This project analyzes the Smart Meters in London dataset, performing data preprocessing, EDA, and predictive modeling to forecast energy usage and identify optimization opportunities. It demonstrates my expertise in transforming raw data into actionable insights for improving energy efficiency using AI and real-world datasets.

This project aims to estimate and compare the annual solar irradiation (kWh/m²) and average daily solar peak hours (HSP) across different zones of Houston, Texas.

A project leveraging LSTM and XGBoost to analyze and predict energy usage, identify inefficiencies, and optimize costs across buildings, providing actionable insights for energy management and savings.