| name | geo-infer-energy |
| description | Energy systems analysis and renewable energy siting. Use when computing LCOE, analyzing energy grid spatial patterns, optimizing renewable energy placement, assessing energy storage, or performing techno-economic analysis of energy projects. |
| prerequisites | {"required":["geo-infer-space","geo-infer-data"],"recommended":["geo-infer-time","geo-infer-bayes"]} |
| difficulty | intermediate |
| estimated_time | 45min |
| examples_dir | ../GEO-INFER-EXAMPLES/examples/ |
GEO-INFER-ENERGY
Instructions
Core Capabilities
- LCOE: Levelized cost of energy calculations for solar, wind, hydro
- Renewable siting: Resource assessment, terrain analysis, constraint mapping
- Grid analysis: Spatial energy grid modeling, load flow, transmission losses
- Techno-economics: NPV, IRR, payback analysis for energy investments
- Emissions: Carbon intensity mapping, reduction pathway scenarios
Key Imports
from geo_infer_energy.core.lcoe import LCOECalculator
from geo_infer_energy.core.renewable_siting import RenewableSiteSelector
from geo_infer_energy.core.grid_analysis import GridAnalyzer
from geo_infer_energy.core.techno_economics import TechnoEconomicModel
Examples
from geo_infer_energy.core.renewable_siting import RenewableSiteSelector
selector = RenewableSiteSelector(technology="solar")
candidates = selector.evaluate(
solar_irradiance=ghi_raster,
terrain=dem,
constraints={"slope_max": 15, "distance_from_grid_km": 10}
)
optimal_sites = selector.rank(candidates, n_top=5)
Guidelines
- LCOE benchmarking in development (Alpha)
Integrations
- Integrates with CLIMATE for renewable resource projections
- Integrates with SPACE for spatial optimization grid
- Test:
uv run python -m pytest GEO-INFER-ENERGY/tests/ -v
