This paper examines the impact of cloud cover on the efficiency of solar panels. Cloudiness significantly reduces the amount of solar radiation reaching the surface of the panels, directly affecting their electricity generation. The study analyzes different types of clouds, their density, altitude, and duration, as well as seasonal cloud patterns in various climatic conditions. Special attention is paid to the dynamic changes in cloud cover throughout the day, as sudden fluctuations in sunlight can cause instability in photovoltaic systems. Methods for accounting for cloudiness in solar power generation modeling are discussed, including the use of satellite monitoring data and local meteorological stations. The paper also explores the application of intelligent forecasting systems based on artificial intelligence and machine learning, which can improve the accuracy of solar potential assessments under variable cloud conditions. The findings of this study can be useful for the design and operation of solar power plants in regions with frequent or unpredictable cloud cover.Using the Solarmeter device, we measured solar insolation in the city of Almalyk under light and dense cloud cover. The obtained values were used in the Matlab Simulink program to model the station and determine the possible power output.