CPS: Medium: Federated Learning for Predicting Electricity Consumption with Mixed Global/Local Models

该提案旨在将联邦学习与电力系统集成，利用来自众多设备的分布式数据更好地预测电力消耗并降低发电成本。 我们的目标是利用电力领域越来越普遍的数据源，即每隔15分钟记录一次用电量的智能电表的激增。我们将开发机器学习方法，根据这些数据预测一天前的用电量。这种学习必须在最终用户不愿意与中央机构共享信息的情况下进行。由于电力市场的结构方式，使这些预测比目前的做法更准确，使电力生产成本更低，环境更可持续。我们建议训练用于时间序列预测的递归神经网络，而不与公用事业共享每个用户的完整数据集，而是通过公用事业和消费者之间的重复交互来保护消费者数据的隐私。要实现这一愿景，需要两项科学进步。首先，我们必须开发有效的，样本效率高，快速的“嵌套”联邦学习方法，可以处理同时是本地和全球的模型。我们需要一个全球模型来捕捉家庭用电的共同模式，但我们也需要一个本地模型来捕捉每个家庭的特殊特征。第二个需要的进步是一个从时间序列数据中学习的神经架构，它能够捕获数据中的长期依赖关系。事实上，电力消耗表现出长期的依赖性和人类的行为是复杂的，任何潜在的模式总是被破坏的噪声，不能直接modeled.The发展的方法，在这里提出可以降低整个美国的电力成本。更间接地说，它可以为安装智能电表的计划提供额外的动力，智能电表可以更高级别的粒度测量电力消耗，同时向消费者保证他们的数据是安全的。最后，它可以更容易地引入依赖天气的可再生能源发电，这给预测时空电力供应带来了新的挑战电力公司为适应不确定的可再生能源发电预测而设计的与消费者需求响应激励相关的需求均衡。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响进行评估，被认为值得支持审查标准。

This proposal aims to integrate federated learning with power systems, leveraging distributed data from numerous devices to better predict electricity consumption and lower the cost of generation. Our goal is to take advantage of data sources which are becoming more common in the power domain, namely the proliferation of smart meters which record electricity consumption at 15- minute intervals. We will develop machine learning methods which predict electricity consumption at the day-ahead scale from this data. Such learning must be done with privacy guarantees for end-users who are hesitant to share information with a central authority. Due to the way power markets are structured, making these predictions more accurately than current practice allows electricity to be produced at lower cost and in a more environmentally sustainable way. We propose to train recurrent neural networks for time series prediction without sharing the full data sets from each user with the utility, but rather through repeated interactions between the utility and the consumer which preserve the privacy of consumer data. To make this vision a reality will require two scientific advances. First, we must develop effective, sample efficient, and fast methods for "nested" federated learning which can deal with models that are simultaneously local and global. We need a global model to capture common patterns of electricity consumption across households, but we also need a local model to capture the idiosyncratic features of each household. The second advance required is a neural architecture for learning from time series data which is capable of capturing long-term dependencies in the data. Indeed, electricity consumption exhibits long-term dependencies and human behavior is complex so that any underlying pattern is always corrupted by noise which cannot be modeled directly.The development of the methods proposed here could reduce the cost of electricity throughout the United States. More indirectly, it could provide additional steam to initiatives to install smart meters which can measure electricity consumption at a higher level of granularity, while at the same time assuring consumers that their data is safe. Finally, it could make it easier to introduce weather dependent renewable generation, which creates a new set of challenges for predicting spatiotemporal electricity supply-demand equilibria associated with consumer demand response incentives designed by utilities to adapt to uncertain renewable generation forecasts.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.