Comparative Between Neural Networks Generate Predictions for Global Solar Radiation and Air Temperature

Abstract

Technology is becoming an increasingly important and indispensable tool in human life, making it necessary to develop various forms of renewable energies. However, over time it became necessary to improve this technology that becomes more advanced and efficient. The purpose of the research is to compare the results of three distinct AI algorithms, forecasting in two hours, using the database available by the Instituto Nacional de Meteorologia (INMET). The results indicate that the K-Nearest Neighbors Regression network proved to be more effective for estimating Global Solar Radiation (W/m²) and Multi-LayerPerceptron for forecasting Air Temperature (ºC).