DiffNILM: A Novel Framework for Non-Intrusive Load Monitoring Based on the Conditional Diffusion Model
Abstract
:1. Introduction
- DiffNILM is the first NILM framework adopting the diffusion model. Specifically, We engineer the conditional diffusion model to address the NILM task, where the total active power and embedded time tags are fed to the model as conditional input, and the appliance power waveform is generated step-by-step from Gaussian noise.
- We propose an encoding method for multi-scale temporal features that takes into account the regularity of power consumption behaviors.
- We implement and evaluate the proposed method on two public datasets, REDD and UKDALE. Empirical results demonstrate that DiffNILM outperforms previous models, as evidenced by both classification metrics and regression metrics.
2. Related Works
3. Denoising Diffusion Probabilistic Models
3.1. The Forward Process
3.2. The Reverse Process
3.3. Training a Diffusion Model
4. Design
4.1. Conditional Diffusion Model as Appliance-Level Data Generator
4.2. Network Architecture
4.3. Training and Sampling Procedures
Algorithm 1: Training. |
1: repeat 2: ${x}_{0}\sim q\left({x}_{0}\right)$ 3: $t\sim Uniform\left(\right\{1,2,\dots ,T\left\}\right)$ 4: $\sqrt{\overline{\alpha}}\sim Uniform\left(\sqrt{{\overline{\alpha}}_{t}},\sqrt{{\overline{\alpha}}_{t-1}}\right)$ 5: $\epsilon \sim \mathcal{N}(\mathbf{0},\mathbf{I})$ 6: Take gradient descent step on ${\nabla}_{\theta}log{\u2225\epsilon -{\epsilon}_{\theta}\left({x}_{\overline{\alpha}},\sqrt{\overline{\alpha}},{x}_{d}\right)\u2225}_{1}$ 7: until converged |
Algorithm 2: Sampling. |
1: ${x}_{T}\sim \mathcal{N}(\mathbf{0},\mathbf{I})$ 2: for $t={T}_{infer},{T}_{infer}-1,\dots ,1$ do 3: $z\sim \mathcal{N}(\mathbf{0},\mathbf{I})$ 4: ${\tilde{\beta}}_{t}=\sqrt{\frac{1-{\overline{\alpha}}_{t-1}}{1-{\overline{\alpha}}_{t}}{\beta}_{t}}$ 5: ${\mu}_{\theta}\left({x}_{\overline{\alpha}},\sqrt{\overline{\alpha}},{x}_{d}\right)=\frac{1}{\sqrt{{\alpha}_{t}}}\left({x}_{t}-\frac{{\beta}_{t}}{\sqrt{1-{\overline{\alpha}}_{t}}}{\epsilon}_{\theta}\left({x}_{\overline{\alpha}},\sqrt{\overline{\alpha}},{x}_{d}\right)\right)$ 6: ${x}_{t-1}={\mu}_{\theta}\left({x}_{t},t\right)+{\tilde{\beta}}_{t}z$ 7: end for 8: return ${x}_{0}$ |
5. Experiments
5.1. Dataset
- Step 1: Merge the data of split-phase mains meter. Two-phase power supply is commonly-used in North American households, so, for REDD, we calculated the sum of each mains meter to obtain the actual aggregated power data.
- Step 2: Resample the power data at a fixed interval of 6 s.
- Step 3: Fill data gaps shorter than 3 min by forward-filling, and fill those longer than 3 min with zeros.
- Step 4: Attach status labels to the datasets. An appliance is classified as being in an ‘on’ state at a particular time point and assigned a status label of 1, provided that its power consumption falls within the acceptable ‘on’ power range and its operation time exceeds the minimum duration specified in Table 1. Otherwise, a status label of 0 is assigned.
- Step 5: Standardize the power data according to Formula (14) to enhance the accuracy of the model and convergence speed.
