2014), 804–816, 10.1016/j.solener.2014.03.026.
53. Arora, S., Taylor, J.W., Forecasting electricity smart meter data using conditional kernel density estimation. Omega 59 (2016), 1–13, 10.1016/j.omega.2014.08.008.
54. Quan, H., Srinivasan, D., Khosravi, A., Uncertainty handling using neural network-based prediction intervals for electrical load forecasting. Energy 73 (2014), 916–925, 10.1016/j.energy.2014.06.104.
55. Hyndman, R.J., Koehler, A.B., Another look at measures of forecast accuracy. Int J Forecast 22:4 (2006), 679–688, 10.1016/j.ijforecast.2006.03.001.
56. Zhang, J., Florita, A., Hodge, B.-M., Lu, S., Hamann, H.F., Banunarayanan, V., et al. A suite of metrics for assessing the performance of solar power forecasting. Sol Energy 111 (2015), 157–175, 10.1016/j.solener.2014.10.016.
57. Gneiting, T., Katzfuss, M., Probabilistic forecasting. Annu Rev Stat Appl 1 (2014), 125–151, 10.1146/annurev-statistics-062713-085831.
58. Zhang, Y., Wang, J., Wang, X., Review on probabilistic forecasting of wind power generation. Renew Sustain Energy Rev 32 (2014), 255–270, 10.1016/j.rser.2014.01.033.
59. Hoff, T.E., Perez, R., Kleissl, J., Renne, D., Stein, J., Reporting of irradiance modeling relative prediction errors. Prog. Photovolt. Res. Appl. 21 (2012), 1514–1519 http://dx.doi.org/10.1002/pip.2225.
60. Perez R, Hoff TE, Chapter 6 - Solar Resource Variability. In: Kleissl J, editor. Solar energy forecasting and resource assessment. Boston: Acadamic Press; 2013. p. 133 - 148.
61. Espinar, B., Ramírez, L., Drews, A., Beyer, H.G., Zarzalejo, L.F., Polo, J., et al. Analysis of different comparison parameters applied to solar radiation data from satellite and German radiometric stations. Sol Energy 83:1 (2009), 118–125, 10.1016/j.solener.2008.07.009.
62. Doane, D.P., Seward, L.E., Measuring skewness: a forgotten statistic?. J Stat Educ 19:2 (2011), 1–18 [doi:10.1136.25312].
63. Pinson, P., Nielsen, H.A., M??ller, J.K., H. Madsen, H., Kariniotakis, G.N., Non-parametric probabilistic forecasts of wind power: required properties and evaluation. Wind Energy 10:6 (2007), 497–516, 10.1002/we.230.
64. Quan, H., Srinivasan, D., Khosravi, A., Short-term load and wind power forecasting using neural network-based prediction intervals. IEEE Trans Neural Netw Learn Syst 25:2 (2014), 303–315, 10.1109/TNNLS.2013.2276053.
65. Khosravi, A., Nahavandi, S., Creighton, D., Prediction intervals for short-term wind farm power generation forecasts. IEEE Trans Sustain Energy 4:3 (2013), 602–610, 10.1109/TSTE.2012.2232944.
66. Khosravi, A., Nahavandi, S., Creighton, D., Construction of optimal prediction intervals for load forecasting problems. IEEE Trans Power Syst 25:3 (2010), 1496–1503, 10.1109/TPWRS.2010.2042309.
67. Meinshausen, N., Quantile regression forests. J Mach Learn Res 7 (2006), 983–999, 10.1111/j.1541-0420.2010.01521.x.
68. Xie, J., Hong, T., Laing, T., Kang, C., On normality assumption in residual simulation for probabilistic load forecasting. IEEE Trans Smart Grid, 2015, 1–8, 10.1109/TSG.2015.2447007.
69. Winkler, R.L., A decision-theoretic approach to interval estimation. J Am Stat Assoc 67:337 (1972), 187–191, 10.2307/2284720.
70. David, M., Ramahatana, F., Trombe, P.J., Lauret, P., Probabilistic forecasting of the solar irradiance with recursive ARMA and GARCH models. Sol Energy 133 (2016), 55–72, 10.1016/j.solener.2016.03.064.
