Smart Forecasting of Power Consumption with Machine Learning Models

Abstract

Accurate forecasting of power consumption is critical for efficient energy management, grid stability, and cost reduction. This study explores the application of advanced machine learning models to predict short-term and long-term power usage patterns. By leveraging historical consumption data alongside relevant external factors such as weather conditions, time of day, and economic indicators, the proposed approach employs algorithms including Random Forest, Support Vector Machines, and Deep Learning networks. The models are trained and validated on real-world datasets to evaluate their predictive accuracy and robustness. Results demonstrate that machine learning techniques significantly improve forecasting precision compared to traditional statistical methods. This enables smarter energy distribution, better demand response strategies, and supports the integration of renewable energy sources, thereby contributing to sustainable power system operation. Traditional statistical methods often fall short in capturing the complex, non-linear patterns of modern electricity usage. This project explores advanced machine learning techniques such as Random Forest, Support Vector Machines, and LSTM networks to predict both short-term and long- term electricity demand. By leveraging historical data, weather conditions, time-based factors, and user behavior, these models demonstrate superior forecasting performance compared to conventional methods. The study shows that machine learning enables smarter load balancing, better integration of renewable energy, and improved decision-making in power distribution systems. The proposed approach supports the development of intelligent, sustainable, and data-driven energy. Keywords: Power Consumption Forecasting, Machine Learning, Random Forest, LSTM, Smart Grid, Energy Management, Electricity Demand Prediction, Time Series Forecasting.