The results presented in this thesis can serve as a basis for many further research directions.
Regarding the power forecasting, the verification of the model chains for other climatic regions could reveal the dependence of the best model chains on the local conditions and identify the worldwide best model chains. Another important step to further analyze the universality of the best model chains is to compare the power predictions created from different irradiance forecasts, e.g., further NWP providers of even satellite-derived forecasts. The accuracy of the power forecasts could be further improved using machine learning hybridized with the presented physical modeling methodology. Finding the most suitable learning algorithm and the best way of hybridization is a wide field for further research. The accuracy could also be improved by fine-tuning the parameters of several capable physical models based on more detailed measurement data, like on-site Gh, Bh, Dh, Gt, Tc, and PDC measurement. Additional studies are also required to identify whether the MAE, the RMSE, or their weighted sum is the best representation of the value of the forecast for the different market participants. The variance of the MAE-optimized forecasts can be reduced by a smoothing post-processing technique (e.g., moving average), but it still has to be discovered how this smoothing affects the RMSE of the forecast.
The PV design optimization framework can be used for a wide range of further research, e.g., a detailed analysis of the effect of different financial subsidies or the changes in the electricity market prices. The framework can be further broadened by other objectives, like minimizing the balancing energy costs resulting from forecast inaccuracies. The extension of the model to describe the effects of a battery storage system can result in an effective tool for evaluating the benefits of the batteries with different storage strategies. Incorporating the design optimization and the expected cost-changing tendencies to a long-term energy system modeling framework can improve the projections for the energy mix and electricity prices of the future.
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