About
This ML project focused on analyzing and visualizing the net energy balance for multiple units, referred to as 'pods,' within a facility. The goal was to provide actionable insights into energy consumption and production patterns, enabling data-driven decisions for optimizing energy usage and exploring storage solutions. Using Python, we developed robust scripts to process raw energy data, clean and transform it, and engineer features for calculating the net energy balance (energy produced minus energy consumed). This required leveraging Pandas for data manipulation and ensuring accurate, efficient handling of large datasets. The analysis phase involved deriving key insights, such as identifying pods with the highest energy deficits or surpluses and observing trends over time. For visualization, we employed matplotlib and seaborn to create intuitive and visually appealing charts, including time series plots, bar graphs, and heatmaps. These visualizations made it easy for stakeholders to identify patterns, anomalies, and opportunities for improvement. This project, part of our ML Projects, played a critical role in uncovering inefficiencies in energy usage and highlighted opportunities for storage integration or redistribution among pods. The insights enabled the facility to make strategic decisions for enhancing energy optimization and sustainability. Skills/Technologies Used: Python: For scripting and data processing. Pandas: To manipulate, clean, and transform data. Matplotlib & Seaborn: For advanced data visualization. Data Analysis: To derive actionable insights for energy optimization. This comprehensive approach ensured the facility gained a deeper understanding of its energy dynamics, paving the way for enhanced efficiency and cost savings.