Efficient Supervised Machine Learning Network for Non-Intrusive Load Monitoring
- An efficient non-parametric supervised machine learning network (ENSML) was proposed with fast inference speed and low storage requirements. The proposed method was used to create a realistic and adaptable NILM formulation model, with the parameter values following a supervised learning strategy.
- The proposed ENSML has a lowered learning parameter; therefore, it takes up less space while performing as well as other state-of-the-art NILM systems.
- The suggested ENSML methodology with the NILM system could recognise newly installed appliances, filling a critical research need.
- A public dataset was used to validate the provided model and approach. All of the hypothesised potentials have been shown to be genuine, in addition to the high precision of load disaggregation.
2. Literature Review
3. Visualisation of Dataset
4. Efficient Non-Parametric Supervised Machine Learning (ENSML) Network as a Predictive Agent
|Algorithm 1 ENSML Algorithm|
Advantages and Disadvantages
5. Setup Experiment
5.1. ENSML Regression Model for Prediction
5.2. Performance Metrics
Data Availability Statement
Conflicts of Interest
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|1||House 1||20||4 kitchen outlets, 3 lightings, 3 washer dryer, 2 mains, 2 ovens, 1 refrigerator, 1 dishwasher, 1 microwave, 1 electric heat, 1 stove, 1-bathroom.|
|2||House 2||11||2 mains, 2 kitchen outlets, 1 lighting, 1 stove, 1 microwave, 1 washer dryer, 1 refrigerator, 1 dishwasher, 1 disposal.|
|3||House 3||22||5 lightings, 3 unknown outlets, 2 mains, 2 washer dryer, 2 kitchen outlets, 1 electronic, 1 refrigerator, 1 dishwasher, 1 disposal, 1 microwave, 1 furnace, 1 smoke alarm, 1-bathroom.|
|4||House 4||20||4 lightings, 3 air-conditioner, 2 mains, 2-bathroom, 2 kitchen outlets, 1 unknown outlet, 1 washer dryer, 1 stove, 1 smoke alarm, 1 dishwasher, 1 miscellaneous, 1 furnace.|
|5||House 5||26||5 lightings, 4 unknown outlets, 2 mains, 2 washer dryers, 2 subpanel, 2 electric heat, 2 kitchen outlets, 1 microwave, 1 furnace, 1-bathroom, 1 dishwasher, 1 disposal, 1 electronics, 1 refrigerator.|
|6||House 6||17||3 air-conditioner, 2 mains, 2 kitchen outlets, 2 unknown outlets, 1 washer dryer, 1 stove, 1 electronics, 1 electrical heat, 1-bathroom, 1 refrigerator, 1 dishwasher, 1 lighting.|
|1||Kelly, Jack, Knottenbelt, Willian ||LSTM||Work best for two state appliances||Does not perform well when it comes to multi-state appliances such as washing machine and dish washer|
|2||Somchai, Boonyang ||ANN||With incomplete information, the data may still produce output.||Provides a probing solution, but it does not specify the why or how.|
|3||Barsim, Karim Said; Bin Yang ||SSL||It estimates the structure of the unlabelled data from its own predictions rather than relying on additional clustering components for this purpose.||Error propagation occurs when misclassified observations are chosen for an iteration, causing the prediction function to be increasingly skewed in subsequent iterations|
|4||Faustine, Anthony; Pereira, Lucas; Bousbiat, Hafsa and Kulkarni, Shridhar ||DNN||To be able to estimate the prediction’s uncertainty by combining appliance states and power consumption values.||Single target regression, which ignores any correlations between targets, yielding a single model for each.|
|5||Jiang, Jie; Kong, Qiuqiang; Plumbley, Mark D; Gilbert, Nigel; Hoogendoorn, Mark and Roijers, Diederik M ||WaveNet||A reduction in filter sizes is achieved by reducing the size of the convolution filters as compared to conventional CNN(s).||Must minimise the loss with an optimizer with a learning rate of 0.001|
|1||Root Node||This is a sample of an entire population that is divided into two or more homogeneous groups.|
|2||Splitting||A process in which a node is divided into two or more sub-nodes|
|3||Decision Node||In a decision network, each subnode splits into further subnodes|
|4||Leaf/Terminal Node||Nodes that do not split are known as Leaf or Terminal nodes.|
|5||Prunning||Pruning is opposite to splitting. It is removing sub-nodes of a decision node|
|6||Branch/Sub-Tree||An individual branch or sub-tree is a part of an entire tree.|
|7||Parent and Child Node||Usually, the parent node of subnodes is referred to as the parent node, whereas subnodes are its children.|
|1||The model can be applied to both classification and regression.||Prone to overfitting.|
|2||Understanding, interpreting and visualising are easy.||No way to extrapolate.|
|3||There is no constraint on data type.||Regression can be unstable.|
|Contribution||Methods/Techniques||Number of Appliances||Precision [%]|
|||Factorial hidden Markov models||REDD||82|
|||Deep Learning Approach||REDD||76|
|||Back propagation neural network||REDD||45|
|||K-means clustering algorithm||REDD||62|
|||Unsupervised Linear Discrimination Method||REDD||81|
|||CNN binary classifier||private||97|
|||Deep CNN and a KNN classifier||private||93.8|
|Present Work||Efficient Non-parametric Supervised Machine Learning Network||REDD||99.55|
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Hadi, M.U.; Suhaimi, N.H.N.; Basit, A. Efficient Supervised Machine Learning Network for Non-Intrusive Load Monitoring. Technologies 2022, 10, 85. https://doi.org/10.3390/technologies10040085
Hadi MU, Suhaimi NHN, Basit A. Efficient Supervised Machine Learning Network for Non-Intrusive Load Monitoring. Technologies. 2022; 10(4):85. https://doi.org/10.3390/technologies10040085Chicago/Turabian Style
Hadi, Muhammad Usman, Nik Hazmi Nik Suhaimi, and Abdul Basit. 2022. "Efficient Supervised Machine Learning Network for Non-Intrusive Load Monitoring" Technologies 10, no. 4: 85. https://doi.org/10.3390/technologies10040085