Research on Non-Intrusive Load Recognition Method Based on Improved Equilibrium Optimizer and SVM Model
Abstract
:1. Introduction
2. Home Load Feature Extraction and Data Pre-Processing
3. Load Identification Model
3.1. EO Algorithm
3.1.1. Population Initialization
3.1.2. Establishing an Equilibrium Pool
3.1.3. Concentration Update
3.2. Improved EO Algorithm (IEO)
3.2.1. Bernoulli Chaotic Mapping Sequence Initializes the Population
3.2.2. Segmented Adaptive Factor Dynamic Adjustment Parameters
3.2.3. Perturbation Mechanism Based on Levy Flight
3.3. SVM Classification Model
3.4. Load Identification Algorithm Based on IEO-SVM Model
4. Dataset
4.1. Self-Test Dataset
4.1.1. Raw Data Acquisition
4.1.2. Dataset Production
4.2. Public Dataset
5. Experimental Analysis and Discussion
5.1. Experimental Design and Evaluation Metrics
- (1)
- IEO-SVM: the proposed method in this paper.
- (2)
- EO-SVM: the SVM method is optimized by the original EO algorithm.
- (3)
- SVM: support vector machine
- (4)
- LR: logistic regression
- (5)
- ANN: artificial neural network
- (6)
- DT: decision tree
- (7)
- k-NN: k-nearest neighbor
- (8)
- PSO-SVM: the method based on PSO-SVM used in reference [20].
- (9)
- AlexNet: the method based on the AlexNet deep learning model used in reference [13].
- (10)
- CNN: the novel structural convolutional neural network method used in reference [23].
- (11)
- CNN-LSTM: the method based on the CNN-LSTM deep learning model used in reference [27].
- (1)
- Experiment 1: The proposed IEO algorithm is compared and analyzed with EO and PSO algorithms based on benchmark functions. The superiority of the proposed IEO algorithm is validated using the average of five optimization values and convergence curves of the algorithm.
- (2)
- Experiment 2: The proposed IEO-SVM method is compared with other load identification algorithms using a self-test dataset. The experimental results are analyzed using a confusion matrix and four evaluation metrics (accuracy, precision, recall and F1_score).
- (3)
- Experiment 3: The IEO-SVM method is compared with other methods using a publicly available dataset. The results are analyzed using four evaluation metrics (accuracy, precision, recall and F1_score).
5.2. IEO Algorithm Performance Test
5.3. Analysis of the Results on the Self-Test Dataset
5.4. Analysis of the Results on the Public Dataset
5.5. Feasibility Analysis of the IEO-SVM Method
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Electrical Appliance Category | Electrical Appliance Category |
---|---|
Smartphone | Desktop computer |
Laptop | Tablet PC + Desktop computer |
Induction cooker (standby) | Induction cooker (running) + Microwave oven (running) |
Induction cooker (running) | Induction cooker (running) + Smartphone |
Microwave oven (standby) | Microwave oven (running) + Smartphone |
Microwave oven (running) | Microwave oven (running) + Tablet PC |
Coffee maker | Smartphone + Laptop |
Electrical Appliance Category | Label | Electrical Appliance Category | Label |
---|---|---|---|
Smartphone | 1 | Desktop computer | 8 |
Laptop | 2 | Tablet PC + Desktop computer | 9 |
Induction cooker (standby) | 3 | Induction cooker (running) + Microwave oven (running) | 10 |
Induction cooker (running) | 4 | Induction cooker (running) + Smartphone | 11 |
Microwave oven (standby) | 5 | Microwave oven (running) + Smartphone | 12 |
