Assessing a Measurement-Oriented Data Management Framework in Energy IoT Applications

The Internet of Things (IoT) has enabled the development of various applications for energy, exploiting unprecedented data collection, multi stage data processing, enhanced awareness and control of the physical environment. In this context, availability of tools for efficient development is paramount. This paper explores and validates in the energy field the use of a generic, flexible, open source measurement oriented data collection framework for IoT, namely Measurify. Based on a literature analysis, we have spotted three domains (namely, vehicular batteries, low voltage (LV) test feeder and home energy management system) and defined for each one of them an application (namely: range prediction, power flow analysis and appliance scheduling), to verify the impact given by the use of the proposed IoT framework. We modelled each one of them with Measurify, mapping the energy field items into the abstract resources provided by the framework. From our experience in the three applications, we highlight the generality of Measurify, with straightforward modeling capabilities and rapid deployment time. We thus argue for the importance for practitioners of using powerful big data management development tools to improve efficiency and effectiveness in the life-cycle of IoT applications, also in the energy domain.