Machine Learning for Load Forecasting and Optimization in Smart Energy Grids

Abstract

Accurate load forecasting plays a foundational role in the reliable operation and economic optimization of modern smart energy grids. The increasing penetration of renewable energy sources, distributed generation, and intelligent consumer devices has introduced significant variability and complexity into power demand patterns. This study presents a comprehensive machine learning based framework for short and medium term load forecasting and operational optimization in smart grids. The proposed approach integrates deep learning architectures with feature selection and adaptive optimization mechanisms to address temporal dynamics, nonlinear dependencies, and demand uncertainty. Experimental evaluations using multi scale consumption data demonstrate improved forecasting accuracy and enhanced grid level decision support compared to traditional statistical methods. The results highlight the practical viability of data driven intelligence in supporting resilient and efficient energy infrastructure.