NON-INTRUSIVE LOAD EXTRACTION OF ELECTRIC VEHICLE CHARGING LOADS FOR EDGE COMPUTING
thesisposted on 01.05.2020 by Hyeonae Jang
In order to distinguish essays and pre-prints from academic theses, we have a separate category. These are often much longer text based documents than a paper.
The accelerated urbanization of countries has led the adoption of the smart power grid with an explosion in high power usage. The emergence of Non-intrusive load monitoring (NILM), also referred to as Energy Disaggregation has followed the recent worldwide adoption of smart meters in smart grids. NILM is a convenient process to analyze composite electrical energy load and determine electrical energy consumption.
A number of state-of-the-art NILM (energy disaggregation) algorithms have been proposed recently to detect various individual appliances from one aggregated signal observation. Different kinds of classification methods such as Hidden Markov Model (HMM), Support Vector Method (SVM), neural networks, fuzzy logic, Naive Bayes, k-Nearest Neighbors (kNN), and many other hybrid approaches have been used to classify the estimated power consumption of electrical appliances from extracted appliances signatures. This study proposes an end-to-end edge computing system with an NILM algorithm, which especially focuses on recognizing Electric Vehicle (EV) charging. This system consists of three main components: (1) Data acquisition and Preprocessing, (2) Extraction of EV charging load via an NILM algorithm (Load identification) on the NILMTK Framework, (3) and Result report to the cloud server platform.
The monitoring of energy consumption through the proposed system is remarkably beneficial for demand response and energy efficiency. It helps to improve the understanding and prediction of power grid stress as well as enhance grid system reliability and resilience of the power grid. Furthermore, it is highly advantageous for the integration of more renewable energies that are under rapid development. As a result, countless potential NILM use-cases are expected from monitoring and identifying energy consumption in a power grid. It would enable smarter power consumption plans for residents as well as more flexible power grid management for electric utility companies, such as Duke Energy and ComEd.