Leveraging Machine Learning and Digital Twin Technology for Efficient Energy Forecasting and Management

This study introduces a novel approach by integrating Digital Twin Technology (DTT) with advanced machine learning and reinforcement learning techniques to enhance power output predictions and optimize energy management. We systematically evaluate various machine learning models, including Long Short-Term Memory (LSTM), Random Forest, Decision Tree, Support Vector Machines (SVM), and Deep Neural Networks (DNN), within a digital twin framework to forecast the outputs of wind turbines, solar panels, and Battery Energy Storage Systems (BESS). Our simulations, based on synthetic data, reveal that DNN excels in predictive accuracy and computational efficiency, establishing it as the most effective model for energy forecasting.

کلیدواژه ها:

Digital Twin Technology (DTT) ، Machine learning ، Deep Neural Networks (DNN) ، Wind turbines ، Solar panels ، Energy forecasting