geo-infer-energy

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