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

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.