Microwave oven (running) | 6 | Microwave oven (running) + Tablet PC | 13 |
Coffee maker | 7 | Smartphone + Laptop | 14 |
Electrical Appliance Category | Label |
---|---|
WaterHeater_Daalderop | 1 |
WashingMachine_Privileg | 2 |
VacuumCleaner_Vento | 3 |
VacuumCleaner_Nilfisk | 4 |
RiceCooker_PanasonicSRG06 | 5 |
Fan_VOV-50W | 6 |
LightBulb_Vintage-40W | 7 |
KitchenHood_AmicaUH17051 | 8 |
Kettle_TCM | 9 |
Hairdryer_Valera54206 | 10 |
Function Name and Expression | Dimension | Algorithm | Search Space | Theoretical Optimal Value | The Average of Five Results |
---|---|---|---|---|---|
Sphere: | 30 | EO | [−100, 100] | 0 | 3.2835 × 10−39 |
PSO | 0 | 3.0278 × 10−2 | |||
IEO | 0 | 4.8112 × 10−54 | |||
Schwefel2.22: | 30 | EO | [−10, 10] | 0 | 2.7620 × 10−23 |
PSO | 0 | 1.3831 × 10−1 | |||
IEO | 0 | 2.9701 × 10−32 | |||
Schwefel1.2: | 30 | EO | [−100, 100] | 0 | 6.1848 × 10−8 |
PSO | 0 | 6.6840 × 101 | |||
IEO | 0 | 9.3542 × 10−21 | |||
Schwefel2.21: | 30 | EO | [−10, 10] | 0 | 4.1181 × 10−10 |
PSO | 0 | 1.2619 × 10−2 | |||
IEO | 0 | 1.1893 × 10−18 | |||
Rastrigin: | 30 | EO | [−5.12, 5.12] | 0 | 0 |
PSO | 0 | 3.4332 × 10−3 | |||
IEO | 0 | 0 | |||
Ackley: | 30 | EO | [−32, 32] | 0 | 7.5495 × 10−15 |
PSO | 0 | 1.3490 × 10−1 | |||
IEO | 0 | 3.9968 × 10−15 | |||
Griewank: | 30 | EO | [−600, 600] | 0 | 0 |
PSO | 0 | 5.0186 × 10−2 | |||
IEO | 0 | 0 |
Evaluation Metrics | Accuracy | Precision | Recall | F1_Value |
---|---|---|---|---|
SVM | 79.14% | 81.22% | 79.14% | 78.67% |
LR | 83.79% | 82.28% | 83.78% | 81.49% |
ANN | 88.57% | 88.7% | 88.57% | 88.49% |
DT | 95.07% | 95.81% | 95.07% | 95.16% |
EO-SVM | 96.93% | 97.58% | 96.93% | 96.91% |
PSO-SVM [20] | 97.71% | 98.1% | 97.71% | 97.73% |
k-NN | 98.29% | 98.48% | 98.28% | 98.3% |
CNN [23] | 92.14% | 93.25% | 92.14% | 91.88% |
AlexNet [13] | 70.5% | 64.95% | 70.5% | 68.86% |
CNN-LSTM [27] | 93.21% | 94.1% | 93.2% | 93.15% |
IEO-SVM | 99.43% | 99.44% | 99.42% | 99.43% |
Evaluation Metrics | Accuracy | Precision | Recall | F1_Value |
---|---|---|---|---|
SVM | 89% | 93.95% | 89% | 85.9% |
LR | 70.9% | 66.15% | 70.8% | 65.7% |
ANN | 69.7% | 77.18% | 69.7% | 66.16% |
DT | 100% | 100% | 100% | 100% |
EO-SVM | 94.9% | 96.62% | 94.9% | 94.54% |
PSO-SVM [20] | 95.8% | 97.04% | 95.8% | 95.6% |
k-NN | 100% | 100% | 100% | 100% |
CNN [23] | 81.5% | 75.97% | 81.5% | 77.63% |
AlexNet [13] | 74.7% | 70.37% | 74.7% | 71% |
CNN-LSTM [27] | 98.3% | 98.44% | 98.3% | 98.29% |
IEO-SVM | 100% | 100% | 100% | 100% |
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Wang, J.; Zhang, B.; Shu, L. Research on Non-Intrusive Load Recognition Method Based on Improved Equilibrium Optimizer and SVM Model. Electronics 2023, 12, 3138. https://doi.org/10.3390/electronics12143138
Wang J, Zhang B, Shu L. Research on Non-Intrusive Load Recognition Method Based on Improved Equilibrium Optimizer and SVM Model. Electronics. 2023; 12(14):3138. https://doi.org/10.3390/electronics12143138
Chicago/Turabian StyleWang, Jingqin, Bingpeng Zhang, and Liang Shu. 2023. "Research on Non-Intrusive Load Recognition Method Based on Improved Equilibrium Optimizer and SVM Model" Electronics 12, no. 14: 3138. https://doi.org/10.3390/electronics12143138
APA StyleWang, J., Zhang, B., & Shu, L. (2023). Research on Non-Intrusive Load Recognition Method Based on Improved Equilibrium Optimizer and SVM Model. Electronics, 12(14), 3138. https://doi.org/10.3390/electronics12143138