论文标题
可伸缩的混合分类 - 回归解决方案，用于高频非信息负载监控
Scalable Hybrid Classification-Regression Solution for High-Frequency Nonintrusive Load Monitoring
论文作者
论文摘要
能够监视和控制其净负载（负载和生成之和）的住宅建筑物可以为电网操作员提供宝贵的灵活性。我们提出了一种新颖的多类非感染负载监控（NILM）方法，该方法可以通过最小的额外设备和成本来高频，可在高频上进行有效的净负载监控能力。提出的基于机器学习的解决方案在不依赖事件检测技术的情况下，在更快的时间范围内运行时提供了准确的多类状态预测（能够为美国电力网中使用的每个60 Hz AC周期提供预测）。我们还引入了一种创新的混合分类方法，该方法不仅可以通过分类来预测载荷，还可以通过回归预测单个负载操作能力水平。带有八个住宅用具的测试床用于验证NILM方法。结果表明，总体方法具有很高的准确性，并且质量缩放和概括属性。此外，该方法显示出足够的响应时间（在160ms之内，对应于10个AC周期），以支持与提供网格频率支持服务相关的快速时标的构建网格相互作用控制。
Residential buildings with the ability to monitor and control their net-load (sum of load and generation) can provide valuable flexibility to power grid operators. We present a novel multiclass nonintrusive load monitoring (NILM) approach that enables effective net-load monitoring capabilities at high-frequency with minimal additional equipment and cost. The proposed machine learning based solution provides accurate multiclass state predictions while operating at a faster timescale (able to provide a prediction for each 60-Hz ac cycle used in US power grid) without relying on event-detection techniques. We also introduce an innovative hybrid classification-regression method that allows for the prediction of not only load on/off states via classification but also individual load operating power levels via regression. A test bed with eight residential appliances is used for validating the NILM approach. Results show that the overall method has high accuracy and, good scaling and generalization properties. Furthermore, the method is shown to have sufficient response time (within 160ms, corresponding to 10 ac cycles) to support building grid-interactive control at fast timescales relevant to the provision of grid frequency support services.
