Renewable Energy Systems Energy Modeling using Deep Learning Techniques

Communities using Sustainable Energy Systems (RES) seek to meet their electrical needs while reducing their reliance on public utilities by integrating renewable energy sources. Additionally, an intelligent micro grid makes it simple to access services for controlling energy use, which might lower utility costs for locals. These infrastructures are influenced by ML technologies, big data, AI, the IoT, and sensor technologies. New advancements in ML technology are required to produce precise learning approaches that can be used in the electricity analytical process, such as such monitoring, forecasting, prediction, scheduling, and decision-making. This will improve power control assistance and the spread of renewable energy sources. However, as the complexity of issues with the smart grid system, such as non-linearity and unpredictability, rises, so does the complexity of the resulting energy data format. The learning process cannot be completed by the fundamental ML approach since it can only evaluate fundamental raw data. Therefore, despite the data’s intricate and extensive structure, the Deep Learning (DL) approach may be used. A Convolutional Neural Network (CNN) will be developed in this study as a learning model to provide precise forecasts of future power usage and renewable energy installations. The echo state network is used to learn temporal features once interesting patterns have been retrieved from the past using the convolution process. The resultant spatiotemporal feature representation is ultimately given to fully connected layers for prediction. The proposed method was developed after thorough testing of both deep learning and machine learning models. When compared to state-of-the-art models, the results show that the recommended model performs as a model for energy equilibrium among production resources and consumers, with significant decreases in forecasting errors using MAE, MSE, RMSE, and NRMSE metrics.