Weather Forecasting for Renewable Energy System: A Review - Meenal, R.; Binu, D.; Ramya, K. C.; Michael, Prawin Angel; Vinoth Kumar, K.; Rajasekaran, E.; Sangeetha, B. (Archives of computational methods in engineering, 2022)
Energy crisis and climate change are the major concerns which has led to a significant growth in the renewable energy resources which includes mainly the solar and wind power generation. In smart grid, there is a increase in the penetration level of solar PV and wind power generation. The solar radiation received at the earth surface is greatly dependent on various atmospheric parameters. Forecasting of solar radiation and photovoltaic power is a major concern in terms of efficient integration of solar PV plants in the power grid. There are significant challenges in smart grid energy management due to the variability of large-scale renewable energy generation. Renewable energy forecasting is critical to reduce the uncertainty related to renewable energy generation for a wide range of planning, investment and decision-making purposes. As renewable energy sources are highly intermittent and variable, all the forecasting models available in the literature contain errors. This paper presents an overview of current and new development of weather forecasting such as solar and wind forecasting techniques for renewable energy system in smart grid. Many forecasting models such as physical models, statistical models, artificial intelligence based models, machine learning and deep learning based models were discussed. It is observed that, despite having no clear understanding on atmospheric physics, the artificial intelligence based methods such as machine learning and deep learning method produces reasonable weather forecasting results.