New Design of a Supervised Energy Disaggregation Model Based on the Deep Neural Network for a Smart Grid
Abstract
:1. Introduction
NILM Background
2. Learning Process and Error Function
3. Deep Learning
3.1. Convolutional Neural Networks
3.2. Recurrence Neural Networks
4. D CNN and RNN on NILM
4.1. REDD Dataset and Preprocessing
4.2. Combining CNN and RNN
4.3. Long Short-Term Memory (LSTM)
4.4. Metrics for Evaluating the NILM
- TP (total number of real positives): When both the device and ground truth are ON.
- FP (total number of fake positives): when the device is ON and ground truth is OFF.
- TN (total number of real negatives): when both the device and ground truth are OFF.
- FN (total number of fake negatives): when the device is OFF and ground truth is ON.
- P- Total number of positives on ground truth.
- N- Total number of negatives on ground truth.
4.4.1. Proportion of Total Energy Classified Correctly
4.4.2. Mean Normalized Error
4.4.3. Recall
4.4.4. Precision
4.4.5. Accuracy
4.4.6. F1 Score
4.4.7. Mean Square Error
4.4.8. Categorical Cross-Entropy
5. Houses not Seen During the Training for Testing
6. Results
- Implementation of DL in NILM and its potential for problem solving were examined.
- A combined method was introduced to show the approach of implementing DL methods using a small amount of real data.
7. Conclusions
8. Future Works
Author Contributions
Funding
Conflicts of Interest
References
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Device | Training | Testing |
---|---|---|
Microwave | 1, 3 | 2 |
Dish washer | 1, 3 | 2, 4 |
Refrigerator | 1, 3 | 2, 6 |
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ÇAVDAR, İ.H.; FARYAD, V. New Design of a Supervised Energy Disaggregation Model Based on the Deep Neural Network for a Smart Grid. Energies 2019, 12, 1217. https://doi.org/10.3390/en12071217
ÇAVDAR İH, FARYAD V. New Design of a Supervised Energy Disaggregation Model Based on the Deep Neural Network for a Smart Grid. Energies. 2019; 12(7):1217. https://doi.org/10.3390/en12071217
Chicago/Turabian StyleÇAVDAR, İsmail Hakkı, and Vahid FARYAD. 2019. "New Design of a Supervised Energy Disaggregation Model Based on the Deep Neural Network for a Smart Grid" Energies 12, no. 7: 1217. https://doi.org/10.3390/en12071217