Using Real-Time Non-Intrusive Load Disaggregation to Evaluate Load Response Potential

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Barati, Peyman | 2023

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 55998 (46)
University: Sharif University of Technology
Department: Energy Engineering
Advisor(s): Rajabi Ghahnavieh, Abbas; Moeini Aghtaie, Moein
Abstract:
In order to actualize the development of sustainable and smart cities, load disaggregation plays such an important role for energy consumption optimization, load response, and distributed energy production network optimization. Researchers have observed that non-intrusive load monitoring is a low-cost method because it doesn't require multiple sensors, but it has issues with algorithm complexity, algorithm variety, and algorithm accuracy dependence on factors like data collection frequency and the type of energy-consuming equipment. In this research, an effort has been made to evaluate how sensitive certain algorithms are to adjustments in the frequency of data collection and the duration of algorithm training for non-intrusive load disaggregation. The practical implementation of non-intrusive load disaggregation in smart cities depends heavily on these two factors
Keywords:
Neural Network ; Edges Detection ; Smart City ; Dispersed Generation ; Demand Response ; Frequency Deviation ; Non-Intrusive Load Disaggregation