Considering the escalating rates of exhaustion of non-renewable energy resources, coupled with the harmful environmental side effects of harnessing them (e.g. damage to public health via air pollution), the need for a near-complete transition to renewable energy production seems inevitable. In recent times, renewable energy production has seen a strong support from investors, governmental initiatives, and industries across the world. Globally installed wind power capacity has seen an increase of 345.24% over the past decade. This increase brings along a need for robust power production management systems having a potential for predicting wind turbine power outputs primarily based on real-time input wind velocities. We propose and compare five optimized robust regression models for forecasting the wind power generated through turbines based on wind velocity vector components, out of which the Extreme Gradient Boosting regression model provided the best results. The forecasted output of our model can be compared with a city’s daily average threshold power requirement in order to make informed decisions about either shutting down an appropriate number of turbines to avoid excessive power production and wastage, or to compensate forecasted shortcomings in production on less windy days via alternative energy generation methodologies.

考虑到不可再生能源的枯竭率不断上升，再加上利用这些有害环境带来的不利影响（例如，由于空气污染对公共健康造成的损害），似乎不可避免地需要向可再生能源生产近乎完全的过渡。近年来，可再生能源生产得到了投资者，政府计划和全球各行业的大力支持。在过去十年中，全球风电装机容量增长了345.24％。这种增加带来了对鲁棒的电力生产管理系统的需求，该系统具有主要基于实时输入风速来预测风力涡轮机功率输出的潜力。我们提出并比较了五个优化的鲁棒回归模型，用于基于风速矢量分量预测涡轮机产生的风力，其中极端梯度加速回归模型提供了最佳结果。我们可以将模型的预测输出与城市的每日平均阈值电力需求进行比较，以便做出明智的决策，以关闭适当数量的涡轮机，以避免过多的电力生产和浪费，或者弥补风力较小时的预测生产缺陷天通过替代能源产生方法。