Energy Demand Prediction with Optimized Clustering-Based Federated Learning

The rapid growth in pervasive Internet-of-Things (IoT) and Deep Learning (DL) is creating a huge demand for applying DL on IoT systems. However, it is non-trivial to train highly accurate DL models in such scenarios due to the following two challenges: (1) individual IoT devices may not have sufficient training data, and (2) simply combining all sensory data across all devices may cause performance degradation due to data imbalance and varying temporal patterns across different devices. The objective of this paper is to achieve high-accurate prediction models for each device in an IoT system. We propose a federated learning approach for IoT systems driven by trend-based clustering for energy demand prediction for Electric Vehicle (EV) charging station network. We first apply a time-series clustering method to identify stations with similar temporal demand patterns. Using time-series data from stations in a cluster, a single Long-Short Term Memory (LSTM) network is trained using FedAvg algorithm for energy demand prediction for all the stations in the cluster. Experimental results on a real-world energy usage dataset from an EV charging station network show that our proposed approach is very competitive against baseline federated learning approaches. In particular, the energy demand prediction error decreases by 80%.