Spatio-temporal dynamic clustering modeling for solar irradiance resource assessment

Nowadays, with the development of international policies and agreements to promote integration renewable energy sources, mainly solar wind, modeling resource by including spatio-temporal variability is crucial determine future PV power plant locations estimate potential generation performances. However, contributions involving long-term periods different time windows explore such are generally scarce. Under this framework, present paper proposes a methodology focused on characterizing clustering through global horizontal irradiance analysis. Hierarchical technique firstly used classify spatial data. Different — from short-term data can be subsequently evaluated using various sources information. The Spanish territory selected as case study, considering 22-year period (1999–2020) 1,936,917 observations online satellite database. Spatial geographical differences discussed compared depending windows, identifying relevant variations for some specific months. Additionally, years more well, in line sunspot peak cycles. proposed approach gives an alternative comprehensive characterization GHI evolution, providing suitable help current European sustainable transition.