Advanced Technologies for Modeling, Optimization and Control of the Future Distribution Grid

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Original Research
30 September 2021
Non-Intrusive Load Monitoring Based on Unsupervised Optimization Enhanced Neural Network Deep Learning
Yu Liu
3 more and 
Pengxiang Tan
Load disaggregation performance of the trained neural network for dataset A: (A) App1, (B) App2, (C) App3, (D) App4, (E) App5, and (F) App6.

Non-intrusive load monitoring has broad application prospects because of its low implementation cost and little interference to energy users, which has been highly expected in the industrial field recently due to the development of learning algorithms. Targeting at the investigation of practical and reliable load monitoring in field implementations, a non-intrusive load disaggregation approach based on an enhanced neural network learning algorithm is proposed in this article. The presented appliance monitoring approach establishes the neural network model following the supervised learning strategy at first and then utilizes the unsupervised learning based optimization to enhance the flexibility and adaptability for diverse scenarios, leading to the improvement of disaggregation performance. By verifications on the REDD public dataset, the proposed approach is demonstrated to be with good performance in non-intrusive load monitoring. In addition to the accuracy enhancement, the proposed approach is also with good scalability, which is efficient in recognizing the newly added appliance.

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