论文标题
连续和无分配的概率风力预测：有条件的归一化流量方法
Continuous and Distribution-free Probabilistic Wind Power Forecasting: A Conditional Normalizing Flow Approach
论文作者
论文摘要
我们提出了一种基于条件归一化流量（CNF）的数据驱动的概率风能预测方法。与现有方法相反，这种方法是无分配的（对于非参数和基于分位数的方法），并且可以直接产生连续的概率密度，因此避免了分数交叉。它依赖于基本分布和一组徒图映射。基本分布的形状参数和徒映射均与神经网络近似。基于样条的条件归一流流量由于其非伴随特性而被认为是基于样条的归一化流量。在训练阶段，模型将输入示例依次映射到基本分布的样本，考虑到条件上下文，其中参数是通过最大似然估计的。为了发布概率预测，最终将基本分布的样本映射到所需分布的样品中。基于开放数据集的案例研究验证了所提出的模型的有效性，并允许我们讨论其在最新情况下的优势和警告。
We present a data-driven approach for probabilistic wind power forecasting based on conditional normalizing flow (CNF). In contrast with the existing, this approach is distribution-free (as for non-parametric and quantile-based approaches) and can directly yield continuous probability densities, hence avoiding quantile crossing. It relies on a base distribution and a set of bijective mappings. Both the shape parameters of the base distribution and the bijective mappings are approximated with neural networks. Spline-based conditional normalizing flow is considered owing to its non-affine characteristics. Over the training phase, the model sequentially maps input examples onto samples of base distribution, given the conditional contexts, where parameters are estimated through maximum likelihood. To issue probabilistic forecasts, one eventually maps samples of the base distribution into samples of a desired distribution. Case studies based on open datasets validate the effectiveness of the proposed model, and allows us to discuss its advantages and caveats with respect to the state of the art.
