A Practical Solution Based on Convolutional Neural Network for Non-Intrusive Load Monitoring

Arash Moradzadeh, Behnam Mohammadi-ivatloo, Mehdi Abapour, Amjad Anvari-Moghaddam, Saeid Gholami Farkoush, Sang-Bong Rhee

Research output: Contribution to journalJournal articleResearchpeer-review

1 Citation (Scopus)
21 Downloads (Pure)


In recent years, the introduction of practical and useful solutions to solve the non-intrusive load monitoring (NILM) as one of the sub-sectors of energy management has posed many challenges. In this paper, an effective and applicable solution based on deep learning called convolutional neural network (CNN) is employed for this purpose. The proposed method with the layer-to-layer structure and extraction of features in the power consumption (PC) curves of each household appliances will be able to detect and distinguish the type of electrical appliances (EAs). Likewise, the load disaggregation for the total home PC will be based on identifying the PC patterns of each EA. To do this, experimental evaluation of reference energy data disaggregation dataset (REDD) related to real-world data and measurement at low frequency is used. The PC curves of each EA are used as input data for training and testing the network. After initial training and testing by the PC data of EAs, the total PC of building obtained from the smart meter are used as input for each network in order to load disaggregation. The trained networks prove to be able to disaggregate the total PC for REDD houses 1, 2, 3, and 4 with a 96.17% mean accuracy. The presented results show the precision and efficiency of the suggested technique for solving NILM problems compared to other used methods.
Original languageEnglish
JournalJournal of Ambient Intelligence and Humanized Computing
Publication statusPublished - 2021


  • Residential load
  • Non-intrusive load monitoring
  • Deep learning
  • Convolutional Neural Network

Fingerprint Dive into the research topics of 'A Practical Solution Based on Convolutional Neural Network for Non-Intrusive Load Monitoring'. Together they form a unique fingerprint.

Cite this