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Maytham S. Ahmed
Department of Electrical, Electronic and System Engineering, Faculty of Engineering and Built Environments, Universiti Kebangsaan Malaysia, Bangi, Selangor 43600, Malaysia

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Review
Published: 30 April 2018 in IEEE Access
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The increasing demand for electricity and the emergence of smart grids have presented new opportunities for a home energy management system (HEMS) that can reduce energy usage. The HEMS incorporates a demand response (DR) tool that shifts and curtails demand to improve home energy consumption. This system commonly 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, performing load control using the HEMS with DR-enabled appliances has become possible. This paper provides a comprehensive review on previous and current research related to the HEMS by considering various DR programs, smart technologies, and load scheduling controllers. The application of artificial intelligence for load scheduling controllers, such as artificial neural network, fuzzy logic, and adaptive neural fuzzy inference system, is also reviewed. Heuristic optimization techniques, which are widely used for optimal scheduling of various electrical devices in a smart home, are also discussed.

ACS Style

Hussain Shareef; Maytham S. Ahmed; Azah Mohamed; Eslam Al Hassan. Review on Home Energy Management System Considering Demand Responses, Smart Technologies, and Intelligent Controllers. IEEE Access 2018, 6, 24498 -24509.

AMA Style

Hussain Shareef, Maytham S. Ahmed, Azah Mohamed, Eslam Al Hassan. Review on Home Energy Management System Considering Demand Responses, Smart Technologies, and Intelligent Controllers. IEEE Access. 2018; 6 ():24498-24509.

Chicago/Turabian Style

Hussain Shareef; Maytham S. Ahmed; Azah Mohamed; Eslam Al Hassan. 2018. "Review on Home Energy Management System Considering Demand Responses, Smart Technologies, and Intelligent Controllers." IEEE Access 6, no. : 24498-24509.

Journal article
Published: 05 March 2017 in PRZEGLĄD ELEKTROTECHNICZNY
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ACS Style

Maytham S. Ahmed. A home energy management algorithm in demand response events for household peak load reduction. PRZEGLĄD ELEKTROTECHNICZNY 2017, 1, 199 -202.

AMA Style

Maytham S. Ahmed. A home energy management algorithm in demand response events for household peak load reduction. PRZEGLĄD ELEKTROTECHNICZNY. 2017; 1 (3):199-202.

Chicago/Turabian Style

Maytham S. Ahmed. 2017. "A home energy management algorithm in demand response events for household peak load reduction." PRZEGLĄD ELEKTROTECHNICZNY 1, no. 3: 199-202.

Journal article
Published: 01 March 2017 in Energy and Buildings
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ACS Style

Maytham S. Ahmed; Azah Mohamed; Tamer Khatib; Hussain Shareef; Raad Z. Homod; Jamal Abd Ali. Real time optimal schedule controller for home energy management system using new binary backtracking search algorithm. Energy and Buildings 2017, 138, 215 -227.

AMA Style

Maytham S. Ahmed, Azah Mohamed, Tamer Khatib, Hussain Shareef, Raad Z. Homod, Jamal Abd Ali. Real time optimal schedule controller for home energy management system using new binary backtracking search algorithm. Energy and Buildings. 2017; 138 ():215-227.

Chicago/Turabian Style

Maytham S. Ahmed; Azah Mohamed; Tamer Khatib; Hussain Shareef; Raad Z. Homod; Jamal Abd Ali. 2017. "Real time optimal schedule controller for home energy management system using new binary backtracking search algorithm." Energy and Buildings 138, no. : 215-227.

Conference paper
Published: 01 November 2016 in 2016 International Conference on Advances in Electrical, Electronic and Systems Engineering (ICAEES)
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Electricity demand response and residential load modeling play important roles in the development of home energy management system. Accurate load models are required to produce a load profile at residential level. In this paper, modeling of four load types that include air conditioner, electric water heater, washing machine, and refrigerator are developed considering customer lifestyle and priority by using Matlab/ Simulink. In addition, the home energy management controller is proposed using artificial neural network (ANN) to predict the optimal ON/OFF status of the home appliances. The feedforward neural network type and Levenberg-Marquardt (LM) training algorithm are chosen for training the ANN in the Matlab toolbox. Results showed that the proposed ANN based controller can decrease the energy consumption for home appliances at specific time and can maintain the total household power consumption below its demand limit without affecting customer lifestyles.

ACS Style

Maytham S. Ahmed; Azah Mohamed; Hussain Shareef; Raad Z. Homod; Jamal Abd Ali. Artificial neural network based controller for home energy management considering demand response events. 2016 International Conference on Advances in Electrical, Electronic and Systems Engineering (ICAEES) 2016, 506 -509.

AMA Style

Maytham S. Ahmed, Azah Mohamed, Hussain Shareef, Raad Z. Homod, Jamal Abd Ali. Artificial neural network based controller for home energy management considering demand response events. 2016 International Conference on Advances in Electrical, Electronic and Systems Engineering (ICAEES). 2016; ():506-509.

