Abstract
A multi-agent architecture for a Non-Intrusive Load Monitoring (NILM) solution is presented and evaluated. The underlying rationale for such an architecture is that each agent (load event detection, feature extraction, and classification) outperforms others of the same type in particular scenarios; hence, by combining the expertise of these agents, the system presents an improved performance. Known NILM algorithms, as well as new algorithms, proposed by the authors, were individually evaluated and compared. The proposed architecture considers a NILM system composed of Load Monitoring Modules (LMM) that report to a Center of Operations, required in larger facilities. For the purposed of evaluating and comparing performance, five load event detect agents, five feature extraction agents, and five classification agents were studied so that the best combinations of agents could be implemented in LMMs. To evaluate the proposed system, the COOLL and the LIT-Dataset were used. Performance improvements were detected in all scenarios, with power-ON and power-OFF detection improving up to 13%, while classification accuracy improved up to 9.4%.
Highlights
Non-Intrusive Load Monitoring (NILM) is a technique based on centralized measuring of electrical energy consumption and, by a disaggregation process, determining the individual consumption of each electrical load
LIT-Dataset were used: the Synthetic (LIT-SYN)-1 to LIT-SYN-8: Models are trained with individual loads from LIT-SYN-1 and evaluated with data from LIT-SYN-8; LIT-SYN-3 to LIT-SYN-8: Models are trained with individual loads from LIT-SYN-3 and evaluated with data from LIT-SYN-8
Concerning the multi-agent architecture, its rationale is to combine the expertise of several event detection agents into a single result; likewise, the expertise of several load classification agents is combined into a single result
Summary
Non-Intrusive Load Monitoring (NILM) is a technique based on centralized measuring of electrical energy consumption and, by a disaggregation process, determining the individual consumption of each electrical load. NILM uses a database of known power signatures of devices to analyze the aggregated power consumption and identify individual loads’ contributions. NILM is a low cost, deployed, flexible and viable solution that provides consumers with detailed information about their energy consumption [1]. NILM provides essential information for use in Smart Grids, in Energy. Management Systems and for Energy Efficiency Initiatives, justifying the current global research effort on the subject. NILM may well become a widespread diagnostic and energy management solution, in the context of Electrical Efficiency, available to every end-user. It identifies energy waste and improper use; for energy management, it may be used by residential users and by commercial/industrial users
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