5.2. Evaluation Metrics
5.2.1. Classification Metrics
5.2.2. Regression Metrics
5.3. Implementation Details
5.4. Results
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Appliance | Reasonable ‘on’ Power Range (W) | Minimum Duration of Operation (s) |
---|---|---|
Microwave | 200∼1800 | 12 |
Washer | 40∼3500 | 1800 |
Dish washer | 50∼1200 | 1800 |
Refrigerator | 50∼400 | 60 |
Symbol | Description | Value |
---|---|---|
L | Length of the input and output power sequences | 480 |
T | Maximum diffusion step | 1000 |
${\beta}_{1:T}$ | Noise schedule | $\mathrm{Linear}\left(1\times {10}^{-6},0.006,1000\right)$ |
${T}_{infer}$ | Inference step | 8 |
${\beta}_{1:{T}_{infer}}$ | Inference noise schedule | $1\times {10}^{-6},2\times {10}^{-6},1\times {10}^{-5},1\times {10}^{-4},1\times {10}^{-3},1\times {10}^{-2},1\times {10}^{-1},9\times {10}^{-1}$ |
N | Number of residual layers | 30 |
C | Number of residual channels | 128 |
n | Length of the dilation cycle | 10 |
Appliance | Model | Accuracy ↑ | F1-Score ↑ | MAE ↓ | MRE ↓ |
---|---|---|---|---|---|
Bi-LSTM | 0.989 | 0.604 | 17.39 | 0.058 | |
CNN | 0.986 | 0.378 | 18.59 | 0.060 | |
Microwave | BERT4NILM | 0.989 | 0.476 | 17.58 | 0.057 |
cGAN | 0.989 | 0.415 | 18.15 | 0.058 | |
DiffNILM | 0.989 | 0.430 | 17.13 | 0.057 | |
Bi-LSTM | 0.989 | 0.125 | 35.73 | 0.020 | |
CNN | 0.970 | 0.274 | 36.12 | 0.042 | |
Washer | BERT4NILM | 0.991 | 0.559 | 34.96 | 0.022 |
cGAN | 0.990 | 0.478 | 29.67 | 0.025 | |
DiffNILM | 0.988 | 0.569 | 26.44 | 0.019 | |
Bi-LSTM | 0.956 | 0.421 | 25.25 | 0.056 | |
CNN | 0.953 | 0.298 | 25.29 | 0.053 | |
Dish washer | BERT4NILM | 0.969 | 0.523 | 20.49 | 0.039 |
cGAN | 0.951 | 0.295 | 24.80 | 0.055 | |
DiffNILM | 0.971 | 0.593 | 18.16 | 0.037 | |
Bi-LSTM | 0.789 | 0.709 | 44.82 | 0.841 | |
CNN | 0.796 | 0.689 | 35.69 | 0.822 | |
Refrigerator | BERT4NILM | 0.841 | 0.756 | 32.35 | 0.806 |
cGAN | 0.811 | 0.732 | 33.83 | 0.820 | |
DiffNILM | 0.868 | 0.794 | 33.58 | 0.808 | |
Bi-LSTM | 0.931 | 0.465 | 30.80 | 0.244 | |
CNN | 0.926 | 0.410 | 28.92 | 0.244 | |
Average | BERT4NILM | 0.948 | 0.579 | 26.35 | 0.231 |
cGAN | 0.935 | 0.498 | 26.70 | 0.240 | |
DiffNILM | 0.954 | 0.597 | 23.70 | 0.230 |
Appliance | Model | Accuracy ↑ | F1-Score ↑ | MAE ↓ | MRE ↓ |
---|---|---|---|---|---|
Bi-LSTM | 0.995 | 0.060 | 6.55 | 0.014 | |
CNN | 0.995 | 0.341 | 6.36 | 0.014 | |
Microwave | BERT4NILM | 0.995 | 0.014 | 6.57 | 0.014 |
cGAN | 0.996 | 0.474 | 5.98 | 0.012 | |
DiffNILM | 0.996 | 0.501 | 4.54 | 0.012 | |
Bi-LSTM | 0.938 | 0.150 | 15.66 | 0.067 | |
CNN | 0.913 | 0.173 | 11.90 | 0.094 | |
Washer | BERT4NILM | 0.966 | 0.325 | 6.98 | 0.040 |
cGAN | 0.959 | 0.376 | 10.84 | 0.062 | |
DiffNILM | 0.986 | 0.390 | 5.74 | 0.058 | |
Bi-LSTM | 0.976 | 0.605 | 36.36 | 0.033 | |
CNN | 0.947 | 0.560 | 25.45 | 0.069 | |
Dish washer | BERT4NILM | 0.966 | 0.667 | 16.18 | 0.049 |
cGAN | 0.961 | 0.646 | 13.89 | 0.042 | |
DiffNILM | 0.980 | 0.662 | 19.58 | 0.030 | |
Bi-LSTM | 0.573 | 0.174 | 43.74 | 0.956 | |
CNN | 0.772 | 0.718 | 29.29 | 0.758 | |
Refrigerator | BERT4NILM | 0.813 | 0.766 | 25.47 | 0.732 |
cGAN | 0.818 | 0.801 | 25.11 | 0.730 | |
DiffNILM | 0.857 | 0.816 | 22.82 | 0.699 | |
Bi-LSTM | 0.994 | 0.531 | 21.26 | 0.007 | |
CNN | 0.997 | 0.850 | 9.64 | 0.003 | |
Kettle | BERT4NILM | 0.998 | 0.907 | 6.82 | 0.002 |
cGAN | 0.998 | 0.911 | 7.09 | 0.002 | |
DiffNILM | 0.999 | 0.918 | 4.59 | 0.002 | |
Bi-LSTM | 0.875 | 0.229 | 21.80 | 0.261 | |
CNN | 0.919 | 0.521 | 14.28 | 0.261 | |
Average | BERT4NILM | 0.943 | 0.503 | 11.47 | 0.194 |
cGAN | 0.946 | 0.642 | 14.58 | 0.170 | |
DiffNILM | 0.964 | 0.657 | 11.45 | 0.164 |
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Sun, R.; Dong, K.; Zhao, J. DiffNILM: A Novel Framework for Non-Intrusive Load Monitoring Based on the Conditional Diffusion Model. Sensors 2023, 23, 3540. https://doi.org/10.3390/s23073540
Sun R, Dong K, Zhao J. DiffNILM: A Novel Framework for Non-Intrusive Load Monitoring Based on the Conditional Diffusion Model. Sensors. 2023; 23(7):3540. https://doi.org/10.3390/s23073540
Chicago/Turabian StyleSun, Ruichen, Kun Dong, and Jianfeng Zhao. 2023. "DiffNILM: A Novel Framework for Non-Intrusive Load Monitoring Based on the Conditional Diffusion Model" Sensors 23, no. 7: 3540. https://doi.org/10.3390/s23073540