Deep Learning based Framework for Forecasting Solar Panel Output Power

Abstract

Energy Harvesting from diverse renewable energy sources has experienced rapid growth due to the adverse environmental impacts of using fossil fuels. Solar energy is a signi cant energy source frequently used for power generation in various applications. Due to the variable nature of solar irradiation, temperature, and other metrological parameters, Photovoltaic (PV) power generation is highly uctuating. This unstable nature of output power has been evolved as a considerable issue in various applications of solar energy prediction system. In this work, a hybrid Deep Learning (DL) model based on Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Attention mechanism to forecast solar cell output power has been proposed. The proposed model is implemented, and its performance is compared with other DL models, including CNN, LSTM, and LSTM with an attention mechanism. The proposed model has been trained and evaluated with a publicly available dataset which contains 20 parameters on which solar panel output power is relatively dependent. The model yields maximum coe cient of determination (R 2 ) up to 84.5%. A lightweight model has also been developed using the pruning technique to implement the DL model into a low-end hardware.