Towards a Scalable Compute Continuum Platform Applied to Electrical Energy Forecasting

The electricity market is witnessing an increasingly digital transition and market liberalisation. To support this transition and promote market liberalisation, digital platforms employed in the power energy market must incorporate smart services and enable seamless deployment of these services in proximity to smart meters. The emergence of Compute Continuum and Edge-to-Cloud solutions provide a promising avenue in this regard. This paper discusses such a distributed computing continuum architecture and accompanying services for predicting/planning local household as well as a whole microgrid electric consumption/production. We present two compute continuum strategies for load forecasting in electrical grids: (1) a centralised approach, which involves training a model on a centralised server, and (2) a decentralised approach using Federated Learning (FL). The former approach involves centralising data from multiple sources onto a single server, while the latter distributes the training process across edge devices and preserves data privacy and security. In both cases the inference model is deployed on edge devices close to the collected data. Results show that our suggested FL forecasting model offers privacy-preserving advantages compared to non-private centralised models, with a slight trade-off in prediction accuracy.