Abstract：
Estimates of the land area occupied by wind energy differ by orders of magnitude due to data scarcity and inconsistent methodology. We developed a method that combines machine learning-based imagery analysis and geographic information systems and examined the land area of 318 wind farms (15,871 turbines) in the U.S. portion of the Western Interconnection. We found that prior land use and human modification in the project area are critical for land-use efficiency and land transformation of wind projects. Projects developed in areas with little human modification have a land-use efficiency of 63.8 ± 8.9 W/m2 (mean ±95% confidence interval) and a land transformation of 0.24 ± 0.07 m2/MWh, while values for projects in areas with high human modification are 447 ± 49.4 W/m2 and 0.05 ± 0.01 m2/MWh, respectively. We show that land resources for wind can be quantified consistently with our replicable method, a method that obviates >99% of the workload using machine learning. To quantify the peripheral impact of a turbine, buffered geometry can be used as a proxy for measuring land resources and metrics when a large enough impact radius is assumed (e.g., >4 times the rotor diameter). Our analysis provides a necessary first step toward regionalized impact assessment and improved comparisons of energy alternatives.
摘要：
由于数据稀缺和方法不一致，对风能所占土地面积的估计存在数量级差异。我们开发了一种结合基于机器学习的图像分析和地理信息系统的方法，并检查了西部互联美国部分318个风电场（15871台涡轮机）的土地面积。我们发现，项目区的事先土地利用和人为改造对于风力项目的土地利用效率和土地转化至关重要。在人类改造很少的地区开发的项目的土地利用效率为63.8±8.9W/m2（平均±95％置信区间），土地转化为0.24±0.07m2/MWh，而人类高度改良地区的项目价值为447±49.4W/m2和0.05±0.01m2/MWh，分别。我们证明，风能的土地资源可以用我们的可复制方法一致地量化，一种使用机器学习消除>99%工作量的方法。要量化涡轮机的外围影响，当假设足够大的影响半径时，缓冲几何可以用作测量土地资源和度量的代理(例如，>转子直径的4倍)。我们的分析为区域化影响评估和改进能源替代方案比较提供了必要的第一步。
