[1] IRENA (2019). Global energy transformation: A roadmap to 2050 (2019 edition).
[2] IRENA (2022). Renewable Power Generation Costs in 2021.
[3] IEA (2022). World Energy Investment 2022.
[4] Tabar, M.R.R. et al. (2014). Kolmogorov spectrum of renewable wind and solar power fluctuations. The European Physical Journal Special Topics. Vol. 223, No. 12, pp. 2637–2644.
[5] Mossoba, J. et al. (2012). Analysis of solar irradiance intermittency mitigation using constant DC voltage PV and EV battery storage. 2012 IEEE Transportation Electrification Conference and Expo (ITEC), 2012.
[6] Anvari, M. et al. (2016). Short term fluctuations of wind and solar power systems. New Journal of Physics. Vol. 18, No. 6, p. 063027.
[7] Traube, J. et al. (2013). Mitigation of Solar Irradiance Intermittency in Photovoltaic Power Systems With Integrated Electric-Vehicle Charging Functionality. IEEE Transactions on Power Electronics. vol. 28, no. 6, pp. 3058–3067.
[8] COES (2014). Technical Procedure PR-01-Short-Term Ooperation programming.
[9] COES (2016). Technical Procedure PR-37- Medium-term operation programming.
[10] Wolak, F.A. (2021). Long-Term Resource Adequacy in Wholesale Electricity Markets with Significant Intermittent Renewables. Cambridge, MA.
[11] Dyson, J. et al. (2017). Utility scale solar short term generation forecasting for improved dispatch and system security. 16th Wind Integration Forum, Berlin, 2017.
[12] Dong, J. et al. (2020). Novel stochastic methods to predict short-term solar radiation and photovoltaic power. Renewable Energy. Vol. 145, pp. 333–346.
[13] Heydari, A. et al. (2019). A novel composite neural network based method for wind and solar power forecasting in microgrids. Applied Energy. Vol. 251, p. 113353.
[14] Paulescu, M. and Paulescu, E. (2019). Short-term forecasting of solar irradiance. Renewable Energy. Vol. 143, pp. 985–994.
[15] Caldas, M. and Alonso-Suárez, R. (2019). Very short-term solar irradiance forecast using all-sky imaging and real-time irradiance measurements. Renewable Energy. vol. 143, pp. 1643–1658.
[16] Brancucci Martinez-Anido, C., et al. (2016). The value of day-ahead solar power forecasting improvement. Solar Energy. Vol. 129, pp. 192–203.
[17] Behera, M.K., Majumder, I., and Nayak, N. (2018). Solar photovoltaic power forecasting using optimized modified extreme learning machine technique. Engineering Science and Technology, an International Journal. Vol. 21, No. 3, pp. 428–438.
[18] Fentis, A. et al. (2019). Short-term nonlinear autoregressive photovoltaic power forecasting using statistical learning approaches and in-situ observations. International Journal of Energy and Environmental Engineering. Vol. 10, No. 2, pp. 189–206.
[19] Bylling, H.C., Pineda, S., and Boomsma, T.K. (2020). The impact of short-term variability and uncertainty on long-term power planning. Annals of Operations Research. Vol. 284, No. 1, pp. 199–223.
[20] Feng, Y. (2014). Scenario generation and reduction for long-term and short-term power system generation planning under uncertainties. Iowa State University.
[21] Tabrizian, S. (2019). Technological innovation to achieve sustainable development - Renewable energy technologies diffusion in developing countries. Sustainable Development. Vol. 27, No. 3, pp. 537–544.
[22] Sen, S. and Ganguly, S. (2017). Opportunities, barriers and issues with renewable energy development – A discussion. Renewable and Sustainable Energy Reviews. Vol. 69, pp. 1170–1181.
[23] Balouktsis, A. and Tsalides, P. (1986). Stochastic simulation model of hourly total solar radiation. Solar Energy. Vol. 37, No. 2, pp. 119–126.
[24] Graham, V.A., Hollands, K.G.T., and Unny, T.E. (1988). A time series model for Kt with application to global synthetic weather generation. Solar Energy.
[25] Graham, V.A. and Hollands, K.G.T. (1990). A method to generate synthetic hourly solar radiation globally. Solar Energy.
[26] Hontoria, L., Aguilera, J., and Zufiria, P. (2002). Generation of hourly irradiation synthetic series using the neural network multilayer perceptron. Solar Energy.
[27] Mellit, A. (2008). Artificial Intelligence technique for modelling and forecasting of solar radiation data: a review. International Journal of Artificial Intelligence and Soft Computing.
[28] Celik, A.N. (2003). Long-term energy output estimation for photovoltaic energy systems using synthetic solar irradiation data. Energy.
[29] Polo, J. et al. (2011). A simple approach to the synthetic generation of solar irradiance time series with high temporal resolution. Solar Energy.
