Intelligent Optimization of Photovoltaic Stations Performance Relying on Integrated Solar Techniques

Most countries seek to establish small photovoltaic stations within cities on the roofs of houses and public buildings due to the lack of large spaces of empty land within cities to establish large stations. The research discusses raising and improving the efficiency of small-sized independent stations on the roofs of public buildings by studying several variables such as panel size, site coordinates, building dimensions, surface area for establishing the station, daily sun angles, and their heights in the building area during the year. The factors affecting photovoltaic panels also include regional climatic factors, tilt angles of panels with the roof, the distances between the rows of the panels, solar radiation, and the panel's temperature. This research uses different artificial intelligence programs such as Google Earth, Skelion, and PVSYST to infer the previous variables and determine the best dimensions between rows and the best tilt angles for installing solar panels. These programs also deduce the effect of climatic factors on the panels, calculate the amount of electrical energy produced, and determine the loss in the station’s annual production. The research results show that the best tilt angles of photovoltaic panels with the roof were between 18° and 22°, as shadows don’t affect the amount of electrical energy generated. The results also confirm the importance of solar radiation falling on photovoltaic panels, as the amount of electrical energy generated is directly proportional to the monthly average amount of radiation falling on photovoltaic panels.