Clustering-Based Serverless Edge Computing Assisted Federated Learning for Energy Procurement

Prosumers nowadays are capable of consuming and generating renewable energy along with providing charging services for public electric vehicles (EVs) through EV support equipment (EVSE). However, the energy demand of prosumers and EVs as well as the renewable energy generation of prosumers have uncertain nature, which causes difficulty for each prosumer to purchase the proper energy at a lower price in advance. Thus, it is paramount important to do energy procurement prediction (EPP) for each prosumer. Nevertheless, submitting data from each prosumer to a centralized server for EPP will result in communication delay and need to consume a huge amount of network bandwidth and energy. Therefore, in this paper, a clustering-based serverless edge computing-assisted federated learning (FL) approach is proposed for EPP, where the objective is to minimize the Huber loss between the predicted and the real value per prosumer. In particular, firstly, normalized Laplacian-based spectral clustering is leveraged to group the prosumers with a similar energy procurement pattern to solve the problem of biased energy procurement forecast caused by updating the model among all the clients. Secondly, long short-term memory (LSTM) in the federated learning setting is utilized to train the global model of each clustered group, where the model aggregation occurs in the serverless edge computing ability-enhanced local edge server with the best performance. The evaluation results demonstrate the proposed method can achieve the lowest Huber loss compared with the baseline methods.