AI-Driven Predictive Energy Consumption Modelling for Smart Green Buildings Using Machine Learning

Abstract

Global energy consumption has increased significantly in recent decades-rapid urbanization and technological advancement. That means sustainability has become a critical global concern; it's something we need to figure out, fast. Buildings account for significant proportion of energy consumption, especially for heating, ventilation, air conditioning and appliances. Smart green Buildings have emerged as a promising solution, but honestly, most management systems running them still use conventional approaches that lack adaptability when the weather shifts or the number of people inside changes. This study addresses this limitations. We built an AI-powered system to predict and model energy use in these smart green buildings. Several machine learning models were implemented-Linear Regression, Random Forest Regression, and LSTM networks-analyzing historical energy consumption data along with environmental info like temperature, humidity, and time of day. To see which model actually works, we evaluated model performance with Mean Absolute Error, Root Mean Square Error, and ${R}^{{2}}$ scores. Turns out, deep learning models and ensemble approaches like LSTM and Random Forest significantly outperform traditional linear regression models, especially when the energy usage gets weird, nonlinear, or changes over time. At the end of the day, we're aiming to give assist facility managers in decision making: predicting how much energy they'll use, reducing energy waste, and making the whole setup more sustainable. This contributes to development of smart infrastructure, and honestly, that's exactly what we need right now.