Predicting solar radiation at high resolutions: A comparison of time series forecasts

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Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques (2019, methodology): “Reikard [53] developed an ARIMA model for”
Time-Series Regression Model for Prediction of Mean Daily Global Solar Radiation in Al-Ain, UAE (2012, methodology): “Reikard [42] employs a combination of logarithmic regression and ARIMA modeling to predict solar radiation at high resolution and compares his models with other forecast methods such as ANN and considered the 24-hour daily seasonality not taken into account in most modeling approaches.”
Photovoltaic and solar power forecasting for smart grid energy management (2015, methodology): “In [26], input data of ARIMA are transformed to log values to predict solar irradiance.”
Intra-hour DNI forecasting based on cloud tracking image analysis (2013, methodology): “The time-series forecasting methodologies have been applied successfully for 5–15 min (Reikard, 2009), 1-h (Cao and Lin, 2008; Sfetsos and Coonick, 2000; Martin et al.”
Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques (2019, background): “Auto-Regressive Moving Average (ARMA) forecasters perform well when data are stationary, while Auto-Regressive Integrated Moving Average (ARIMA) models perform well with non-stationary time series [26].”