Electrical energy demand is expected to increase in the upcoming years making more unsustainable the traditional ways to get it. For this reason, the world is increasing the share of renewable energy. However, the energy transition comes with some challenges due to the uncertainties of the natural resource grid as wind and solar. This change of paradigm is a reality for big-scale systems like the electricity grid and small-scale systems like a Home Energy Management System (HEMS). In essence, the objective of the thesis is to design a forecasting photovoltaic (PV) model versatile and generic for being able to adapt to the specifications of every system under investigation. The model is designed by considering and using parametric and non-parametric methods. This type of modelling is popularly known as grey-box modelling. The core of the model is defined by physics equations, so it is the parametric part of the model. However, this part has some assumptions and idealizations with it. For compensating them, two data-driven corrections are done following non-parametrical techniques. Therefore, the model takes advantage of both procedures. In order to validate the model, two case studies that consist of two PV systems located in different countries are defined. From their historical power output data and the forecast made by the PV model, it is possible to calculate their mismatch and verify the design model. In the end, the calculations show that the model is robust when it comes to forecasting even if the systems are located at different points, but it loses accuracy on those days that the sky is fully covered.