IoT-Based Solar Power Forecasting Using Deep Learning

Due to the growing carbon footprints and the impacts of climate change, less fossil fuels are being used for transportation and energy production. The cost of creating solar photovoltaic (PV) panels has been reduced as a result of the improvements in manufacturing processes, bringing solar energy output on level with that of traditional fossil fuels. Solar power production is unpredictable, and this is influenced by the site’s capability to host. Once renewable energy sources have been installed, forecasting is essential for the grid’s effective management and functioning. The projection is helpful for choosing investments and setting up the distribution network during the initial planning phases. Three error measures were used to compare and evaluate the effectiveness of the LSTM network and variational mode decomposition (VMD).