71. Koenker, R., Bassett, G., Regression quantiles. Econometrica 46:1 (1978), 33–50.
72. Taieb, S.B., Huser, R., Hyndman, R.J., Genton, M.G., Forecasting uncertainty in electricity smart meter data by boosting additive quantile regression. IEEE Trans Smart Grid 7:5 (2016), 2448–2455, 10.1109/TSG.2016.2527820.
73. Koenker, R., Hallock, K., Quantile regression. J Econ Perspect 15:4 (2001), 143–156, 10.1017/CBO9780511754098 [arXiv:1011.1669v3].
74. Breiman, L., Random forests. Mach Learn 45:1 (2001), 5–32, 10.1023/A:1010933404324.
75. Rasmussen, C.E., Williams, C.K.I., Gaussian processes for machine learning. 2006, MIT Press.
76. Roberts, S., Osborne, M., Ebden, M., Reece, S., Gibson, N., Aigrain, S., Gaussian processes for time-series modelling. Philos Trans R Soc Lond A Math Phys Eng Sci, 371, 1984.
77. Girard, A., Rasmussen, C.E., Candela, J.Q., Murray-Smith, R., Gaussian process priors with uncertain inputs-application to multiple-step ahead time series forecasting. Adv Neural Inf Process Syst, 2003, 545–552.
78. Efron, B., Bootstrap methods: another look at the jackknife. Ann Stat 7:1 (1979), 1–26, 10.1214/aos/1176348654 [arXiv:1306.3979v1].
79. Khosravi, A., Nahavandi, S., Creighton, D., Atiya, A.F., Lower upper bound estimation method for construction of neural network-based prediction intervals. IEEE Trans Neural Netw 22:3 (2011), 337–346, 10.1109/TNN.2010.2096824.
80. Friedman, J.H., Greedy function approximation: a gradient boosting machine. Statistics 29:5 (2001), 1189–1232, 10.1214/aos/1013203451.
81. Bühlmann, P., Boosting for high-dimensional linear models. Ann Stat 34:2 (2006), 559–583, 10.1214/009053606000000092 [arXiv:0606789].
82. Rosenblatt, M., Remarks on some nonparametric estimates of a density function. Ann Math Stat 27:3 (1956), 832–837, 10.1214/aoms/1177728190.
83. Parzen, E., On estimation of a probability density function and mode. Ann Math Stat 33:3 (1962), 1065–1076, 10.1214/aoms/1177704472.
84. Sheather SJ, Jones MC, A reliable data based bandwidth selection method for kernel density estimation. http://dx.doi.org/10.2307/2345597; 1991.
85. Altman, N.S., An introduction to kernel and nearest-neighbor nonparametric regression. Am Stat 46:3 (1992), 175–185, 10.1080/00031305.1992.10475879.
86. Delle Monache, L., Eckel, F.A., Rife, D.L., Nagarajan, B., Searight, K., Probabilistic weather prediction with an analog ensemble. Mon Weather Rev 141 (2013), 3498–3516, 10.1175/MWR-D-12-00281.1.
87. da Silva Fonseca, J.G., Oozeki, T., Ohtake, H., Takashima, T., Kazuhiko, O., On the use of maximum likelihood and input data similarity to obtain prediction intervals for forecasts of photovoltaic power generation. J Electr Eng Technol 10:3 (2015), 1342–1348, 10.5370/JEET.2015.10.3.1342.
88. Chu, Y., Li, M., Pedro, H.T.C., Coimbra, C.F.M., Real-time prediction intervals for intra-hour DNI forecasts. Renew Energy 83 (2015), 234–244, 10.1016/j.renene.2015.04.022.
89. Sperati, S., Alessandrini, S., Delle, L., An application of the ECMWF ensemble prediction system for short-term solar power forecasting. Sol Energy 133 (2016), 437–450, 10.1016/j.solener.2016.04.016.
90. Torregrossa, D., Boudec, J.L., Paolone, M., Model-free computation of ultra-short-term prediction intervals of solar irradiance. Sol Energy 124 (2016), 57–67, 10.1016/j.solener.2015.11.017.
91. Bacher, P., Madsen, H., Nielsen, H.A., Online short-term solar power forecasting. Sol Energy 83:10 (2009), 1772–1783, 10.1016/j.solener.2009.05.016.
