عنوان

Development of Forecasting and Scheduling Methods and Data Analytics Based Controls for Smart Loads in Residential Buildings

شماره

TL56175

زبان متن نوشتاري يا گفتاري و مانند آن

انگلیسی

عنوان اصلي

Development of Forecasting and Scheduling Methods and Data Analytics Based Controls for Smart Loads in Residential Buildings

نام عام مواد

[Thesis]

نام نخستين پديدآور

Alam, S M Mahfuz

نام ساير پديدآوران

Ali, Mohd Hasan

نام ناشر، پخش کننده و غيره

The University of Memphis

تاریخ نشرو بخش و غیره

2020

متن يادداشت

200 p.

جزئيات پايان نامه و نوع درجه آن

Ph.D.

کسي که مدرک را اعطا کرده

The University of Memphis

امتياز متن

2020

متن يادداشت

A smart building is the one that is equipped with automated systems such as lighting, shading, heating, ventilation, and air-conditioning (HVAC), etc., for fulfilling consumers' demand. An efficient load forecasting system helps the building energy management system (BEMS) schedule the loads, operate the energy sources and energy storage systems effectively during peak hours to reduce cost of energy and remove burden on the grids. Furthermore, the load scheduling is a key element for demand side management (DSM) system to actively participate in demand response program. Researchers have been investigating on improved and effective load forecasting and load scheduling methods over the last decade. The conventional methods such as the artificial neural network (ANN) technique needs a lot of previous or historical data for training and learning. Moreover, the correlation between the inputs and output is very crucial for better performance of the ANN methods. Similarly, other conventional methods such as random forest, LSBoosting and long short-term memory (LSTM) have their own drawbacks.In order to overcome the drawbacks and limitations of conventional methods, this dissertation proposes new methods for load forecasting and scheduling. An effective real-time health monitoring system with a view to checking the health condition of all loads and getting a pre-alert before the advent of a disaster or faults is proposed. In addition, a web-based application to be accessed by any smart device such as smart television, mobile phone, etc., is suggested. Moreover, the impact of increased energy consumption during office hours due to COVID-19 pandemic on the local distribution transformer is analyzed. Also, the behind-the-meter (BTM) sources to mitigate the adverse effect of increased load on distribution transformer is proposed.Simulation results performed by MATLAB/Simulink software indicate that the proposed load forecasting and scheduling methods perform better than conventional methods. The proposed health monitoring system is effective and can provide both normal operating and alert messages based on the operating conditions. Moreover, it is found that the load consumption has increased due to the COVID-19 lockdown situations. Finally, the proposed BTM solution can mitigate the adverse effect of increased loads and reduces the transformer loss.

اصطلاح موضوعی

COVID-19

اصطلاح موضوعی

Electrical engineering

مستند نام اشخاص تاييد نشده

Alam, S M Mahfuz

مستند نام اشخاص تاييد نشده

Ali, Mohd Hasan

مستند نام تنالگان تاييد نشده

The University of Memphis

نام الکترونيکي

فرمت انتشار

p

نوع ماده

[Thesis]

کد کاربرگه

276903

سطح دسترسي

a

تكميل شده

Y