A New Unsupervised Event Detector for Non-Intrusive Load Monitoring

Transcription

1 A New Unsupervised Event Detector for Non-Intrusive Load Monitoring GlobalSIP 2015, 14th Dec. Benjamin Wild, Karim Said Barsim, and Bin Yang Institute of Signal Processing and System Theory of,, Germany

2 Introduction Change interval detection in aggregate NILM signals. Motivations: Reliable transient feature extraction. Noise-free space for unsupervised and semisupervised event-based NILM. [1] K. Anderson, A. Ocneanu, D. Benitez, D. Carlson, A. Rowe, and M. Berges, BLUED: a fully labeled public dataset for Event-Based Non-Intrusive load monitoring research, in Proceedings of the 2 nd KDD Workshop on Data Mining Applications in Sustainability (SustKDD), Beijing, China, Aug (2/11) 14 th Dec. 2015

3 Task and tools Task: Real-time harmonic analysis of aggregate current signals. Accurate segmentation of aggregate NILM signals. Addressing time varying loads in event-based NILM. Change detection in harmonic components. High detection sensitivity and more robustness to noise. High dimensional aggregate signals (harmonics). Tools: : IQ-Demodulation Event detection: Kernel Fischer Discriminant Analysis (KFDA) [1] IQ-Demodulation Fischer Discriminant Analysis NILM test dataset: BLUED [2] Suitable for event-based energy disaggregation [1] S. Mika, A. J. Smola and B. Schoelkopf, An improved training algorithm for kernel Fisher discriminants, Proc. AISTATS, pp , [2] K. Anderson, A. Ocneanu, D. Benitez, D. Carlson, A. Rowe, and M. Berges, BLUED: a fully labeled public dataset for Event-Based Non-Intrusive load monitoring research, in Proceedings of the 2 nd KDD Workshop on Data Mining Applications in Sustainability (SustKDD), Beijing, China, Aug (3/11) 14 th Dec. 2015

4 Instantaneous current i n and voltage v n signals v n = i n = H V 2 V k n cos k ω 1 n n + φ k k=1 H I 2 I k n cos k ω 1 n n + φ k k=1 Event detection on current harmonics Changes in higher harmonics may not be observed in the aggregate power signals. P = 1 T න 0 V k ~0 for k > 1 IQ-Demodulation (down-mixing) T IV v n i n dn = V k I k cos φ k H k=1 In-phase: i I n = i n cos k ω line n Quadrature: i Q n = i n sin k ω line n Low-pass filtering: Narrow frequency band around each component. Harmonics components: መI k n = LP i I n 2 + LP i Q n 2 low-pass filtering IQ-Demodulation down mixing Robust to variations in the grid line frequency (50 or 60 Hz). (4/11) 14 th Dec. 2015

5 Input signals: current harmonics θ n = መI 1, መI 3,, መI H The first 11 (odd) harmonic components Step-like change: a change from one steady state to another. mainly suitable for on-off and FSM loads. Active section: a change in the signal from the current steady state. includes intervals caused by time varying loads. The detection problem: a binary test H 0 : steady state (null) H 1 : active section (alternative) Estimate H {H 0, H 1 } Step-like events (5/11) 14 th Dec. 2015

6 Principal Component Analysis (PCA): Unsupervised dimensionality reduction. Maximize intra-class variations. Projection in the direction of the maximum variance. Kernel Fischer Discriminant Analysis (KFDA): Supervised dimensionality reduction. Maximize inter-class separation: S B φ = μ2 φ μ1 φ μ 2 φ μ1 φ T Θ 1 μ 1 Principal Component Analysis Minimize intra-class variation: S W φ = i=1,2 Objective function: ν = arg max v θ Θ i φ φ θ μ i J φ ν = νt S B φ ν ν T S W φ ν φ θ μ i φ T Θ 1 μ 1 Θ 2 μ 2 Fischer Discriminant Analysis The value of J φ ν is a proximity measure between the two distributions and φ is the kernel function. (6/11) 14 th Dec. 2015

7 A main sliding window consisting of two sub-windows: Θ 1 = {θ 1 1, θ 2 1,, θ l1 (1) } Θ 2 = {θ 1 2, θ 2 2,, θ l2 (2) } Fischer Discriminant Analysis [3] : ν = arg max v Gaussian kernel function: Test statistic: where J φ ν = S B φ + γi 1 μ2 φ μ1 φ φ θ i, θ j = exp θ i θ j 2 T = J φ ν H = H 0 H = H 1 Dynamic threshold ξ n : H if T ξ otherwise 2σ 2 ξ n = β std መI i n l 1,, መI i n + l 2 1 i=1 [3] Z. Harchaoui, F. Bach, O. Cappe and E. Moulines, Kernel-based methods for hypothesis testing, IEEE Signal Processing Magazine, pp , 2013 (7/11) 14 th Dec. 2015

8 Post-detection constraints: n end,coarse n start,coarse l 1 + l 2 E d = T n ξ n > λ Refinement: n start,fine = n start,coarse + l 2 n Step-like events: n end,fine = n end,coarse l 1 Min. change in mean values. (8/11) 14 th Dec. 2015

9 : BLUED Test dataset: BLUED [1] Residential, building-level, 2-phase (A,B) power dataset Raw data: current i(n) and voltage v(n) at 12kHz sampling. : IQ-Demodulation First 11 (odd) harmonic components of the current signal. 8 th order IIR low pass filter with f c = 6Hz Event detection: Kernel Fischer Discriminant Analysis (KFDA). Dynamic threshold ξ(n). Post processing (extract only step-like events). Min. difference in real power (8W) [1] K. Anderson, A. Ocneanu, D. Benitez, D. Carlson, A. Rowe, and M. Berges, BLUED: a fully labeled public dataset for Event-Based Non-Intrusive load monitoring research, in Proceedings of the 2 nd KDD Workshop on Data Mining Applications in Sustainability (SustKDD), Beijing, China, Aug (9/11) 14 th Dec. 2015

10 Evaluation metrics: False Positive Percentage (FPP) = FP Precision = Recall = F 1 -Score = TP TP+FP TP TP+FN 2 TP 2 TP+FP+FN E Phase A Phase B # Events # Detections True Positives False Positives False Negatives False Positive Percentage (FPP) 0.33% 14.61% Precision 99.66% 86.32% Recall 98.78% 92.17% F1-score 99.21% 89.15% (10/11) 14 th Dec. 2015

11 Thank you for your attention (10/11) 14 th Dec. 2015