Neural Network-Based Load Identification for Residential Electrical Installations. A Review and an Online Experimental Application-Reference-Cited by-同舟云学术

Neural Network-Based Load Identification for Residential Electrical Installations. A Review and an Online Experimental Application

Published:2023 Issue: Volume: Page:487-499
ISSN:1865-0929
Container-title:Communications in Computer and Information Science
language:
Short-container-title:

Author:

Sonck-Martinez Gerardo Arno,Rodríguez-Mata Abraham Efrain,Medrano-Hermosillo Jesus Alfonso,Baray-Arana Rogelio,Morales-Estrada Efren,Gonzalez-Huitron Victor Alejandro

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-45316-8_31

Reference32 articles.

1. Kundu, A., Juvekar, G.P., Davis, K.: Deep neural network based non-intrusive load status recognition. In: 2018 Clemson University Power Systems Conference (PSC), pp. 1–6. IEEE (2018)

2. Alam, S.M., Ali, M.H.: A new subtractive clustering based ANFIS system for residential load forecasting. In: 2020 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), pp. 1–5. IEEE (2020)

3. Paul, S., Upadhyay, N., Padhy, N.P.: Residential appliance identification using 1-D convolutional neural network based on multiscale sinusoidal initializers. IEEE Trans. Industr. Inform. 18(11), 7444–7453 (2022)

4. Grover, H., Panwar, L., Verma, A., Panigrahi, B.K., Bhatti, T.S.: A multi-head convolutional neural network based non-intrusive load monitoring algorithm under dynamic grid voltage conditions. Sustain. Energy Grids Netw. 32, 100938 (2022)

5. Moradzadeh, A., Mohammadi-Ivatloo, B., Abapour, M., Anvari-Moghaddam, A., Gholami Farkoush, S., Rhee, S.-B.: A practical solution based on convolutional neural network for non-intrusive load monitoring. J. Ambient. Intell. Humaniz. Comput. 12(10), 9775–9789 (2021). https://doi.org/10.1007/s12652-020-02720-6