Efficient Non-Intrusive Load Monitoring for Energy Savings

The utilization of Non-Intrusive Load Monitoring (NILM) techniques, such as load division and calibration, offers a cost-effective and scalable solution for breaking down aggregate power usage into individual appliances. By providing appliance-specific feedback to end-users, significant energy savings up to 15% can be achieved. This approach leverages the unique signatures of appliances to improve monitoring accuracy and efficiency, making it a practical choice for managing energy consumption in buildings.

• NILM
• Load Monitoring
• Energy Efficiency
• Appliance Feedback
• Energy Savings

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1. INDiC: Improved Non-Intrusive load monitoring using load Division and Calibration Nipun Batra Haimonti Dutta Amarjeet Singh CCLS CCLS 10/3/2024

2. Motivation Buildings contribute significantly to overall energy (electricity, gas, etc.) usage New buildings constructed at rapid rate 100 80 India US UK 60 40 20 0 2

3. Efficacy of appliance specific feedback Providing appliance specific feedback to end users can save upto 15% energy. 3

4. Systems for providing appliance specific feedback Appliance monitors Provide appliance specific information Scale poorly Cost increases with each appliance Intrusive Smart meter Give whole home power information Information must somehow be broken into different appliances Non intrusive Cost effective 4

5. Non Intrusive Load Monitoring (NILM) Breaking down aggregate power observed at meter into different appliances 5

6. Why NILM works? Each appliance has a unique signature This is based on the appliance circuitry Borrowed from Empirical Characterization and Modeling of Electrical Loads in Smart Homes, Barker et. al 6

7. Key Idea I-Load division Different loads are assigned to different mains Smart meter capable of measuring individual mains 7

8. Key Idea I-Load Division Instead of doing NILM on Mains 1+ Mains 2, as done before, perform NILM on both separately Intuition: Separating out independent components Less noise (as noise is distributed too!) More scalable 8

9. Key Idea II- Calibration Different appliance monitors may measure different power for the same appliance 9

10. Key Idea II- Calibration Power change measured by appliance monitor is significantly lesser than the measurement done at mains 10

11. INDiC Calibrate Processed Mains 1 data Apply NILM Raw data Mains 1 data Load division Calibrate Processed Mains 2 data Apply NILM Mains 2 data 11

12. Experiments-I Load Division REDD dataset from MIT Problem complexity almost halved! Overall Mains 1 Mains 2 Dishw asher Stove Kitchen Refrigerator Microwave Lighting 12

13. Experiment II Calibration Unaccounted power or noise reduces after calibration Should improve accuracy After calibration Before calibration 13

14. Combinatorial Optimization (CO) based NILM Take all possible combinations of appliances in different states and match to total power Exponential in number of appliances Load division gives exponential improvements!! Toy example illustrating CO Fan OFF OFF ON ON AC OFF ON OFF ON Total Power (W) 0 1000 200 1200 14

15. Evaluation Metrics Mean Normalized Error (MNE) Normalized error in energy assigned to an appliance Given by ?|????????? ?????? ?????? ??????|/ ?|?????? ??????| RMS Error (RE (Watts)) RMS error in power assigned to an appliance 15

16. [i,j] entry: Number of instances in ith state predicted in jth state Results Refrigerator s accuracy improves significantly Refrigerator Confusion Matrix Without INDiC With INDiC State 1 4541 221 5 State 2 430 4434 44 State 3 98 156 151 State 1 4740 1775 112 State 2 288 2860 63 State 3 41 176 25 State 1 State 2 State 3 State 1 State 2 State 3 16

17. Results -II Both MNE and RE reduce significantly after applying INDiC Appliance Without INDIC MNE (%) With INDiC RE (W) 91 131 64 MNE (%) 25 73 63 RE (W) 67 52 43 Refrigerator 52 Dishwasher 662 Lighting 176 17

18. Acknowledgments TCS Research and Development for supporting Nipun Batra through PhD fellowship NSF-DEITy for project fund 18