A Machine Learning Approach for Solar Radiation Assessment using Multispectral Satellite Images
Solar radiation estimation is an important parameter in engineering applications including solar power plant modelling, photovoltaic cell modelling, and solar heating system modelling. Therefore, the proper estimation of solar radiation is necessary. In recent years, solar radiation prediction models have been established based on parameters including ambient temperature, sunlight period, humidity and cloud coverage estimated from traditional meteorological stations and analyzed indirectly as a function of solar radiation. These models are divided into two categories: artificial intelligence-based parametric methods like Angstrom, and nonparametric methods. It has been found in the literature that data on solar radiation can be calculated using these models at a specific location. One of the easiest ways of measuring solar radiation on the surface is to use sensor data from ground sites, over existing ground points, it also provides high temporal resolution projections of incoming solar radiation. This strategy, however, has a number of technological and financial drawbacks, including high costs and the need for fully skilled labor, as well as the need for daily solar sensor maintenance, washing, and calibration. Ground sensor networks, on the other hand, are hardly ever available insufficient spatial coverage to address spatial pattern.
Solar radiation obtained by satellite is a trustworthy instrument to measure solar irradiance at ground level in a wide region. In addition, hourly values obtained were at least as precise as interpolation at a distance of 25 km from ground stations. Multispectral sensors are usually used on satellites to characterize environmental conditions such as light dispersion, reflection and absorption by ray leaves, water vapors, ozone, aerosols and clouds, as the amount of radioactive radiation emitted by the atmosphere not only affects the distribution of the atmospheric components but also the sensitivity of the sensor.
The large variety of observation techniques of satellites are thus intended to be perfect for the measurement of spatial variation in solar radiation. Satellite imaging may be utilized in two ways: to design complicated models of radiation transmission utilizing atmospheric characteristics from multi-spectral pictures, or to search for table-based models associated with the radiation process' physical parameterization.
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