92. Scolari E, Torregrossa D, Boudec JL, Paolone M, Ultra-Short-Term Prediction Intervals of Photovoltaic AC Active Power. In: International Conference Probabilistic Methods Application to Power System. PMAPS 2016, 2016. http://dx.doi.org/10.1109/PMAPS.2016.7764064.
93. Scolari, E., Sossan, F., Paolone, M., Irradiance prediction intervals for PV stochastic generation in microgrid applications. Sol Energy 139 (2016), 116–129, 10.1016/j.solener.2016.09.030.
94. Wan, C., Lin, J., Song, Y., Xu, Z., Yang, G., Probabilistic forecasting of photovoltaic generation: an efficient statistical approach. IEEE Trans Power Syst, 8950(8), 2016, 1, 10.1109/TPWRS.2016.2608740.
95. Chai, S., Xu, Z., Wong, W.K., Optimal granule-based pis construction for solar irradiance forecast. IEEE Trans Power Syst 31:4 (2016), 3332–3333, 10.1109/TPWRS.2015.2473097.
96. Boland, J., Spatial-temporal forecasting of solar radiation. Renew Energy 75 (2015), 607–616, 10.1016/j.renene.2014.10.035.
97. Wang S, Jia C, Prediction intervals for short-term photovoltaic generation forecasts. In: Proceedings of the 5th International Conference on Instrumentation and Measurement, Computer, Communication and Control IMCCC 2015, 2016, pp. 459–463. http://dx.doi.org/10.1109/IMCCC.2015.103.
98. Chu, Y., Coimbra, C.F.M., Short-term probabilistic forecasts for direct normal irradiance. Renew Energy 101 (2017), 526–536, 10.1016/j.renene.2016.09.012.
99. Bracale A, Carpinelli G, Falco PD, A Bayesian-based approach for the short-term forecasting of electrical loads in smart grids. Part II: numerical applications. In: International Symp. Power Electron. Electr. Drives, Autom. Motion, 2016, pp. 129–136.
100. Guan, C., Luh, P.B., Michel, L.D., Chi, Z., Hybrid kalman filters for very short-term load forecasting and prediction interval estimation. IEEE Trans Power Syst 28:4 (2013), 3806–3817.
101. Bracale, A., Caramia, P., Carpinelli, G., di Fazio, A.R., Varilone, P., A bayesian-based approach for a short-term steady-state forecast of a smart grid. IEEE Trans Smart Grid 4:4 (2013), 1760–1771, 10.1109/TSG.2012.2231441.
102. Bilionis, I., Constantinescu, E.M., Anitescu, M., Data-driven model for solar irradiation based on satellite observations. Sol Energy 110 (2014), 22–38, 10.1016/j.solener.2014.09.009.
103. Grantham, A., Gel, Y.R., Boland, J., Nonparametric short-term probabilistic forecasting for solar radiation. Sol Energy 133 (2016), 465–475, 10.1016/j.solener.2016.04.011.
104. Tao L, He J, Wang Y, Zhang P, Zhang H, Wang H, Miao Y, Wang J, Operational risk assessment of distribution network with consideration of PV output uncertainties. In: China International Conference Electr. Distrib., 2016, pp. 10–15.
105. AlHakeem, D., Mandal, P., Ul Haque, A., Yona, A., Senjyu, T., Tseng, T.-L., et al. Strategy to quantify uncertainties of wavelet-grnn-pso based solar pv forecasts using bootstrap confidence intervals. Energy Soc Gen Meet, 2015, 1–5.
106. Bessa, R., Trindade, A., Silva, C.S., Miranda, V., Probabilistic solar power forecasting in smart grids using distributed information. Int J Electr Power Energy Syst 72 (2015), 16–23, 10.1016/j.ijepes.2015.02.006.
107. Tastu, J., Pinson, P., Trombe, P.J., Madsen, H., Probabilistic forecasts of wind power generation accounting for geographically dispersed information. IEEE Trans Smart Grid 5:1 (2014), 480–489, 10.1109/TSG.2013.2277585.
