Non-intrusive Energy Disaggregation using Signal Unmixing Undergraduate: Philip Wolfe Mentors: Alireza Rahimpour, Yang Song Professor: Dr. Hairong Qi Final Presentation- 7/17/14

Project Description

Why? ◦Increase consumer knowledge ◦Effect change in consumer behavior ◦Save energy ◦Gather valuable data

Data Management and Visualization ◦Given ◦REDD- public dataset for disaggregation research ◦Problems ◦UTC format, unevenly spaced intervals (1, 3, 4 seconds, geographic location ◦house data ~1.5e6, memory management, increased calculation time ◦Evaluating results and data inefficient ◦Solutions ◦Convert to serial time, and time zone- Boston (EST) ◦Vectorization, downsample (1/60 Hz)

Data Management and Visualization ◦Implement GUI to run through indexes and grab desired data ◦Channel mains and device indexing mismatch ◦Day prediction algorithm ◦Variable number channel input handling ◦Visualization GUI to plot each device ◦Checkbox callback channel plotting ◦Timescale axis conversion ◦Color dictionary selection/cycling and Legend ◦Variable number channel input handling

Approach

Supervised Training

Generalization

Results Figure 1. Aggregate House 6, Day 1.

Results Figure 2. Individual Device Comparison. House 6, Day 1.

Results Figure 3. Estimate vs True device consumption

Conclusion

Future Work ◦Optimize computational time ◦Multicore processing ◦Find alternative feature extraction method ◦More powerful computer (currently 2.4GHz) ◦Increase accuracy ◦Use higher sampling rate for calculations (downside computation time) ◦Additional data ◦Manually identify key patterns/features (not optimal) ◦Create specialized signal feature extraction for each device

Questions