Using AI and GIS to Identify Optimal Microgrid Sites: Accelerating Rural Electrification with Data-Driven Insights

Introduction: Bridging the Energy Gap with AI-powered Microgrids

Over 760 million people worldwide still lack access to reliable electricity, with rural regions in developing countries bearing the brunt of this energy deficit. Traditional electrification methods, which involve manual surveys and site visits, are slow, expensive, and ineffective at scale. This inefficiency often delays renewable energy projects limiting their reach and impact. Effective microgrid site identification is crucial for addressing these challenges.

Artificial Intelligence (AI) and Geographic Information Systems (GIS) are transforming how energy companies and governments identify electrification needs and deploy solar solutions, particularly by enabling the identification of optimal microgrid sites. By automating site selection for microgrid installations, analyzing geospatial data, and providing actionable insights, these technologies are speeding up rural electrification and enhancing renewable energy adoption.

Key Challenges in Rural Electrification

Case Studies: Transforming Energy Access with AI Powered Microgrid Site Identification

1. Identifying Potential Solar Micro-Grid Sites in the Philippines

The Problem

The Philippines faces unique energy challenges due to its geography, with many rural communities disconnected from the central grid. These areas rely on costly diesel generators, leading to high energy costs and environmental damage. Traditional methods for identifying suitable locations for solar micro-grids are time-consuming, expensive, and lack precision, delaying the adoption of sustainable energy solutions.

The Solution

Using AI-powered GIS tools, Omdena streamlined the process of identifying optimal sites for solar micro-grid deployment, focusing on precise microgrid site identification.

1. Data Integration:
   • Satellite Imagery: High-resolution data from Sentinel-2 and Landsat 8 was used to assess land use and solar irradiance.
   • Demographic Data: GRID3 datasets helped identify off-grid communities by analyzing population density and energy demand.
   • Energy Metrics: Historical consumption data and solar performance metrics supported accurate demand modeling.
2. Machine Learning Analysis:
   • Clustering Algorithms: Identified regions with significant energy demand and high solar potential.
   • Convolutional Neural Networks (CNNs): Processed satellite imagery to pinpoint viable land for solar installations.
3. Visualization Tools:
   • Heatmaps and dashboards provided actionable insights, enabling stakeholders to prioritize high-impact regions efficiently.

The Impact

Challenge Highlight

Data Gaps and Geographic Complexity: Many rural areas lacked precise energy consumption data, requiring the team to interpolate demand based on population density. Additionally, the Philippines’ diverse terrain made generalizing models challenging, necessitating localized calibration and iterative testing to ensure accuracy.

2. Transforming Energy Access Planning in Kenya

The Problem

In Makueni County, Kenya, limited access to reliable energy hinders socio-economic development, particularly in agriculture. Planners face challenges in integrating granular energy data to address supply and demand disparities. Traditional methods for mapping solar penetration and renewable energy use are time-consuming and prone to inaccuracies, underscoring the need for efficient microgrid site identification to accelerate the deployment of sustainable energy solutions.

The Solution

Omdena partnered with the World Resources Institute (WRI) to enhance the Energy Access Explorer (EAE) tool by integrating advanced AI models and diverse datasets to streamline energy planning.

1. AI Models Utilized:
   • ARIMA and LSTM: Forecasted energy generation and demand trends for better resource allocation.
   • Convolutional Neural Networks (CNNs): Accurately identified solar panels in satellite imagery.
   • Clustering Algorithms: Mapped patterns and trends in energy usage for more targeted solutions.
2. Data Sources Integrated:
   • Geospatial Imagery: Used for solar panel detection to improve supply-side data.
   • Weather Data: Incorporated to predict renewable energy generation potential.
   • Historical and Real-Time Grid Data: Combined to model energy demand and identify underserved areas.

The Impact

Challenge Highlight

Data Inconsistencies and Limited Availability: Access to granular energy consumption and solar installation data was limited. The project overcame this by leveraging advanced image recognition models and integrating diverse datasets to fill data gaps, ensuring accurate and actionable insights for energy planners.

3. Optimizing Solar Container Placement in Nigeria

The Problem

In Nigeria, millions of people lack access to reliable electricity, with rural and peri-urban areas particularly underserved. Identifying optimal locations for solar container installations is challenging due to limited data on electricity demand and the high costs associated with manual site assessments.

The Solution

Omdena developed a heatmap-based methodology to identify high-priority locations for solar container deployment, leveraging population and satellite imagery data to pinpoint areas with the greatest need for electrification.

