In this work, we address the problem of providing fast and on-line households appliance load detection in a nonintrusive way from aggregate electric energy consumption data. Enabling on-line load detection is a relevant research problem as it can unlock new grid services such as demand-side management and raises interactivity in energy awareness possibly leading to more green behaviours.To this purpose, we propose an On-line-NILM (Non-Intrusive Load Monitoring) machine learning algorithm combining two methodologies: i) Unsupervised event-based profiling and ii) Markov chain appliance load modelling. The event-based part performs event detection through contiguous and transient data segments, events clustering and matching. The resulting features are used to build household-specific appliance models from generic appliance models. Disaggregation is then performed on-line using an Additive Factorial Hidden Markov Model from the generated appliance model parameters. Our solution is implemented on the cloud and tested with public benchmark datasets. Accuracy results are presented and compared with literature solutions, showing that the proposed solution achieves on-line detection with comparable detection performance with respect to non on-line approaches.

Modern electric distribution systems with emerging operation methods and advanced metering systems bring new challenges to the system analysis, control and management. Interdependency of cyber and physical layers and interoperability of various control and management strategies require wide and accurate test and analysis before field implementation. Real-time simulation is known as a precise and reliable method to support new system/device development from initial design to implementation. However, for the study of different application algorithms, considering the various expertise requirements, the interconnection of multiple development laboratories to a real-time simulation lab, which constitutes the core of an interdepartmental real-time simulation platform, is needed. This paper presents the implemented architecture of such an integrated lab, which serves real-time simulations to different application fields within electric distribution system domain. The architecture is an implementation of an Internet-of-Things to facilitate software inthe-loop (SIL) and hardware in-the-loop (HIL) tests. A demo of the proposed architecture is presented, applied to the testing of a fault location algorithm in a portion of a realistic distribution system model. The implemented platform is flexible to integrate different algorithms in a plug-and-play fashion through a designed communication interface.

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