new method of optimizing solar energy prediction through simultaneous calculations of fuzzy logic and mean error percentage

Photovoltaic electricity generation is completely dependent on uncertain and uncontrollable meteorological factors such as solar radiation, space temperature, module temperature, pressure and wind direction, and humidity. the electricity output from the photovoltaic system changes dynamically in time according to the changes of environmental factors. therefore, it is very difficult to accurately predict the amount of electricity produced from a solar system. in this article, a fuzzy logic-based model for solar energy forecasting is developed and presented, which includes meteorological parameters such as dew point in addition to commonly known parameters such as duration of sunlight, ambient temperature, wind speed and relative humidity of the climate region. uses differently. the results obtained from intelligent modeling with measured data for several weather stations representing several different climate zones, i.e. combined climate, hot and dry, hot and humid, cold and cloudy and climate zone. it is average, they will be compared.