Design of a Smart Socket Functioned with Electrical Appliance Identification

In order to solve a series of problems of the existing smart socket using intrusive load monitoring method, such as high cost and complicated manufacturing process, this paper applies non-intrusive load monitoring method to the smart socket and proposes a design of a smart socket functioned with electrical appliance identification. Based on two load identification algorithms, namely, relative Euclidean distance algorithm and vector distance algorithm, the smart socket has the ability to learn the electricity parameters of various electrical appliances in the electrical characteristics sample database. Then it can automatically identify the types of the electrical appliances plugged into the smart socket and provide users with electricity information of the electrical appliances, such as voltage, current, active power, power factor, frequency and cumulative power consumption.

[1]  Shirantha Welikala,et al.  Incorporating Appliance Usage Patterns for Non-Intrusive Load Monitoring and Load Forecasting , 2019, IEEE Transactions on Smart Grid.

[2]  Wang Dan,et al.  Review of Non-intrusive Load Appliance Monitoring , 2018, 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC).

[3]  Nikolaos Doulamis,et al.  Bayesian-optimized Bidirectional LSTM Regression Model for Non-intrusive Load Monitoring , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[4]  Douglas P. B. Renaux,et al.  Non-Intrusive Load Monitoring: an Architecture and its evaluation for Power Electronics loads , 2018, 2018 IEEE International Power Electronics and Application Conference and Exposition (PEAC).

[5]  Sachin Kumar Jain,et al.  Non Intrusive Load Monitoring and Load Disaggregation using Transient Data Analysis , 2018, 2018 Conference on Information and Communication Technology (CICT).

[6]  Timo Bernard,et al.  Non-Intrusive Load Monitoring (NILM): Unsupervised Machine Learning and Feature Fusion : Energy Management for Private and Industrial Applications , 2018, 2018 International Conference on Smart Grid and Clean Energy Technologies (ICSGCE).

[7]  Mohammad Shahidehpour,et al.  An Overview of Non-Intrusive Load Monitoring: Approaches, Business Applications, and Challenges , 2018, 2018 International Conference on Power System Technology (POWERCON).

[8]  Bin Li,et al.  A Modified IP-Based NILM Approach Using Appliance Characteristics Extracted by 2-SAX , 2019, IEEE Access.

[9]  Jun Hu,et al.  Convolutional Sequence to Sequence Non-intrusive Load Monitoring , 2018, The Journal of Engineering.

[10]  Xiaodong Liu,et al.  Low-Complexity Non-Intrusive Load Monitoring Using Unsupervised Learning and Generalized Appliance Models , 2019, IEEE Transactions on Consumer Electronics.

[11]  Omar A. Nasr,et al.  New Semi-Supervised and Active Learning Combination Technique for Non-Intrusive Load Monitoring , 2018, 2018 IEEE International Conference on Smart Energy Grid Engineering (SEGE).

[12]  Zhengguang Xu,et al.  A New Non-Intrusive Load Monitoring Algorithm Based on Event Matching , 2019, IEEE Access.

[13]  L. Umanand,et al.  Approach to Non-Intrusive Load Monitoring using Factorial Hidden Markov Model , 2018, 2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS).

[14]  Olivier Sentieys,et al.  Improving NILM by Combining Sensor Data and Linear Programming , 2019, 2019 IEEE Sensors Applications Symposium (SAS).

[15]  Robson Ribeiro Linhares,et al.  Designing a Novel Dataset for Non-intrusive Load Monitoring , 2018, 2018 VIII Brazilian Symposium on Computing Systems Engineering (SBESC).