A Machine Learning Approach for Load Forecasting in EV Integrated Renewable Power Grids
Chittireddy Dhanalakshmi, N.V.A Ravikumar, Kella Sai, Attada Venkata Sai
1,3,4B.Tech Student, Department of EEE, GMR Institute of Technology, Rajam-532127, Andhra Pradesh, India
2Senior Assistant Professor, Department of EEE, GMR Institute of Technology, Rajam-532127, Andhra Pradesh, India
Email: 23341A0227@gmrit.edu.in1
Abstract - The traditional power system's structure is rapidly evolving due to the shift towards electric vehicles (EVs) and renewable energy sources. With more electric vehicles on the road and solar and wind power plants joining the grid, it is getting increasingly challenging to predict the level of electrical usage due to the unpredictability of these technologies. Keep in mind that precise electric demand forecasting is essential for energy dispatch scheduling, grid stability, and the development of an efficient smart grid and EV model. The chaotic behaviour of today's electricity system and its non-linearity are beyond the scope of traditional statistical models and approaches. The ability to work with large volumes of data, exploit hidden data, and generate precise short-term (from minutes to weeks) and long-term (from months to years) load estimates has always been a strength of machine learning (ML) models. This research introduces a machine-learning technique to load forecasting in power systems combining renewable energy and EV. The approach has included a feature engineering process, a preparation phase, and the use of many machine learning algorithms, such as Random Forest, Support Vector Regression, and LSTM. According to the study, machine learning techniques improve forecasting accuracy when compared to conventional techniques. The study concludes by outlining the difficulties, restrictions, and potential avenues for further research in intelligent energy management.
KeyWords: Machine learning, Load forecasting, Electric Vehicles, Renewable Energy, Smart Grid.
