Some Applications of ANN to Solar Radiation Estimation and Forecasting for Energy Applications

In solar energy, the knowledge of solar radiation is very important for the integration of energy systems in building or electrical networks. Global horizontal irradiation (GHI) data are rarely measured over the world, thus an artificial neural network (ANN) model was built to calculate this data from more available ones. For the estimation of 5-min GHI, the normalized root mean square error (nRMSE) of the 6-inputs model is 19.35%. As solar collectors are often tilted, a second ANN model was developed to transform GHI into global tilted irradiation (GTI), a difficult task due to the anisotropy of scattering phenomena in the atmosphere. The GTI calculation from GHI was realized with an nRMSE around 8% for the optimal configuration. These two models estimate solar data at time, t, from other data measured at the same time, t. For an optimal management of energy, the development of forecasting tools is crucial because it allows anticipation of the production/consumption balance; thus, ANN models were developed to forecast hourly direct normal (DNI) and GHI irradiations for a time horizon from one hour (h+1) to six hours (h+6). The forecasting of hourly solar irradiation from h+1 to h+6 using ANN was realized with an nRMSE from 22.57% for h+1 to 34.85% for h+6 for GHI and from 38.23% for h+1 to 61.88% for h+6 for DNI.