geoskill-wind-resource-assessment

风能资源评估 | Wind Resource Assessment

Performs wind resource assessment on wind-speed time-series rasters, estimating Weibull distribution parameters pixel-by-pixel and computing the Wind Power Density (WPD), for wind farm siting, resource surveying, and energy yield estimation.

Core algorithms:

• Weibull fitting: the wind-speed frequency distribution is described by Weibull(k, c). Two methods are provided: the method of moments (Justus & Mikhail 1978: k ≈ (σ/μ)^-1.086, c = μ/Γ(1+1/k)) and maximum likelihood (scipy.stats.weibull_min MLE).
• Wind power density: WPD = 0.5 × ρ × mean(v³), with ρ the standard air density ≈ 1.225 kg/m³. This is the most direct physical measure of wind resource strength (unit W/m²).
• Power-law height extrapolation: v(z) = v_ref × (z/z_ref)^α, with α ≈ 1/ln(z_ref/z0), extrapolating wind speed observed at 10 m to hub height (default 100 m).
• Annual energy yield estimation: estimates the annual energy production of a single turbine (MWh/yr) from the regional mean WPD and the capacity factor.

The --synthetic mode generates simulated wind-speed fields that follow a Weibull distribution (spatially varying scale parameter c), so the full pipeline and parameter recovery can be validated without network access or real data.

Dependencies / 依赖

pip install 'numpy' 'rasterio' 'scipy'

Usage / 使用方法

基本用法（仅给 bbox，自动合成）

python geoskill-wind-resource-assessment.py --bbox 116.0 39.0 117.0 40.0 --output-dir ./out

Example 1: Synthetic Data Offline Assessment

python geoskill-wind-resource-assessment.py --bbox 116.0 39.0 117.0 40.0 --synthetic --n-dates 100 --output-dir ./out

示例 2：MLE 拟合 + 自定义轮毂高度

python geoskill-wind-resource-assessment.py --bbox 121.0 31.0 122.0 32.0 --synthetic --method mle --height 120 --output-dir ./sh

Example 3: Real Wind Speed Time-Series Raster

python geoskill-wind-resource-assessment.py --input wind_ts.tif --height 100 --roughness 0.1 --output-dir ./real

Example 4: Custom Air Density and Capacity Factor (High Altitude)

python geoskill-wind-resource-assessment.py --input wind_ts.tif --air-density 1.0 --capacity-factor 0.30 --output-dir ./plateau

示例 5：矩估计 vs MLE 对比

python geoskill-wind-resource-assessment.py --bbox 116 39 117 40 --synthetic --method moment --output-dir ./cmp_moment --quiet
python geoskill-wind-resource-assessment.py --bbox 116 39 117 40 --synthetic --method mle --output-dir ./cmp_mle --quiet

Output / 输出

Data Source / 数据源 / Source

• Input: local multi-band wind-speed time-series GeoTIFF (one time step per band)
• Synthetic mode: Weibull(k, c) distributed wind-speed fields generated locally, no external data source

Privacy / 隐私声明 / Privacy

• Fully offline by default; no network requests are made
• --synthetic mode reads no external data
• All computation is done locally; no user data is uploaded

License / License

MIT

---

---

name: geoskill-wind-resource-assessment description: '基于风速时序的风能资源评估：逐像元 Weibull 分布拟合（矩估计/MLE）、风功率密度 WPD=0.5ρmean(v³)、幂律高度外推与年发电量估算。Wind resource assessment from wind-speed time series: per-pixel Weibull fitting, wind power density, power-law height extrapolation, and annual energy yield. 输出平均风速/功率密度/Weibull 参数 GeoTIFF + 参数 JSON + manifest。'

风能资源评估 | Wind Resource Assessment

对风速时序栅格执行风能资源评估，逐像元估计 Weibull 分布参数并计算风功率 密度（Wind Power Density, WPD），用于风电场选址、资源普查和发电量预估。

核心算法：

• Weibull 拟合：风速频率分布用 Weibull(k, c) 描述。提供矩估计 （Justus & Mikhail 1978：k ≈ (σ/μ)^-1.086，c = μ/Γ(1+1/k)）与最大似然 （scipy.stats.weibull_min MLE）两种方法。
• 风功率密度：WPD = 0.5 × ρ × mean(v³)，ρ 为标准空气密度 ≈ 1.225 kg/m³。 这是衡量风能资源强弱最直接的物理量（单位 W/m²）。
• 幂律高度外推：v(z) = v_ref × (z/z_ref)^α，α ≈ 1/ln(z_ref/z0)，把 10 m 观测风速外推到轮毂高度（默认 100 m）。
• 年发电量估算：由区域平均 WPD 与容量系数估算单台机组年发电量（MWh/yr）。

支持 --synthetic 模式生成符合 Weibull 分布的模拟风速场（尺度参数 c 空间变化）， 无需网络和真实数据即可验证全流程与参数恢复。

依赖

pip install 'numpy' 'rasterio' 'scipy'

使用方法

基本用法（仅给 bbox，自动合成）

python geoskill-wind-resource-assessment.py --bbox 116.0 39.0 117.0 40.0 --output-dir ./out

示例 1：合成数据离线评估

python geoskill-wind-resource-assessment.py --bbox 116.0 39.0 117.0 40.0 --synthetic --n-dates 100 --output-dir ./out

示例 2：MLE 拟合 + 自定义轮毂高度

python geoskill-wind-resource-assessment.py --bbox 121.0 31.0 122.0 32.0 --synthetic --method mle --height 120 --output-dir ./sh

示例 3：真实风速时序栅格

python geoskill-wind-resource-assessment.py --input wind_ts.tif --height 100 --roughness 0.1 --output-dir ./real

示例 4：自定义空气密度与容量系数（高海拔）

python geoskill-wind-resource-assessment.py --input wind_ts.tif --air-density 1.0 --capacity-factor 0.30 --output-dir ./plateau

示例 5：矩估计 vs MLE 对比

python geoskill-wind-resource-assessment.py --bbox 116 39 117 40 --synthetic --method moment --output-dir ./cmp_moment --quiet
python geoskill-wind-resource-assessment.py --bbox 116 39 117 40 --synthetic --method mle --output-dir ./cmp_mle --quiet

输出

数据源 / Source

• 输入：本地多波段风速时序 GeoTIFF（每个波段一个时相）
• 合成模式：本地生成 Weibull(k, c) 分布风速场，无外部数据源

隐私声明 / Privacy

• 默认完全离线运行，不发起任何网络请求
• --synthetic 模式不读取任何外部数据
• 所有计算在本地完成，不上传用户数据

License

MIT