| Summary: | 碩士 === 國立臺北科技大學 === 自動化科技研究所 === 98 === Traditional load monitoring system requires installations of sensors installed at different loads in order to monitor their operational states. On the other hand, the nonintrusive load monitoring (NILM) system requires installing a current or voltage sensor at the main electrical panel. By analyzing the voltage and current signals, the power usage of each load can be obtained.
In this thesis, an adaptive nonintrusive load monitoring system with an optimization strategy that integrates a new feature extraction method and artificial intelligence recognition technique is proposed. The proposed system is used to monitoring the energizing and de-energizing state of each load by applying k-nearest-neighbor rules and back-propagation artificial neural networks.
Through the experimental tests in different environments, the worst overall correct rate for all scenarios is 88.30%. Additionally, the proposed system can achieve the ability of adaptation by applying artificial immune system. These results show that the proposed system has feasibility, accuracy, and robustness.
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