عنوان

Non-intrusive Load Monitoring in Residential Building

شماره

TLpq2272318122

زبان متن نوشتاري يا گفتاري و مانند آن

انگلیسی

عنوان اصلي

Non-intrusive Load Monitoring in Residential Building

نام عام مواد

[Thesis]

نام نخستين پديدآور

Lu, Mengqi

نام ساير پديدآوران

Li, Zuyi

نام ناشر، پخش کننده و غيره

Illinois Institute of Technology

تاریخ نشرو بخش و غیره

2019

نام خاص و کميت اثر

128

جزئيات پايان نامه و نوع درجه آن

Ph.D.

کسي که مدرک را اعطا کرده

Illinois Institute of Technology

امتياز متن

2019

متن يادداشت

Non-Intrusive Load Monitoring (NILM) is an important application to monitor household appliance activities and provide related information to house owner or/and utility company via a single sensor installed at the electrical entry of the house. With this information, utilities can do many tasks such as energy conservation, planning generation more wisely, and demand response (DR) study. For house owners, they can understand their bill more clearly and make monthly budget plan. For researchers, NILM system is a good way to do the energy management in buildings and help to provide power information for smart homes design. Thus, an increasing number of new algorithms have been developed in recent years. In these algorithms, researchers either use existing public datasets or collect their own data which causes such problems as insufficiency of electrical parameters, missing of ground-truth data, absence of many appliances, and lack of appliance information. To solve these problems, this dissertation presents a model-based platform for NILM system development, namely Functional Intrusive Load Monitor (FILM). By using this platform, the state transitions and activities of all the involved appliances can be preset by researchers, and multiple electrical parameters such as harmonics and power factor can be monitored or calculated. This platform will help researchers save the time of collecting experimental data, utilize precise control of individual appliance activities, and develop load signatures of devices. Moreover, event detection, as an important part of event-based NILM methods, has a direct impact on the accuracy of the ultimate load disaggregation results in the entire NILM framework. This dissertation also presents a hybrid event detection approach for relatively complex household load datasets that include appliances with long transients, high fluctuations, and/or near-simultaneous actions. The structure, steps, and working principle of this approach are described in detail. The proposed approach does not require additional information about household appliances, nor does it require any training sets. Case studies on different datasets are conducted to evaluate the performance of the proposed approach in comparison with several existing approaches including log likelihood ratio detector with maxima (LLD-Max) approach, active window-based (AWB) approach, and generalized likelihood ratio (GLR) approach. Results show that the proposed approach works well in detecting events in complex household load datasets and performs better than the existing approaches.

موضوع مستند نشده

Electrical engineering

موضوع مستند نشده

Engineering

مستند نام اشخاص تاييد نشده

Li, Zuyi

مستند نام اشخاص تاييد نشده

Lu, Mengqi

نام الکترونيکي

فرمت انتشار

p

نوع ماده

[Thesis]

کد کاربرگه

276903

سطح دسترسي

a

تكميل شده

Y