论文标题
Powergan：使用生成对抗网络综合设备功率特征
PowerGAN: Synthesizing Appliance Power Signatures Using Generative Adversarial Networks
论文作者
论文摘要
非侵入性负载监控（NILM）允许用户和能源提供商仅使用建筑物的智能电表来深入了解家用电器用电消耗。使用大量标记的电器功率数据对尼尔姆的大多数技术进行培训。此类数据的收集具有挑战性，使数据成为创建良好概括性尼尔姆解决方案的主要瓶颈。为了减轻数据限制，我们提出了第一个真正的合成设备功率签名生成器。我们的解决方案Powergan基于有条件的，逐渐生长的1-D WASSERSTEIN生成对抗网络（GAN）。使用Powergan，我们能够合成真正的随机和现实的设备功率数据签名。我们通过使用传统的GAN评估方法（例如Inception评分）来评估Powergan生成的样本以及数值。
Non-intrusive load monitoring (NILM) allows users and energy providers to gain insight into home appliance electricity consumption using only the building's smart meter. Most current techniques for NILM are trained using significant amounts of labeled appliances power data. The collection of such data is challenging, making data a major bottleneck in creating well generalizing NILM solutions. To help mitigate the data limitations, we present the first truly synthetic appliance power signature generator. Our solution, PowerGAN, is based on conditional, progressively growing, 1-D Wasserstein generative adversarial network (GAN). Using PowerGAN, we are able to synthesise truly random and realistic appliance power data signatures. We evaluate the samples generated by PowerGAN in a qualitative way as well as numerically by using traditional GAN evaluation methods such as the Inception score.
