Forecasting renewable energy sources is of critical importance to several practical applications in the energy field. However, due to the inherent volatile nature of these energy sources, doing so remains challenging. Numerous time-series methods have been explored in literature, which consider only one specific type of renewables (e.g., solar or wind), and are suited to small-scale (micro-level) deployments. In this paper, the different types of renewable energy sources are reflected, which are distributed at a national level (macro-level). To generate accurate predictions, a methodology is proposed, which consists of two main phases. In the first phase, the most relevant variables having impact on the generation of the renewables are identified using correlation analysis. The second phase consists of (1) estimating model parameters, (2) optimising and reducing the number of generated models, and (3) selecting the best model for the method under study. To this end, the three most-relevant time-series auto-regression based methods of SARIMAX, SARIMA, and ARIMAX are considered. After deriving the best model for each method, then a comparison is carried out between them by taking into account different months of the year. The evaluation results illustrate that our forecasts have mean absolute error rates between 6.76 and 11.57%, while considering both inter- and intra-day scenarios. The best models are implemented in an open-source REN4Kast software platform.

Renewables are the greener substitute for the conventional polluting sources of generating energy. For their successful integration into the power grid, accurate forecasts are required. In this paper, we report the lessons acquired from our previous works on generating time-series ARIMA-based forecasting models for renewables. To this end, we considered a consistent dataset spanning the last four years. Assuming four different performance metrics for each of the best ARIMA-based models of our previous works, we derived a new optimal model for each month of the year, as well as for the two different methodologies suggested in those works. We then evaluated the performance of those models, by comparing the two methodologies: in doing so, we proposed a hybrid methodology that took the best models out of those two methodologies. We show that our proposed hybrid methodology has improved yearly accuracy of about 89.5% averaged over 12 months of the year. Also, we illustrate in detail for the four years under study and each month of the year the observed percentage of renewables and its corresponding accuracy compared to the generated forecasts. Finally, we give the implementation details of our open-source REN4KAST software platform, which provides several services related to renewables in Germany.