India is scaling up solar energy rapidly, but comprehensive and up-to-date information about existing projects is scattered across government portals, company PDFs, press releases, and industry databases. This fragmented data makes it difficult for developers, investors, and planners to:
- Track what solar capacity already exists
- Understand where opportunities or infrastructure gaps lie
- Make informed investment or policy decisions
Build an AI-powered agent that:
- Scrapes, aggregates, and organizes publicly available data to produce a nationwide map of all commercial solar projects in India
- Develops a smart dashboard that visualizes this data to offer a clear picture of the country's solar energy landscape, project by project
- Scrape structured and unstructured data from:
- Government portals (e.g., MNRE, SECI, POSOCO/Grid India)
- Company investor reports and PDFs
- News articles and press releases
- Open satellite imagery / GIS platforms
- Clean, match, and standardize project-level data
- Store in a searchable and filterable database
- Capacity (MW)
- Location (lat/long), project images
- Developer / Owner / Operator
- Year of commissioning
- Type: Utility-scale, Rooftop, Floating, Hybrid (e.g., with storage or wind)
- Cell technology (c-Si, CdTe, etc.)
- Bifacial vs monofacial
- Grid interconnection infrastructure
- Upstream manufacturers
- Offtake agreements (PPA vs merchant market)
- Financing details (if public)
- Historical performance or dispatch data
- Irradiance and grid proximity metrics
- Interactive map of all solar projects in India
- Filters by size, type, developer, commissioning year, and location
- Clickable pop-ups with project-level metadata
- Exportable views for policy briefs or investor pitch decks
- MNRE India – Ministry of New & Renewable Energy
- SECI – Solar Energy Corporation of India
- POSOCO / Grid India – Real-time dispatch & infrastructure
- [Investor Relations PDFs] from Adani, ReNew, Tata Power, Azure
- NSEFI – National Solar Energy Federation of India
- Google Dataset Search – Aggregated datasets
- PDF parsing: pdfplumber, PyMuPDF
- Scraping: BeautifulSoup, Selenium, Scrapy
- Mapping: Mapbox, Leaflet, Plotly Dash
- Database: SQLite or Pandas for prototype; PostgreSQL for scaling
- Optional: Use LangChain for document parsing + GPT for summarization
| Criterion | Goal |
|---|---|
| Data Coverage | Number and diversity of solar projects mapped from multiple sources |
| Extraction Accuracy | Correct parsing of technical and business details |
| Dashboard Clarity & Usability | Easy to filter, explore, and interpret project data |
| Update & Scalability Readiness | Can the system be reused or updated for other regions or technologies? |
| Impact Potential | Helps real users (planners, investors) make better infrastructure decisions |
India is on track to become one of the world's largest solar power producers, but its success hinges on transparency, coordination, and data-driven planning. With a "Solar Detective" AI agent, we can shine light on the entire energy landscape—empowering smart investments, accelerating new project development, and helping India meet its clean energy goals.