[30] Laslett, D., Creagh, C., and Jennings, P. (2014). A method for generating synthetic hourly solar radiation data for any location in the south west of Western Australia, in a world wide web page. Renewable Energy.
[31] Ngoko, B.O., Sugihara, H., and Funaki, T. (2014). Synthetic generation of high temporal resolution solar radiation data using Markov models. Solar Energy. Vol. 103, pp. 160–170.
[32] Grantham, A.P. et al. (2017). Generating synthetic five-minute solar irradiance values from hourly observations. Solar Energy. Vol. 147, pp. 209–221.
[33] Grantham, A.P., Pudney, P.J., and Boland, J.W. (2018). Generating synthetic sequences of global horizontal irradiation. Solar Energy. Vol. 162, No. November 2017, pp. 500–509.
[34] Larrañeta, M. et al. (2018). Methodology to synthetically downscale DNI time series from 1-h to 1-min temporal resolution with geographic flexibility. Solar Energy. Vol. 162, No. October 2017, pp. 573–584.
[35] Zhang, W. et al. (2018). A stochastic downscaling approach for generating high-frequency solar irradiance scenarios. Solar Energy. Vol. 176, pp. 370–379.
[36] Frimane, Â. et al. (2019). Non-parametric Bayesian-based recognition of solar irradiance conditions: Application to the generation of high temporal resolution synthetic solar irradiance data. Solar Energy. Vol. 182, pp. 462–479.
[37] Wang, S.-Y., Qiu, J., and Li, F.-F. (2018). Hybrid Decomposition-Reconfiguration Models for Long-Term Solar Radiation Prediction Only using Historical Radiation Records. Energies. Vol. 11, No. 6, p. 1376.
[38] Nam, K., Hwangbo, S., and Yoo, C. (2020). A deep learning-based forecasting model for renewable energy scenarios to guide sustainable energy policy: A case study of Korea. Renewable and Sustainable Energy Reviews. Vol. 122, p. 109725.
[39] MEM Peru (2003). Peruvian Solar Energy Atlas, Available: http://dger.minem.gob.pe/atlassolar/.
[40] NASA (2022). NASA POWER - Prediction of Worldwide Energy Resources, Available: https://power.larc.nasa.gov/.
[41] JRC (2022). JRC Photovoltaic Geographical Information System (PVGIS)-European Commission, Available: https://re.jrc.ec.europa.eu/pvg_tools/en/.
[42] Numerical Technologies (2022). Ntrand, Available: http://www.ntrand.com/.
[43] Duffie, J.A. and Beckman, W.A. (2013). Solar Engineering of Thermal Processes: Fourth Edition.
[44] Shukla, K.N., Rangnekar, S., and Sudhakar, K. (2015). Comparative study of isotropic and anisotropic sky models to estimate solar radiation incident on tilted surface: A case study for Bhopal, India. Energy Reports.
[45] Prieto, J.I., Martínez-García, J.C., and García, D. (2009). Correlation between global solar irradiation and air temperature in Asturias, Spain. Solar Energy. Vol. 83, No. 7, pp. 1076–1085.
[46] HOMER (2022). HOMER Pro User Manual, Available: https://homerenergy.com/products/pro/docs/latest/.
[47] Shiva Kumar, B. and Sudhakar, K. (2015). Performance evaluation of 10 MW grid connected solar photovoltaic power plant in India. Energy Reports. Vol. 1, pp. 184–192.
[48] Gostein, M., Caron, J.R., and Littmann, B. (2014). Measuring soiling losses at utility-scale PV power plants. 2014 IEEE 40th Photovoltaic Specialist Conference (PVSC), 2014.
[49] Zorrilla-Casanova, J. et al. (2011). Analysis of dust losses in photovoltaic modules.
[50] Micheli, L., Deceglie, M.G., and Muller, M. (2018). Mapping Photovoltaic Soiling Using Spatial Interpolation Techniques. IEEE Journal of Photovoltaics. pp. 1–6.
[51] Deceglie, M.G., Micheli, L., and Muller, M. (2018). Quantifying Soiling Loss Directly From PV Yield. IEEE Journal of Photovoltaics. Vol. 8, No. 2, pp. 547–551.
[52] Caron, J.R. and Littmann, B. (2012). Direct monitoring of energy lost due to soiling on first solar modules in California. 2012 IEEE 38th Photovoltaic Specialists Conference (PVSC) PART 2, 2012.
[53] IFC (2015). Utility-Scale Solar Photovoltaic Power Plants: A project Developer’s Guide. Washington, D.C.
[54] Dobos, A.P. (2014). PVWatts Version 5 Manual.
[55] ME Chile (2017). Chilean Solar Explorer, Available: https://solar.minenergia.cl/exploracion.
[56] WMO (2022). World Radiation Data Centre, Available: http://wrdc.mgo.rssi.ru/wrdc_en.htm.
[57] Zhang, C. et al. (2018). Generative Adversarial Network for Synthetic Time Series Data Generation in Smart Grids. 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), 2018.
)