Increasing Clean Energy Access in Africa Through AI

Background

Sub-Saharan Africa faces a significant energy crisis, with over 600 million people lacking access to electricity and many relying on firewood and charcoal for cooking. Electricity demand is growing at 11% annually—the highest globally—highlighting the urgent need for sustainable energy solutions. Aging infrastructure, high maintenance costs, and underutilized data further exacerbate the challenges. Renewable energy, particularly solar PV, offers a transformative opportunity for modernizing energy grids and meeting growing demands, especially for commercial and industrial sectors.

Objective

This project aimed to:

1. Develop predictive models for solar rooftop installations and gas pay-as-you-go reticulation services.
2. Enhance energy adoption where it’s most needed by leveraging smart energy monitors.
3. Build analytics insights to support renewable energy transition for Commercial and Industrial (C&I) clients and suppliers.
4. Detect operational anomalies in solar assets using predictive models integrated with weather data.

Approach

To address the energy access challenges in Africa, Omdena collaborated with 50 technology changemakers over two months. The team utilized the following methods and tools:

1. Data Collection: Organized historical and forward-looking data, including smart meter readings, demographic data, weather forecasts, and market trends.
2. Data Integration: Built relational and time-series databases and developed APIs for seamless data access.
3. Predictive Modeling: Designed algorithms to project energy usage, detect anomalies in solar assets, and optimize gas supply logistics.
4. IBM Deep Thunder Integration: Incorporated weather data to enhance solar installation designs and operations.

Results and Impact

• Predictive Solar Solutions: Enabled NeedEnergy to design tailored solar installations based on projected energy usage.
• Enhanced Energy Monitoring: Leveraged smart energy monitors to improve decision-making for C&I clients transitioning to renewable energy.
• Operational Optimization: Provided insights to gas suppliers for better inventory management and delivery planning.
• Anomaly Detection: Developed models to identify and mitigate operational issues in solar assets, ensuring reliability and efficiency.
• Scalable Systems: Created APIs and data storage solutions to support long-term energy research and application.

By addressing inefficiencies and promoting renewable energy adoption, the project significantly contributes to sustainable energy access in Africa.

Need Energy about the AI Challenge results

Future Implications

The findings from this project have far-reaching implications:

• Policy Influence: The insights can guide governments and organizations in shaping energy policies and frameworks for renewable adoption.
• Energy Sector Modernization: The tools and models developed offer a blueprint for modernizing energy infrastructure across Africa and beyond.
• Scalable Applications: The methodologies used can be replicated in other regions facing similar energy challenges, promoting global energy equity.
• Enhanced Research: The databases and APIs established lay the foundation for ongoing innovation in clean energy solutions.