Numerical Simulation and Analysis of Solar Energy Forecasting Using Machine Learning Methodologies
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Abstract
India is a developing country with a high energy demand that is hard to meet with traditional ways of making power. As of July 30, 2012, New Delhi to Kolkata were all affected by the world's biggest power outage. In the next five years, India's ability to make electricity will grow by 44%. India's need for electricity goes up as the country's population and economy grow. So, what needs to change to cut down on power outages and meet the energy needs of the future? India has decided to use renewable energy sources instead of fossil fuels because it is cheaper and better for the environment. In recent years, more PV panels have been installed because they are becoming more cost-effective as a source of renewable energy. In the meantime, more data and more powerful computers have made it possible for machine-learning algorithms to make better predictions. Machine learning and time series models can help people in the energy business make accurate predictions about how much energy solar PV panels will produce. In this study, different sites are used to compare different machine learning approaches and time series models to see which one works best. Wind energy forecasting has already had a lot of research done on it, but solar energy forecasting is just now getting more and more attention. This study gives a model for a thorough review and analysis. Power system operational planning is one of the most important things to think about right now. For the power system to work well, many different factors must be predicted as accurately as possible over different forecasting horizons. But different variables have different characteristics, and academics have come up with different ways to predict them in the literature. But putting into action and analyzing recently published forecasting models is hard because there are a lot of outside factors that interact with each other in a complicated way. To plan for renewable energy sources, it is important to come up with a smart plan. Because the power system is getting more and more complicated, it is still a work in progress to find the best way to predict these variables with the least amount of computer work.
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