The Solar Energy Forecasting by Pearson Correlation using Deep Learning Techniques

Abstract

Solar energy is one of the most important renewable energy sources (RES) with many advantages as compared to other types of sources. Climate change is gradually becoming a global challenge for the sustainable development of humanity. There will potentially be two key features, for future electricity systems, high penetration or even dominance of renewable energy sources for clean energy e.g., onshore/offshore wind and solar PV. Solar energy forecasting is essential for the energy market. Machine learning and deep learning techniques are commonly used for providing an accurate forecasting of the energy that will be produced. The weather factors are related to each other in terms of influence, a wide range of features that are necessary to consider in the prediction process. In this paper, the effect of some atmospheric factors like Evapotranspiration and soil temperature are investigated using deep learning techniques. Higher accuracy is achieved when new features related to solar irradiation were considered in the forecasting process.