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Title:	Development of an intelligent home energy management system considering demand response scheme
Authors:	Maytham Sabeeh Ahmed (P73312)
Supervisor:	Azah Mohamed, Prof. Dr.
Keywords:	Demand-side management (Electric utilities) Dwellings -- Energy conservation Universiti Kebangsaan Malaysia -- Dissertations Dissertations, Academic -- Malaysia
Issue Date:	6-Jul-2017
Description:	The increasing demand for electricity and the emergence of smart grids have presented new opportunities for home energy management (HEM) systems for the purpose of reducing energy usage. HEM system incorporates demand response (DR)tool that shifts and curtails demand to improve the home energy consumption. It usually creates optimal consumption schedules by considering several factors such as energy costs, environmental concerns, load profiles, and consumer comfort. With the deployment of smart meters, it is possible to perform load control using HEM system with DR enabled appliances. Previous works in scheduling household appliances focused on saving energy and reducing electricity bill without considering customercomfort. Therefore, there is a need to develop an intelligent HEM system that considers DR enabled home appliances, customer comfort and the use of a suitable heuristic optimization technique to determine optimal scheduling of home appliances. The objective of this study is to develop an intelligent HEM system with DR enabled appliances considering Malaysia's environment, home occupancy, and electricity price. To design a valid case study, a survey has been conducted among 384 homeowners in Kajang and Putrajaya for the purpose of determining the home electrical appliances that consume high power consumption. Simulation models of four home appliances, namely, air conditioner (AC), water heater (WH), washing machine (WM), and refrigerator (REF) have been developed based on mathematical and measurement empirical models using the Matlab/Simulink software. In addition, hardware prototype smart sockets have been designed and built to link the home appliances to the HEM controller using the ZigBee type Xbee pro series 2 at the protocol of IEEE 802.15.4. A graphical user interface software has also been developed for the HEM controller. For optimal HEM scheduling of the residential DR strategy, the binary backtracking search algorithm (BBSA) is proposed to determine the optimal on/off operational status of the home appliances. For predicting optimal schedules of the HEM system, a hybrid lighting search algorithm (LSA) based artificial neural network (ANN) has been developed. The survey results showed that many respondents expressed their interest in reducing their electric bill and saving energy in their residential buildings and that the home appliances that consume high power are the AC, WH, WM and REF. The results of the proposed BBSA schedule controller can significantly reduce the peak-hour energy consumption during the DR event by 23 % per day or 5.19 kWh per day at weekday and 27 % per day or 6.6 kWh per day at weekend, considering the four appliances. The BBSA schedule controller is compared with the binary particle swarm optimization (BPSO) schedule controller and the results showed that the BBSA is better than the BPSO in terms of reducing the energy consumption of home appliances. The results for predicting optimal on/off status of the four home appliances showed that the hybrid LSA-ANN gives a mean absolute error (MAE) of 9.128e-09, whereas the hybrid PSO-ANN gives a MAE of 1.195-08. A comparison of results shows that the hybrid LSA-ANN is better than the hybrid PSO-ANN in terms of scheduling household appliances and reducing the peak load. The proposed intelligent HEM system can make accurate decision in scheduling and shifting the domestic load operation at the peak period by scheduling the electrical appliances at a specific time without affecting the comfort of customers.,"Certification of Masters/Doctoral Thesis" is not available,Ph.D.
Pages:	150
Call Number:	TJ163.5.D86A377 2017 3 tesis
Publisher:	UKM, Bangi
Appears in Collections:	Faculty of Engineering and Built Environment / Fakulti Kejuruteraan dan Alam Bina

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