Κοτσιλίτης Σαράντης

Περίληψη:
Energy systems monitoring is a key factor in reducing global energy consumption. Although intrusive approaches can be adopted, Non-Intrusive Load Monitoring (NILM), which targets estimation of individual-appliance consumption from aggregated electricity measurements, is the main choice for retrofitting the plethora of non-smart electrical installations. Contemporary NILM requirements include high-frequency power-line sampling, efficient disaggregation algorithms that can, potentially, run locally, on the monitoring device, to offload the remote fog/cloud infrastructure, and various open datasets to allow the development of such algorithms. Towards all these directions, in this paper, a versatile monitoring device to support NILM research is presented. It offers high sampling frequency, sufficiency of local resources, and has been designed to be used in different environments (residential, commercial, industrial). Actually, it was deployed in all these environments, with multiple copies of it being used in two different industrial facilities for over two years. The developed device’s cost has been kept low, its accuracy has been experimentally verified to be sufficiently high, while its key hardware characteristics, in the context of NILM research, compare favorably with those of other devices in the literature. Along with the device, a residential, high-frequency dataset is also presented and made publicly available.

Περίληψη:
Non-Intrusive Load Monitoring (NILM) aims to determine individual-appliance energy consumption with minimum cost, by decomposing aggregated electricity measurements. Although important for achieving energy conservation and cost minimization, NILM requires high-frequency sampling rates to provide accurate results. This requirement significantly increases the need for storage and computational resources in the electric utility’s fog/ cloud infrastructure, and for bandwidth on the customer’s side. To resolve these issues, on-site disaggregation, i.e., on the monitoring device, can be employed. However, to keep device-cost low, lightweight NILM algorithms are needed. To this end, a lightweight event-detection algorithm designed to ease on-site implementation, on either software or hardware, is proposed. Event detection is the first, critical half of the well-established event-based NILM approach; it identifies appliance state changes (events). Although a few lightweight event-detection techniques, utilizing high-frequency data, have been presented in the literature, their performance is relatively low in complex-load cases. The proposed algorithm utilizes simple-to-compute features and employs multiple simple criteria to declare an event as detected, and slope-coefficient inspection to identify steady states. Moreover, it can detect events with very small time difference between them. Comparisons show that its performance is superior even against more complex event-detection approaches, while its low computational cost is also verified.