Solar radiation forecasting using artificial neural network and random forest methods: Application to normal beam, horizontal diffuse and global components. (March 2019)

• Record Type: Journal Article
• Authors: Benali, L.; Notton, G.; Fouilloy, A.; Voyant, C.; Dizene, R.
• Is Part Of: Renewable energy. Volume 132(2019)
• Page Start: 871
• Page End: 884
• Publication Date: 2019-03
• Subjects: Solar irradiation forecasting -- ANN -- Random forest -- Beam solar radiation -- Diffuse solar radiation -- Global solar radiation; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2018.08.044
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: Three methods, smart persistence, artificial neural network and random forest, are compared to forecast the three components of solar irradiation (global horizontal, beam normal and diffuse horizontal) measured on the site of Odeillo, France, characterized by a high meteorological variability. The objective is to predict hourly solar irradiations for time horizons from h+1 to h+6. The random forest (RF) method is the most efficient and forecasts the three components with a nRMSE from 19.65% for h+1–27.78% for h+6 for the global horizontal irradiation (GHI), a nRMSE from 34.11% for h+1–49.08% for h+6 for the beam normal irradiation (BNI); a nRMSE from 35.08% for h+1–49.14% for h+6 for diffuse horizontal irradiation (DHI). The improvement brought by the use of RF compared to Artificial Neural Network (ANN) and smart persistence (SP) increases with the forecasting horizon. A seasonal study is realized and shows that the forecasting of solar irradiation during spring and autumn is less reliable than during winter and summer because during these periods the meteorological variability is more important. Highlights: Forecasting of the three solar components: beam, diffuse and global. Comparison of three forecasting methods: Smart persistence, MLP and Random Forest. Good accuracy of the random forest forecasting tool. Forecasting of hourly solar data with a time horizon from h+1 to h+6.