Comprehensive Analysis of Energy Demand Prediction Using Advanced Machine Learning Techniques

¹ Department of Electrical and Electronics Engineering, Presidency University, Bengaluru, Karnataka, India
² Department of Electrical and Electronics Engineering, Presidency University, Bengaluru, Karnataka, India
³ Department of Electrical and Electronics Engineering, Presidency University, Bengaluru, Karnataka, India
⁴ Department of Electrical and Electronics Engineering, Presidency University, Bengaluru, Karnataka, India
⁵ Department of Electrical and Electronics Engineering, Presidency University, Bengaluru, Karnataka, India

^* Corresponding author: joshimanohar@presidencyuniversity.in

Abstract

Energy prediction plays a critical role in maximizing energy usage, reducing costs, and improving the effectiveness of power systems. Machine learning (ML) techniques are increasingly potent for analyzing intricate patterns in energy consumption and providing precise forecasts—both crucial for effective energy management. This study examines the application of ML in forecasting energy usage, focusing on two techniques: Long Short-Term Memory (LSTM) and Support Vector Machines (SVM). LSTM models, known for their ability to capture complex patterns, are evaluated for time-series energy data prediction. SVM, a supervised learning algorithm, is analyzed for its performance in energy forecasting under varying data conditions. The study compares the predictive accuracy, computational efficiency, and generalization capabilities of these models using metrics like R², RMSE, and MAE. Results indicate that LSTM excels with large datasets and non-linear patterns, while SVM is effective for smaller datasets with sensitivity to outliers. This analysis provides insights into selecting appropriate models for specific data characteristics and prediction requirements.