Abstract
Witnessing the importance of energy efficiency and sustainability, there is a definite need for modular, standard-component-driven, vendor-independent energy management systems. A system with real-world scenarios capable of driving practical experiences and incorporating AI enhancements would serve as a base for Energy Community platforms that can include even small energy consumers and producers. A multi-year R&D pilot project was established to create a unique Energy Community model using the industrial environment at ZalaZONE, the Hungarian Vehicle Proving Ground in Zalaegerszeg. The main novelty of the project and the study is the unique setup of parallel R&D work from the academic and practitioner's view and the unique data per second of online, real-time data collection methodology and structure. The EMAK platform uses micro transactional data processing enabled to collect, aggregate, and analyse data every second, build more efficient consumption, and balance models with machine learning enhancements. The onsite research includes three buildings (five building parts) with 200 sensors, 5 data aggregators, energy meters, internal and external heat and humidity meters, and weather stations, measuring electricity consumption, heat, humidity, wind, sun radiation, and more environmental data. That network and the backbone software set measure, aggregate, display, and monitor 867 data points, transmitting 8,600,000 data points daily. Although the project lasts till 31st December 2025, the work has already contributed to several key findings within the ecosystem.
