In Nigeria, frequent power cuts pose substantial challenges to businesses, leading to daily operational disruptions and increased costs of running a business. Businesses suffer several hours without access to electricity each day, with many relying on diesel or petrol generators. Our study aims to understand and proffer solutions to this daily ordeal using Python data science techniques.

Our methodology entails a sensor-based data-driven approach where we deploy energy monitoring devices to monitor and collect operational data and utilize versatile Python libraries and tools to develop personalized energy use profile for a typical business operating within an active office environment. This profile furnishes the business with detailed information on when and where energy is consumed, identifies energy-intensive appliances, and provides insights into consumption patterns and blackouts at different points in time.

Leveraging state-of-the-art multi-dimensional visualizations and anomaly detection frameworks in Python we give recommendations on optimizing energy usage, identifying predator appliances, strategies to reduce fuel costs and electricity bills, and mitigating the impact of blackouts on business operations.

Benefits of our Presentation to the Audience:

Participants will learn about using local data to address local problems.

Participants will learn the application of Python native visualization tools for multi-dimensional data visualization.

Participants will learn Python frameworks for anomaly detection tasks.