The production of electrical energy using renewable energy sources is currently an irreplaceable element in the composition of the service offer of environmentally sustainable power generation systems. In this way, the production of electricity from photovoltaic sources is highly complementary to the seasonality of wind energy sources. Along with its very competitive prices compared to fossil fuels, solar energy will have an ever-increasing economic and environmental impact.

The production of photovoltaic solar energy has grown annually at a rate of 40% and both the associated technology and the configuration of the plants themselves are changing in a disruptive way. Instead of easily accessible peak megawatt (MW) power plants with just one type of technology available, the new configurations are hundreds of MW installed in various locations and with various technologies involved. Current photovoltaic plant operators are not prepared to optimize and maximize the profitability of these new types of assets due to the significant increase in installed power, the difficulty of accessing the panels (granularity), the significant number of hours of employees for data analysis and, therefore, also due to the lack of adequate management tools.

With the increase in the number of large plants with different technologies, it is necessary to develop photovoltaic asset management tools that answer the current reality of large plants, which is why SMART-PV will focus on this market. The project is perfectly aligned with several national and international technological and economic plans, in particular the European technical plan for the energy sector - Strategic Energy Technology Plan (SET Plan), and will contribute to catapulting Portugal as a leader in the management of large-scale photovoltaic plants.

The SMART-PV solution is a cloud computing system that combines the acquisition and management of information through intrinsic data generated by the photovoltaic production plants, with aerial images for the identification and characterization of eventual defects in the photovoltaic modules, and data climatic and geographic conditions, among others, to produce a predictive preventive maintenance procedure aimed primarily at large plants located in any location.