Wind is one kind of high-efficient, environmentally-friendly and cost-effective energy source. Wind power, as one of the largest renewable energy in the world, has been playing a more and more important role in supplying electricity. Though growing dramatically in recent years, the amount of generated wind power can be directly or latently affected by multiple uncertain factors, such as wind speed, wind direction, temperatures, etc. More importantly, there exist very complicated dependencies of the generated power on the latent composition of these multiple time-evolving variables, which are always ignored by existing works and thus largely hinder the prediction performances. To this end, we propose DEWP, a novel Deep Expansion learning for Wind Power forecasting framework to carefully model the complicated dependencies with adequate expressiveness. DEWP starts with a stack-by-stack architecture, where each stack is composed of (i) a variable expansion block that makes use of convolutional layers to capture dependencies among multiple variables; (ii) a time expansion block that applies Fourier series and backcast/forecast mechanism to learn temporal dependencies in sequential patterns. These two tailored blocks expand raw inputs into different latent feature spaces which can model different levels of dependencies of time-evolving sequential data. Moreover, we propose an inference block corresponding for each stack, which applies multi-head self-attentions to acquire attentive features and maps expanded latent representations into generated wind power. In addition, to make DEWP more expressive in handling deep neural architectures, we adapt doubly residue learning to process stack-by-stack outputs. Finally, we present extensive experiments in the real-world wind power forecasting application on two datasets from two different turbines to demonstrate the effectiveness of our approach.

翻译：风能是一种高效、环保且经济可行的能源。作为全球最大的可再生能源之一，风能在电力供应中发挥着日益重要的作用。尽管近年来风电装机容量增长迅猛，但风电输出功率可能直接或潜在地受到多种不确定性因素的影响，例如风速、风向、温度等。更重要的是，这些随时间演变的多元变量的潜在组合与输出功率之间存在高度复杂的依赖关系，而现有研究常常忽略这一点，从而极大限制了预测性能。为此，我们提出DEWP——一种新颖的用于风电功率预测的深度扩展学习框架，旨在以充分的表达能力精细建模这种复杂依赖关系。DEWP采用逐堆叠架构，每个堆叠由两部分组成：(i) 变量扩展块，利用卷积层捕获多个变量间的依赖关系；(ii) 时间扩展块，应用傅里叶级数与回测/预测机制学习序列模式中的时间依赖性。这两个定制化模块将原始输入扩展至不同的潜在特征空间，可建模时间演化序列数据中不同层级的依赖关系。此外，我们为每个堆叠设计了推理块，该模块采用多头自注意力机制获取注意力特征，并将扩展后的潜在表示映射为生成的风电功率。为增强DEWP处理深度神经架构的表达能力，我们还引入了双重残差学习来处理逐堆叠输出。最后，我们在两个不同风机的真实风电功率预测数据集上进行了充分实验，验证了本方法的有效性。