Integrating OpenEMS Edge into smart home energy systems

Abstract

The rapid digitalization of residential environments has increased the need for reliable and secure systems capable of managing high-frequency data streams from connected devices. This paper demonstrates the integration of an OpenEMS Edge instance in a smart home use case to enable localized data processing,
encryption, and secure transmission from embedded devices. While previous approaches to home energy management have relied primarily on cloud-centric architectures, recent studies emphasize the advantages of edge computing in reducing latency and preserving user privacy. OpenEMS has been applied in industrial environments, but its adaptation to residential scenarios remains limited. The proposed system employs AESbased encryption at the microcontroller level and FPGA-accelerated decryption to ensure confidentiality of sensor data prior to transmission. Data is forwarded to the OpenEMS Edge instance, which handles integration, persistence, and communication through open standards such as MQTT. A simulation environment, implemented in Python, dynamically updates encrypted sensor readings and validates interoperability with the
OpenEMS data model. The implementation demonstrates low-latency, privacy-preserving data exchange between devices and the energy management layer, confirming the feasibility of secure, real-time control within residential networks. The approach can be extended to broader Internet of Energy applications, offering improved resilience and scalability for smart grid integration. This research contributes to a reproducible opensource framework that combines edge computing, encryption, and standardized communication for secure and
efficient smart home energy management.