Maytham S. Ahmed

Smart plug prototype for monitoring electrical appliances in Home Energy Management System

Recently, the technology of Home Energy Management System (HEMS) has expanded for the purpose of reducing energy consumption. This paper presents the development of a smart plug with a wireless Zigbee sensor for measuring power consumption of electrical appliances in the HEMS. Experiments were carried out to evaluate the power consumption of a wireless sensor node in a smart plug using only Zigbee as a microcontroller. Experimental results showed that the smart plug using Zigbee is capable of processing and analyzing the analogue sensor signal with lower power consumption. In addition, the data obtained from the wireless sensor is more accurate and smoother as compared with the data obtained from the oscilloscope. The proposed smart plug has the characteristics of simple design, low cost, low power consumption and easy to control electrical home appliances by switching on/off from the HEMS controller.

Artificial neural network based controller for home energy management considering demand response events

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.

Rule Base Home Energy Management System Considering Residential Demand Response Application

The increasing number of consumer and household appliances causes the rise in home energy use. Therefore, home energy management (HEM) technology is essential to manage and reduce electricity consumption. The objective of this paper is to present an intelligent algorithm for HEM using rule base technique to manage the power consumption with demand response (DR) feature. The scheduling algorithm considers household loads according to the comfort level, customer preference setting and priority of appliance that can be managed at a given time. The algorithm guarantees the total power consumption to be below the electrical demand limit. To exhibit the performance of the proposed HEM, a number of simulations are carried out including DR signal from the network operator. The results show that the algorithm can effectively respond to DR signal, comfort level, customer preference setting and priority of appliance. Furthermore, the algorithm is simple to implement and has flexibility to control the appliances.

A home energy management algorithm in demand response events for household peak load reduction

In the last decades, home energy consumption has increased significantly due to increasing load demand in the residential sector. This paper presents a home energy management (HEM) algorithm to manage the home appliances in a house during a demand response (DR) event. The developed algorithm considers load appliances according to customer preference setting, priority of appliance, and comfortable lifestyle that can be changed at any given time and performs DR at appliance level. The load models are developed based on the operational and physical characteristics for the purpose of DR strategies. Appropriate residential load models are required to support the DR strategies and therefore air conditioner, water heater, electric vehicle and washing machine are chosen as the loads. The proposed HEM algorithm is shown to be effective in managing power consumption at appliances level and can maintain the total household power consumption below its demand limit (DL) without affecting the comfort level. Streszczenie. W artykule predstawiono algorytm do zarządzania konsumpcja energii w gospodarstwach domowych. Algorytm zarządza enegią przy założonym poziomie dopuszczalnego limitu I bazuje na charakterystykach urządzeń podłączonych do sieci. Algorytm zarządzania konsumpcj a enegii w gospodarstwach domowych

Performance Evaluation of Various Adaptive Neuro Fuzzy Inference System Based Maximum Power Point Tracking for Photovoltaic System

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.