| Summary: | Solar energy and the concept of passive architecture and Net Zero Energy buildings are being increased. For an optimal management of the building energy, a Model Predictive Control is generally used but requires an accurate building model and weather forecast. For a more reliable modelling, the knowledge of the global solar irradiation is not sufficient; three methods, smart persistence, artificial neural network and random forest, are compared to forecast the three components of solar irradiation measured on the site with a high meteorological variability. Hourly solar irradiations are forecasted for time horizons from h+1 to h+6. The random forest method (RF) is the most efficient and the accuracy of forecasts are in term of nRMSE, from 19.65% for h+1 to 27.78% for h+6 for global horizontal irradiation, from 34.11% for h+1 to 49.08% for h+6 for beam normal irradiation, from 35.08% for h+1 to 49.14% for h+6 for diffuse horizontal irradiation. The improvement brought by the use of RF compared to the two other methods increases with the forecasting horizon. A seasonal study is realized and shows that the forecasting during spring and autumn is less reliable than during winter and summer due to a higher meteorological variability.
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