Deep Learning-Based Forecasting of Solar Photovoltaic Generation for Microgrid Control

Deep Learning-Based Forecasting of Solar Photovoltaic Generation for Micro grid Control is a new approach to accurately predicting solar photovoltaic generation in micro grids. These feature renewable energy such as Solar PV and are a responsive approach for providing efficient, sustainable power solutions. In contrast, solar PV power production is not dispatch able because it depends on the changing weather and other factors. The new method applies deep learning algorithms, mainly artificial neural networks, to historical data to accurately forecast solar PV output. The latter sorts the variation due to a broad array of meteorological and operational parameters, including temperature, humidity level, cloud cover, and shading Moreover, the model can forecast results online and keep learning with new data for real-time micro grid control. The deep learning-based method has shown superior performance to traditional forecasting methods in improving the precision of PV generation forecasts. Benefits include more efficient energy management, better integrating renewable sources into the micro grid, and lower costs. This means that the Deep Learning-Based Forecasting of Solar Photovoltaic Generation for Micro grid Control could significantly improve the reliability and efficiency of micro grid operations and promote a more sustainable and resilient energy system.