论文标题
风力曲线建模中应用的时间过度拟合问题
The temporal overfitting problem with applications in wind power curve modeling
论文作者
论文摘要
本文涉及一个非参数回归问题，其中输入变量和错误随时间相关。研究的动机源于对风力曲线进行建模。使用现有的模型选择方法，例如交叉验证，在存在时间自相关的情况下导致模型过度拟合。这种现象被称为时间过度拟合，这会导致性能丧失，同时预测与训练时间域不同的时间域的响应。我们提出了一种基于高斯工艺（GP）的方法来解决时间过度拟合问题。我们的模型分为两个部分 - 时间不变的组件和一个随时间变化的组件，每个组件都是通过GP建模的。我们将推理方法修改为基于稀疏的策略，这是从马尔可夫链蒙特卡洛采样借用的想法，以克服时间过度拟合并估算时间不变的组件。我们将我们提出的方法与现有的功率曲线模型和可用的想法进行了广泛的比较，以处理真实风力涡轮机数据集的时间过度拟合。当预测与培训时间段不同的时间段的响应时，我们的方法会产生显着改善。本文的补充材料和计算机代码可在线获得。
This paper is concerned with a nonparametric regression problem in which the input variables and the errors are autocorrelated in time. The motivation for the research stems from modeling wind power curves. Using existing model selection methods, like cross validation, results in model overfitting in presence of temporal autocorrelation. This phenomenon is referred to as temporal overfitting, which causes loss of performance while predicting responses for a time domain different from the training time domain. We propose a Gaussian process (GP)-based method to tackle the temporal overfitting problem. Our model is partitioned into two parts -- a time-invariant component and a time-varying component, each of which is modeled through a GP. We modify the inference method to a thinning-based strategy, an idea borrowed from Markov chain Monte Carlo sampling, to overcome temporal overfitting and estimate the time-invariant component. We extensively compare our proposed method with both existing power curve models and available ideas for handling temporal overfitting on real wind turbine datasets. Our approach yields significant improvement when predicting response for a time period different from the training time period. Supplementary material and computer code for this article is available online.
