Utilization of machine‐learning algorithms for wind turbine site suitability modeling in Iowa, USA
Utilization of machine‐learning algorithms for wind turbine site suitability modeling in Iowa, USA
复制标题
利用机器学习算法在美国爱荷华州进行风力涡轮机场地适宜性建模
DOI：
10.1002/we.1723
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发表时间：
2015
期刊：
影响因子：
4.1
通讯作者：
J. Wessling
中科院分区：
文献类型：
--
作者：
A. Petrov;J. Wessling
由于当前从化石燃料转向可再生能源，有必要规划安装新的基础设施以满足对清洁能源的需求。确定风力涡轮机选址适宜性的传统方法会受到专家任意选择标准和模型参数的影响，这可能会导致生成的模型存在一定程度的不确定性。该研究提出了另一种基于经验的方法，用于为爱荷华州建立风力涡轮机选址模型。我们采用传统上用于模拟物种分配的“生态位”原则，为风力涡轮机的放置开发一个新的多标准、空间明确的框架。利用现有涡轮机位置的适宜性条件信息，我们将七个变量（风速、海拔、坡度、土地覆盖、基础设施和定居点的距离以及人口密度）纳入两种机器学习算法[最大熵法（Maxent）和规则集预测遗传算法（GARP）]中，以对适合安装风力涡轮机的区域进行建模。该方法的性能在全州范围和爱荷华州西部的六个县地区进行了测试。 Maxent 和 GARP 确定爱荷华州西北和中北部地区是安装新型风力涡轮机的最佳地点。 Maxent 的变量贡献信息阐明了环境变量的相对重要性及其规模依赖性。它还可以验证有关变量与风力涡轮机适用性之间关系的现有假设。由此产生的模型表现出很高的准确性，并表明所提出的方法是开发风力涡轮机选址应用程序的可能方法。版权所有 © 2014 约翰·威利父子有限公司
Because of the current shift away from fossil fuels and toward renewable energy sources, it is necessary to plan for the installation of new infrastructure to meet the demand for clean energy. Traditional methods for determining wind turbine site suitability suffer from the selection of arbitrary criteria and model parameters by experts, which may lead to a degree of uncertainty in the models produced. An alternative empirically based methodology for building a wind turbine siting model for the state of Iowa is presented in the study. We employ ‘ecological niche’ principles traditionally utilized to model species allocation to develop a new multicriteria, spatially explicit framework for wind turbine placement. Using information on suitability conditions at existing turbine locations, we incorporate seven variables (wind speed, elevation, slope, land cover, distance of infrastructure and settlements, and population density) into two machine-learning algorithms [maximum entropy method (Maxent) and Genetic Algorithm for Rule Set Prediction (GARP)] to model suitable areas for installation of wind turbines. The performance of this method is tested at the statewide level and a six-county region in western Iowa. Maxent and GARP identified areas in the Northwest and North Central regions of Iowa as the optimum location for new wind turbines. Information on variable contributions from Maxent illuminates the relative importance of environmental variables and its scale-dependent nature. It also allows validating existing assumptions about the relationship between variables and wind turbine suitability. The resultant models demonstrate high levels of accuracy and suggest that the presented approach is a possible methodology for developing wind turbine siting applications. Copyright © 2014 John Wiley & Sons, Ltd.