Energy Savings in Buildings Using Predictive Analysis

Abstract

Effective energy management in buildings is essential for reducing operational costs, enhancing efficiency, and minimizing environmental impact. This paper explores the integration of machine learning techniques, specifically Long ShortTerm Memory (LSTM) networks, to predict energy consumption patterns and optimize usage. By leveraging predictive energy modeling, buildings can reduce peak demand, shift nonessential loads, and enhance overall energy efficiency. The study examines the potential benefits of LSTM-based forecasting in enabling data-driven decision-making, leading to smarter and more sustainable energy management strategies.