1. Data Integration:
   • GRID3 Population Data: Provided insights into population density in underserved regions.
   • NOAA Nighttime Lights: Highlighted areas without electricity by analyzing light intensity at night.
   • Threshold Calibration: Refined accuracy by calibrating data with known villages and their electricity status.
2. Heatmap Creation:
   • Gaussian Filters: Smoothed demand signals to enhance the clarity of high-need areas.
   • Image Segmentation: Clustered demand hotspots using advanced segmentation techniques.
3. Output:
   • Created interactive maps to visualize high-demand locations, enabling planners to prioritize installations efficiently.

The Impact

Challenge Highlight

Data Noise and Coverage Gaps: Discrepancies in nighttime satellite imagery and sparse population data required intensive calibration. The project overcame this by integrating multiple datasets and validating results with known data points, ensuring actionable and reliable insights.

4. AI-Driven School Mapping: Enhancing Educational Planning in Sudan

The Problem

In Sudan, school location data is often inaccurate, incomplete, or entirely missing, hindering efforts to provide essential connectivity infrastructure. Many schools are in remote or insecure regions, making manual mapping methods expensive, time-consuming, and infeasible. Addressing this challenge is critical for Giga, a UNICEF-ITU initiative aimed at connecting schools globally to the internet, and for improving educational opportunities for children in Sudan.

The Solution

Omdena led an AI-based initiative to predict school locations across Sudan, leveraging high-resolution satellite imagery and advanced deep-learning techniques. The UNICEF Sudan Country Office provided crucial contextual support, offering on-ground insights and parameters that guided the mapping process.

1. AI Models Used: Advanced AI tools analyzed satellite images to identify schools. One type of model focused on recognizing patterns in the images, while another specialized in detecting exact locations and boundaries of schools.
2. Data Preparation: We carefully labeled thousands of images to train the AI models better, ensuring they could accurately tell schools apart from other buildings.
3. Results: The AI reviewed 90,000 images and created detailed maps showing school locations. After refining the process, we achieved a 95% accuracy rate, making this a valuable tool for mapping schools in underserved regions.

The Impact

Challenge Highlight

Mapping school locations in Sudan is essential but challenging due to incomplete data, especially in remote and conflict-prone areas. The project aimed to address this gap using AI and satellite imagery, guided by parameters provided by the UNICEF Sudan Country Office.

5. A Building Detection AI Model for Humanitarian OpenStreetMap 

The Problem

Accurate maps are essential for disaster resilience and infrastructure planning, yet many disaster-prone regions lack updated or comprehensive mapping data. Traditional manual mapping methods are slow, resource-intensive, and often unable to account for localized geographic and structural variations.

The Solution

Omdena developed an AI-assisted mapping tool in partnership with The Humanitarian OpenStreetMap team to efficiently identify building footprints in underserved and disaster-prone areas. This tool leverages AI to create localized, high-accuracy models tailored to specific regional conditions.

1. Data Integration:
   • Aerial Imagery: Utilized high-resolution images from OpenAerialMap for spatial analysis.
   • Building Polygons: Extracted and combined structural data from OpenStreetMap (OSM).
   • Raster and Vector Data: Merged image (raster) and polygon (vector) datasets to enhance model training.
2. AI Modeling:
   • UNet-Based Segmentation: Trained a robust model to extract building footprints from imagery.
   • Localized Fine-Tuning: Adapted the RAMP model to specific regions, achieving high accuracy and relevance to local architecture.
3. Results:
   • Achieved 92% Intersection-over-Union (IoU) in rural areas.
   • Generated geo-referenced, vectorized outputs ready for mapping campaigns.

The Impact

Challenge Highlight

Localized Model Accuracy: Regions with unique architectural styles and environmental features required highly customized training data. By fine-tuning models with localized datasets and testing iteratively, the team ensured reliable and precise outputs for each target area.

6. Streamlining the Identification of Suitable Sites for Solar Panel Installations in UK

The Problem

Urban areas in the UK faced delays in solar adoption due to inefficient manual assessments of rooftops for solar panel suitability.

The Solution

Omdena and Ecosite used Google Solar API and AI-driven image recognition to analyze rooftops for size, structure, and solar potential. Data on rooftop dimensions, materials, and estimated energy output was integrated into a user-friendly dashboard for energy planners.

The Impact

Challenge Highlight

Generalizing the AI model to accommodate diverse urban architectures and rooftop materials required additional training on localized datasets.

Conclusion: Scaling AI-Powered Microgrid Sites through GIS Solutions

These case studies demonstrate how AI-powered microgrid technologies, particularly through effective microgrid site identification, and GIS are revolutionizing rural electrification and solar adoption. By addressing data gaps, automating processes, and optimizing resource allocation, these solutions significantly enhance the efficiency and scalability of energy projects.

Key Takeaways