Solar power plant systems face complex nonlinear dynamics, which make accurate prediction challenging with traditional methods due to atmospheric fluctuations, solar radiation variations, and environmental uncertainties. Remote sensing and deep neural networks (such as CNN, RNN, and LSTM) enable the analysis of spatial-temporal data, which provides superior performance in predicting solar radiation for renewable energy production. These methods are crucial in advanced sensor systems for the design, prediction, maintenance, and control of solar power plants, and they offer greater safety, reliability, and efficiency compared to classical approaches. This article aims to review remote sensing and neural network technologies, their advantages (high accuracy, generalizability), and their limitations compared to traditional methods for solar radiation prediction. Unlike other reviews, this study summarizes adaptive intelligent models, proposes simple yet effective methods based on remote sensing and neural network sensor systems, maps the digital transformation to smart solar power plants with integrated technologies, and evaluates the impact of these technologies on the renewable energy value chain.

Remote Sensing, Neural Networks, Solar Radiation Prediction, Solar Power Plant, Machine Learning, Solar Irradiance, Renewable Energy.