Chicago/Turabian Style

Maytham S. Ahmed; Azah Mohamed; Hussain Shareef; Raad Z. Homod; Jamal Abd Ali. 2016. "Artificial neural network based controller for home energy management considering demand response events." 2016 International Conference on Advances in Electrical, Electronic and Systems Engineering (ICAEES) , no. : 506-509.

Journal article
Published: 06 September 2016 in Energies
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Demand response (DR) program can shift peak time load to off-peak time, thereby reducing greenhouse gas emissions and allowing energy conservation. In this study, the home energy management scheduling controller of the residential DR strategy is proposed using the hybrid lightning search algorithm (LSA)-based artificial neural network (ANN) to predict the optimal ON/OFF status for home appliances. Consequently, the scheduled operation of several appliances is improved in terms of cost savings. In the proposed approach, a set of the most common residential appliances are modeled, and their activation is controlled by the hybrid LSA-ANN based home energy management scheduling controller. Four appliances, namely, air conditioner, water heater, refrigerator, and washing machine (WM), are developed by Matlab/Simulink according to customer preferences and priority of appliances. The ANN controller has to be tuned properly using suitable learning rate value and number of nodes in the hidden layers to schedule the appliances optimally. Given that finding proper ANN tuning parameters is difficult, the LSA optimization is hybridized with ANN to improve the ANN performances by selecting the optimum values of neurons in each hidden layer and learning rate. Therefore, the ON/OFF estimation accuracy by ANN can be improved. Results of the hybrid LSA-ANN are compared with those of hybrid particle swarm optimization (PSO) based ANN to validate the developed algorithm. Results show that the hybrid LSA-ANN outperforms the hybrid PSO based ANN. The proposed scheduling algorithm can significantly reduce the peak-hour energy consumption during the DR event by up to 9.7138% considering four appliances per 7-h period.

ACS Style

Maytham S. Ahmed; Azah Mohamed; Raad Z. Homod; Hussain Shareef. Hybrid LSA-ANN Based Home Energy Management Scheduling Controller for Residential Demand Response Strategy. Energies 2016, 9, 716 .

AMA Style

Maytham S. Ahmed, Azah Mohamed, Raad Z. Homod, Hussain Shareef. Hybrid LSA-ANN Based Home Energy Management Scheduling Controller for Residential Demand Response Strategy. Energies. 2016; 9 (9):716.

Chicago/Turabian Style

Maytham S. Ahmed; Azah Mohamed; Raad Z. Homod; Hussain Shareef. 2016. "Hybrid LSA-ANN Based Home Energy Management Scheduling Controller for Residential Demand Response Strategy." Energies 9, no. 9: 716.

Journal article
Published: 01 August 2015 in Applied Mechanics and Materials
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The maximum output power of a photovoltaic (PV) system with a DC-DC converter depends mainly on the solar irradiance (G) and the temperature (T). Therefore, a maximum power point tracking (MPPT) mechanism is required to improve the overall system. The conventional MPPT approaches such as the perturbation and observation (P&O) technique have difficulty in finding true maximum power point. Thus various intelligent MPPT systems such as fuzzy logic controllers (FLC) are recently introduced. In FLC based MPPT, selecting the type of the membership function (MF) and the number of the fuzzy sets (FS) is critical for better performance. Thus, in this paper various adaptive neuro fuzzy inference system (ANFIS) is utilized to automatically tune the FLC membership functions instead of adopting the trial and error method. To find suitable MF for FLC, ANFIS is developed in MATLAB/Simulink and the effect of different types MF investigated. Simulation result shows that the FLC with triangular MF and seven FS give the best result. The evaluation indices used in this study includes the maximum extracted energy, minimum standard deviation of the error, and minimum mean square error.

ACS Style

Ammar Hussein Mutlag; Hussein Shareef; Azah Mohamed; Jamal Abd Ali; Maytham S. Ahmed. Performance Evaluation of Various Adaptive Neuro Fuzzy Inference System Based Maximum Power Point Tracking for Photovoltaic System. Applied Mechanics and Materials 2015, 785, 215 -219.

AMA Style

Ammar Hussein Mutlag, Hussein Shareef, Azah Mohamed, Jamal Abd Ali, Maytham S. Ahmed. Performance Evaluation of Various Adaptive Neuro Fuzzy Inference System Based Maximum Power Point Tracking for Photovoltaic System. Applied Mechanics and Materials. 2015; 785 ():215-219.

Chicago/Turabian Style

Ammar Hussein Mutlag; Hussein Shareef; Azah Mohamed; Jamal Abd Ali; Maytham S. Ahmed. 2015. "Performance Evaluation of Various Adaptive Neuro Fuzzy Inference System Based Maximum Power Point Tracking for Photovoltaic System." Applied Mechanics and Materials 785, no. : 215-219.