A privacy-preserving edge data aggregation for TinyML energy forecasting in households - Komin, Anton; Boiko, Olha (Technology audit and production reserves, 2025)
The object of this research is the use of tiny machine learning (ML) forecasting models and low-power edge processing as a part of a hybrid energy management system (HEMS) with a particular emphasis on ensuring end-user data privacy and trust. The research addresses the challenge of the collection, aggregation, and processing of sensitive data in smart grid operational modes decision-making tasks. An in-depth literature review revealed that failing to meet user expectations for control and privacy often leads to dissatisfaction and disengagement. This study introduced a complex solution that tries to solve the indicated gap and proposes a prototype of a HEMS data aggregation subsystem designed to supply information to an energy consumption forecasting module based on mobile ML models. The developed LSTM-based household energy consumption forecasting models were converted into CoreML and TensorFlow Lite formats, maintained accuracy with an RMSE of 0.211 kWh, inference time under 0.5 ms, 800 kB size on disk, and up to 20 MB RAM usage. These results confirm their feasibility for deployment in HEMS forecasting subsystems on low-power edge devices. To supply these models with data, a prototype of the HEMS data aggregation system was developed. It uses open-source software (Home Assistant, InfluxDB) and a scalable, privacy-centered container architecture that keeps sensitive data at the edge. Tests on Raspberry Pi 5 (16 GB) showed 97.2% availability over 72 hours, with 12% RAM usage, 18% CPU load, and CPU temperatures of 44–51°C when processing 1440 records per sensor daily. This confirms reliable aggregation with low resource demands and good scalability. Considering the results, the models and prototype can be considered as the sensing and edge computing layers of HEMS, providing the necessary data for operational mode selection in household microgrids.