Yield Prediction Of PV Solar Energy Systems And Its Application In The Energy Grid For Operational Efficiency

The generation of power from photovoltaic (PV) solar panels is influenced by a multitude of factors. These include the tilt and orientation of the solar panels, the latitude of their location, and the prevailing climate and weather conditions. Additionally, shading at specific locations, particularly if the panels are not part of a solar facility, can significantly impact their efficiency. The quality and efficiency of the panels themselves, along with the preventive maintenance of both the solar panels and associated components such as inverters and trackers, are also critical. Finally, the overall system design and installation play a vital role in determining the power output. Understanding these factors and their influence is essential for accurately predicting the load that PV systems might impose on the grid and for maximizing their energy production. Independent studies are necessary to assess the dramatic effects of solar irradiance on production and its variance from the production specifications of the solar panels. This paper aims to examine various algorithmic approaches to better predict solar generation in the El Paso, Texas region and the Logan, Utah region, home to Utah State University. The data utilized in this study is sourced from the Texas Community Solar Facility, owned by El Paso Electric, and the Solar Car-Park located at Utah State University. To accurately predict solar generation and determine whether grid load needs to be alleviated with alternative resources or if energy should be sold at a higher rate, a comprehensive study of different predictive processes is conducted. This research is crucial as it can enhance grid stability and reliability, improve economic efficiency, and inform the development of more robust government policies with significant positive environmental impacts