Optimizing Federated Learning for Scalable Power-demand Forecasting in Microgrids

TL;DR

This paper enhances federated learning techniques for scalable, privacy-preserving power demand forecasting in microgrids, achieving high accuracy and low training costs across thousands of clients.

Contribution

It introduces optimizations like exponentially weighted loss for federated training, improving prediction accuracy and scalability in large-scale IoT-based power demand forecasting.

Findings

Federated learning outperforms traditional methods like ARIMA and individual DNNs.

Exponential weighting improves model prediction accuracy.

Scalable FL training is feasible with the proposed optimizations.

Abstract

Real-time monitoring of power consumption in cities and micro-grids through the Internet of Things (IoT) can help forecast future demand and optimize grid operations. But moving all consumer-level usage data to the cloud for predictions and analysis at fine time scales can expose activity patterns. Federated Learning~(FL) is a privacy-sensitive collaborative DNN training approach that retains data on edge devices, trains the models on private data locally, and aggregates the local models in the cloud. But key challenges exist: (i) clients can have non-independently identically distributed~(non-IID) data, and (ii) the learning should be computationally cheap while scaling to 1000s of (unseen) clients. In this paper, we develop and evaluate several optimizations to FL training across edge and cloud for time-series demand forecasting in micro-grids and city-scale utilities using DNNs to achieve a high prediction accuracy while minimizing the training cost. We showcase the benefit of using exponentially weighted loss while training and show that it further improves the prediction of the final model. Finally, we evaluate these strategies by validating over 1000s of clients for three states in the US from the OpenEIA corpus, and performing FL both in a pseudo-distributed setting and a Pi edge cluster. The results highlight the benefits of the proposed methods over baselines like ARIMA and DNNs trained for individual consumers, which are not scalable.