Abstract
Non-intrusive Load Monitoring (NILM) systems aim at identifying and monitoring the power consumption of individual appliances using the aggregate electricity consumption. Many issues hinder their development. For example, due to the complexity of data acquisition and labeling, datasets are scarce; labeled datasets are essential for developing disaggregation and load prediction algorithms. In this paper, we introduce a new NILM system, called Integrated Monitoring and Processing Electricity Consumption (IMPEC). The main characteristics of the proposed system are flexibility, compactness, modularity, and advanced on-board processing capabilities. Both hardware and software parts of the system are described, along with several validation tests performed at residential and industrial settings.
Highlights
The smart grid concept consists of shifting from a centralized energy generation scheme to a fully-automated and distributed power generation network that provides a two-way flow of electricity and information between utilities and end-users [1]
The main contribution of this paper resides in the development of a new method to investigate electric signals, which is materialized through the functionalities of the proposed Integrated Monitoring and Processing Electricity Consumption (IMPEC) system
Current sensor: a number of split core current transformer with current or voltage output can be used for current measurements
Summary
The smart grid concept consists of shifting from a centralized energy generation scheme to a fully-automated and distributed power generation network that provides a two-way flow of electricity and information between utilities and end-users [1] In this context, it has been shown that providing regular feedback to users about their electricity consumption may help them reduce the electricity bill [2]. It was developed as an alternative to intrusive load monitoring which requires a power meter connected to each and every appliance and to a central metering point to perform the same task of power disaggregation and load monitoring [10] The use of such an approach allows the energy provider and the consumer to gain information inexpensively to make informed decisions and serve as a base for other tasks such as load forecasting [11]
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