108. Liu, Y., Shimada, S., Yoshino, J., Kobayashi, T., Miwa, Y., Furuta, K., Ensemble forecasting of solar irradiance by applying a mesoscale meteorological model. Sol Energy 136 (2016), 597–605, 10.1016/j.solener.2016.07.043.
109. Nagy, G.I., Barta, G., Kazi, S., Borbély, G., Simon, G., GEFCom2014: probabilistic solar and wind power forecasting using a generalized additive tree ensemble approach. Int J Forecast 32:3 (2016), 1087–1093, 10.1016/j.ijforecast.2015.11.013.
110. Juban, R., Ohlsson, H., Maasoumy, M., Poirier, L., Kolter, J.Z., A multiple quantile regression approach to the wind, solar, and price tracks of GEFCom2014. Int J Forecast 32:3 (2016), 1094–1102, 10.1016/j.ijforecast.2015.12.002.
111. Zhang B, Dehghanian P, Kezunovic M, Spatial-Temporal Solar Power Forecast through Use of Gaussian Conditional Random Fields, in: IEEE PES Gen. Meet., no. 2, 2016, pp. 16–20. http://dx.doi.org/10.1109/PESGM.2016.7741503.
112. Aryaputera A, Verbois H, Walsh W, Probabilistic accumulated irradiance forecast for Singapore using ensemble techniques. In: Conference Rec. IEEE Photovoltatic Special Conference, Vol. 2016-Novem, 2016, pp. 1113–1118. http://dx.doi.org/10.1109/PVSC.2016.7749786.
113. Takeda, H., Short-term ensemble forecast for purchased photovoltaic generation. Sol Energy 149 (2017), 176–187, 10.1016/j.solener.2017.03.088.
114. Almeida V, Gama J, Prediction Intervals for Electric Load Forecast: Evaluation for Different Profiles. In: Proceedings 18th Intelligent System Application to Power System, 2015, pp. 1–6. http://dx.doi.org/10.1109/ISAP.2015.7325539.
115. Golestaneh F, Pinson P, Gooi HB, Generation and Evaluation of Space-Time Trajectories of Photovoltaic Power, Applied Energy arXiv:1603.06649.
116. Almeida, M.P., Perpiñán, O., Narvarte, L., PV power forecast using a nonparametric PV model. Sol Energy 115 (2015), 354–368, 10.1016/j.solener.2015.03.006.
117. le Cadre, H., Aravena, I., Papavasiliou, A., Solar PV power forecasting using extreme learning machanical and information fusion. HAL(April), 2015, 22–24.
118. Yamazaki T, Wakao S, Fujimoto Y, Hayashi Y, Improvement of Prediction Interval Estimation Algorithm with Just-In-Time Modeling for PV System Operation. In: Photovoltatic Special Conference (PVSC), 2015 IEEE 42nd, Vol. 1, 2015, pp. 4–9.
119. Yamazaki, T., Homma, H., Wakao, S., Fujimoto, Y., Hayashi, Y., Estimation prediction interval of solar irradiance based on just-in-time modeling for photovoltaic output prediction. Electr Eng Jpn (Engl Transl Denki Gakkai Ronbunshi) 195:3 (2016), 1–10, 10.1002/eej.22822.
120. 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 32:3 (2016), 1081–1086, 10.1016/j.ijforecast.2015.11.002.
121. Pierro, M., Bucci, F., de Felice, M., Maggioni, E., Moser, D., Perotto, A., et al. Multi-Model Ensemble for day ahead prediction of photovoltaic power generation. Sol Energy 134 (2016), 132–146, 10.1016/j.solener.2016.04.040.
122. Cornaro, C., Pierro, M., Bucci, F., Master optimization process based on neural networks ensemble for 24-h solar irradiance forecast. Sol Energy 111 (2015), 297–312, 10.1016/j.solener.2014.10.036.
123. Bracale A, Carpinelli G, Falco PD, Rizzo R, Russo A, Bracale A, Carpinelli G, Falco PD, Rizzo R, New advanced method and cost-based indices applied to probabilistic forecasting of photovoltaic generation, J Renew Sustain Energy 8 (023505). http://dx.doi.org/10.1063/1.4946798.
