Numerical Modeling and Performance Assessment of Machine Learning-Based Solar Photovoltaic Energy Forecasting System

In order to combat climate change, renewable energy sources should be used in place of fossil fuels. Renewable energy comes from many different places. However in many nations, notably India, grid-connected power systems rely heavily on solar and wind energy. In addition, because both solar and wind sources are unpredictable, it might be difficult to plan for their generation. Due to the fluctuating nature of these resources, forecasting plays a crucial role here. Even for short-term forecasting, a sizable database that is examined using specialized algorithms and powerful computing power is required. Wind and solar power forecasting is an inescapable issue for the Indian energy system due to the present boom in variable power sources and associated grid management difficulties. When fluctuating solar and wind resources are used more often, a proper balance is needed. Forecasting for wind and solar energy is primarily required to guarantee the reliability of the electricity supply in general and the smooth operation of the grid in particular. The current weather conditions have a significant impact on the availability of wind and solar energy. Solar and wind power plant operations are made more difficult by the fact that the power produced from these resources does not satisfy the demand for energy in the same way as the power produced by conventional plants. In order to comprehend the anticipated power generation over a range of temporal and geographical scales, it is crucial to predict wind and solar energy. So, predicting solar and wind output within acceptable bounds is crucial for the effective integration of power from renewable sources into the electrical grid. By analyzing the pattern or trend, forecasting or prediction is the act of generating educated guesses about the future based on both current and historical data. Forecasting is an essential and cost-effective method that is now required for planning the production of energy from variable sources, namely solar and wind. The practice of making predictions based on historical and current data is known as forecasting. Energy forecasting can assist in resolving some of the problems caused by a lack of confidence in the resource. Solar energy forecasting has just recently received attention, whereas wind energy forecasting has already received substantial investigation. In this work, multiple linear regression-based machine learning approach is suggested for probabilistic estimations of solar energy.