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The share of photovoltaic energy is more and more increasing in the World energy mix; the intermittence of this production makes difficult to maintain the stability of the electricity grid and the balance production-consumption. Predicting in advance the solar production facilitates the operation of the grid manager. This paper aims to forecast hourly global solar irradiation for time horizons from h+1 to h+6, using two approaches: multiple linear regression (MLR) and artificial neural network (ANN) models. The choice of inputs in these models is crucial for a good prediction and is investigated here; generally, only endogenous data are used (global solar irradiation), the addition of exogenous ones often improves the accuracy (ambient temperature, humidity, pressure and differential pressure) but they are not always available; the introduction of ordinal variables is studied: four ordinal variables allow to introduce the double seasonality of solar irradiation. The performances of two forecasting models (linear and nonlinear models) with combinations of endogenous, exogenous and ordinal variables are compared on two Algerian sites with different meteorological variabilities. It appears that adding ordinal variables to endogenous data decreases the nRMSE values and enables to reach the same level of reliability than adding exogenous variables while simplifying the implementation. This addition as inputs in ANN models decreased nRMSE by 0.45–1.65% points (2.6–6.2%) for Algiers and by 0.2–0.3% point (1–3%) for Ghardaïa according to the forecasting horizon. [ABSTRACT FROM AUTHOR]

In this paper, a new combined model coupling the linear autoregressive moving average (ARMA) model and the nonlinear artificial neural network (ANN) model has been proposed in order to estimate the daily global solar radiation. The main feature of this approach lies in the fact that has the strength to capture the advantages containing in both models. The combined method have been developed and tested using global solar radiation data recorded during two years (2012–2013) for two different climate sites in Algeria. The obtained results showed an improvement of the combined model over ARMA and ANN models in term of mean absolute error (MPE) of about 18.1% and 2.7%, for the first site, of about 27.26% and 1.39% for the second site. Moreover, compared to the ARMA and ANN models, a decrease in the RMSE values of about 17.1% and 3.59% compared to the ARMA and ANN models has been observed. [ABSTRACT FROM AUTHOR]