124. Davò, F., Alessandrini, S., Sperati, S., Delle Monache, L., Airoldi, D., Vespucci, M.T., Post-processing techniques and principal component analysis for regional wind power and solar irradiance forecasting. Sol Energy 134 (2016), 327–338, 10.1016/j.solener.2016.04.049.
125. Saint-Drenan, Y., Good, G., Braun, M., A probabilistic approach to the estimation of regional photovoltaic power production. Sol Energy 147 (2017), 257–276, 10.1016/j.solener.2017.03.007.
126. Chai Songjian, Niu Ming, Xu Z, Lei Lai Loi, Wong KP, Nonparametric conditional interval forecasts for PV power generation considering the temporal dependence. In: 2016 IEEE Power Energy Soc Gen Meet, IEEE, 2016, pp. 1–5. http://dx.doi.org/10.1109/PESGM.2016.7741953.
127. Barta G, Nagy G, Papp G, Simon G, Forecasting framework for open access time series in energy, 2016 IEEE International Energy Conference ENERGYCON 2016 arXiv:1606.00656 http://dx.doi.org/10.1109/ENERGYCON.2016.7514015.
128. Wijaya TK, Sinn M, Chen B, Forecasting Uncertainty in Electricity Demand. in: AAAI Work Comput Sustain, 2015, pp. 120–126.
129. He, Y., Xu, Q., Wan, J., Yang, S., Short-term power load probability density forecasting based on quantile regression neural network and triangle kernel function. Energy 114 (2016), 498–512, 10.1016/j.energy.2016.08.023.
130. He, Y., Liu, R., Li, H., Wang, S., Lu, X., Short-term power load probability density forecasting method using kernel-based support vector quantile regression and Copula theory. Appl Energy 185 (2017), 254–266, 10.1016/j.apenergy.2016.10.079.
131. Xie J, Hong T, Comparing two model selection frameworks for probabilistic load forecasting. In: 2016 International Conference Probabilistic Methods Applied to Power System Beijing, China, Oct 16-20; 2016. http://dx.doi.org/10.1109/PMAPS.2016.7764081.
132. Gaillard, P., Goude, Y., Nedellec, R., Additive models and robust aggregation for GEFCom2014 probabilistic electric load and electricity price forecasting. Int J Forecast 32:3 (2016), 1038–1050, 10.1016/j.ijforecast.2015.12.001.
133. Xie, J., Hong, T., GEFCom2014 probabilistic electric load forecasting: an integrated solution with forecast combination and residual simulation. Int J Forecast 32:3 (2016), 1012–1016, 10.1016/j.ijforecast.2015.11.005.
134. Mangalova, E., Shesterneva, O., Sequence of nonparametric models for GEFCom2014 probabilistic electric load forecasting. Int J Forecast 32:3 (2016), 1023–1028, 10.1016/j.ijforecast.2015.11.001.
135. Dordonnat, V., Pichavant, A., Pierrot, A., GEFCom2014 probabilistic electric load forecasting using time series and semi-parametric regression models. Int J Forecast 32:3 (2016), 1005–1011, 10.1016/j.ijforecast.2015.11.010.
136. Ziel, F., Liu, B., Lasso estimation for GEFCom2014 probabilistic electric load forecasting. Int J Forecast 32:3 (2016), 1029–1037, 10.1016/j.ijforecast.2016.01.001 [arXiv:1603.01376v1].
137. Haben, S., Giasemidis, G., A hybrid model of kernel density estimation and quantile regression for GEFCom2014 probabilistic load forecasting. Int J Forecast 32:3 (2016), 1017–1022, 10.1016/j.ijforecast.2015.11.004.
138. Takeda, H., Tamura, Y., Sato, S., Using the ensemble Kalman filter for electricity load forecasting and analysis. Energy 104 (2016), 184–198, 10.1016/j.energy.2016.03.070.
139. Lorenz E, Heinemann D, Wickramarathne H, Beyer HG, Bofinger S, Forecast of ensemble power production by grid-connected PV systems. In: 20th European PV Conference, 2007.
140. Kou, P., Gao, F., A sparse heteroscedastic model for the probabilistic load forecasting in energy-intensive enterprises. Int J Electr Power Energy Syst 55 (2014), 144–154, 10.1016/j.ijepes.2013.09.002.