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Smart city article details
| Title | Reinforced Mcts For Non-Intrusive Online Load Identification Based On Cognitive Green Computing In Smart Grid |
|---|---|
| ID_Doc | 44869 |
| Authors | Jiang Y.; Liu M.; Li J.; Zhang J. |
| Year | 2022 |
| Published | Mathematical Biosciences and Engineering, 19, 11 |
| DOI | http://dx.doi.org/10.3934/mbe.2022540 |
| Abstract | Cognitive green computing (CGC) is widely used in the Internet of Things (IoT) for the smart city. As the power system of the smart city, the smart grid has benefited from CGC, which can achieve the dynamic regulation of the electric energy and resource integration optimization. However, it is still challenging for improving the identification accuracy and the performance of the load model in the smart grid. In this paper, we present a novel algorithm framework based on reinforcement learning (RL) to improve the performance of non-invasive load monitoring and identification (NILMI). In this model, a knowledge base of load power facilities (LPF-KB) architecture is designed to facilitate the load data-shared collection and storage; utilizing deep convolutional neural networks (DNNs) structure based on the attentional mechanism to enhance the representations learning of load features; using RL-based Monte-Carlo tree search (MCTS) method to construct an optimal strategy network, and to realize the online combined load prediction without relying on the prior knowledge. We use the massive experiment on the real-world datasets of household appliances to evaluate the performance of our method. The experimental results show that our approach has remarkable performance in reducing the load online identification error rate. Our model is a generic model, and it can be widely used in practical load monitoring identification and the power prediction system. c 2022 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0) |
| Author Keywords | cognitive green computing; deep learning; identification; load identification; Monte-Carlo search tree; non-invasive load monitoring; reinforcement learning |
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