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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.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Geospatial Active Inference framework with 44 modules for ecological, civic, and commercial spatial analysis. Use when working with geospatial data, Active Inference, Bayesian modeling, H3 hexagonal indexing, spatial statistics, or any domain-specific geographic analysis (agriculture, health, economics, risk, climate, energy, transport, marine, forestry, water).
Statistical Parametric Mapping for geospatial data. Use when performing GLM-based spatial analysis, random field theory corrections, cluster-level inference, or neuroimaging-style statistical mapping on geographic datasets.
Ant Colony Optimization and swarm intelligence for geospatial problems. Use when solving spatial optimization with ACO, PSO, ABC algorithms, implementing stigmergic coordination, or optimizing geographic routing and resource allocation with bio-inspired methods.
H3 hexagonal spatial indexing and multi-backend spatial operations. Use when working with H3 cells, spatial indexing, coordinate systems, raster/vector operations, or any spatial backend dispatch (H3, SRAI, PostGIS).
Unified test runner and testing infrastructure for the GEO-INFER ecosystem. Use when running cross-module tests, configuring test categories, setting up test fixtures for spatial data, or analyzing test results.
Generative geospatial art and cartographic visualization. Use when creating artistic map visualizations, generative spatial art, interactive cartographic displays, animation sequences, or aesthetically-focused geographic rendering.
| 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/ |
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
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)
uv run python -m pytest GEO-INFER-ENERGY/tests/ -v