Non-Intrusive Load Monitoring: A Multi-Agent Architecture and Results

Non-intrusive Load Monitoring (NILM) is a technology that allows the identification of individual electrical loads from a single aggregated measurement of voltage/current, hence, useful for diagnostic of the consumption of electrical energy. This is performed by means of load detection and disaggregation techniques, as there are several different power signatures from the active loads. This paper proposes a multi-agent architecture and evaluates its performance. Four detection methods were selected: Discrete Wavelet Transform (DWT); Kalman Filter; Derivatives, and Half Cycle Active Power. For the power signature recognition agents, different feature extractors and machine learning methods were evaluated: Discrete Fourier Transform, DFT with Exponential Damping, V-I Trajectories, Wavelet and Power Envelope; these were combined with four classifiers: k-Nearest Neighbors, Ensemble Method, Support Vector Machine and Decision Tree. The detection, feature extractors and classifiers methods were tested using waveforms sampled in real situations of the electric network available at the COOL and LIT datasets. The results for the proposed multi-agent architecture indicates improvements in the event detection, lower occurrences of false positives, better feature extractions and better classification results.