Empowering Latin American communities with AI-driven tools to transition from fossil fuels to renewable energy solutions.

Many rural and semi-urban communities in Latin America rely on expensive, polluting diesel/oil generators due to unreliable grid access. Transitioning to renewables faces significant challenges:

• Unclear viability: Lack of technical expertise to assess renewable potential
• High upfront costs: Fear of investing without guaranteed returns
• Skill gaps: No local capacity to install/maintain systems
• Information asymmetry: Difficulty accessing reliable data and expert analysis

SoLatAm is an AI-driven platform that empowers communities to transition to renewables through three integrated components:

• Interactive Mapping: Select locations and analyze solar potential with precision
• Terrain Analysis: Evaluate slope, aspect, and other geographical features
• Cost-Benefit Calculator: Project ROI timelines and compare to fossil fuel costs
• NASA Data Integration: Leveraging NASA POWER API for reliable solar irradiance data

• Image Recognition: Upload images of potential installation sites for expert assessment
• Multilingual Assistant: Get answers to technical questions in Spanish and other languages
• Voice Interaction: Use voice commands for accessibility in low-literacy contexts

• Knowledge Exchange: Forums and resource libraries to share experiences
• Local Workforce Marketplace: Connect with trained technicians in your area
• Progress Tracking: Measure community-wide impact and achievements

SoLatAm combines several components:

1. Data Processing Pipeline:

   • NASA POWER API integration for solar irradiance data
   • Geographic terrain analysis (slope, aspect calculations)
   • Energy production potential modeling

2. AI Services:

   • OpenAI-powered multilingual chatbot
   • Computer vision for solar installation analysis
   • Voice recognition for accessibility

3. Interactive Interfaces:

   • Streamlit-based web application
   • Interactive maps with Folium
   • Data visualizations with Matplotlib

Our platform uses advanced algorithms to analyze terrain characteristics, such as slope and aspect, and solar irradiance data to create detailed solar potential maps:

For optimal solar panel installation, this feature computes the ideal tilt angle and direction based on your location’s latitude:

Users can upload images of their sites or existing installations for AI-powered analysis:

• Python 3.8+
• Pip package manager

1. Clone the repository:

git clone https://github.com/marc-herrero/UAB-the-hack25.git
cd UAB-the-hack25