Raad Z. Homod

Review on the HVAC System Modeling Types and the Shortcomings of Their Application

The modeling of the heating, ventilation, and air conditioning (HVAC) system is a prominent topic because of its relationship with energy savings and environmental, economical, and technological issues. The modeling of the HVAC system is concerned with the indoor thermal sensation, which is related to the modeling of building, air handling unit (AHU) equipments, and indoor thermal processes. Until now, many HVAC system modeling approaches are made available, and the techniques have become quite mature. But there are some shortcomings in application and integration methods for the different types of the HVAC model. The application and integration processes will act to accumulate the defective characteristics for both AHU equipments and building models such as nonlinear, pure lag time, high thermal inertia, uncertain disturbance factors, large-scale systems, and constraints. This paper shows types of the HVAC model and the advantages and disadvantages for each application of them, and it finds out that the gray-box type is the best one to represent the indoor thermal comfort. But its application fails at the integration method where its response deviated to unreal behavior.

Modeling of heat and moisture transfer in building using RLF method

This study is concerned with effectiveness of building internal temperature and relative humidity by ventilation and infiltration flow rate. Building model is inevitable to study the feasibility of building ventilation, and how to affect on indoor air quality. The empirical method which used in building model is a residential load factor (RLF). RLF formed to calculate cooling/heating load depend upon indoor/outdoor temperature. The transparency, functionality of indoor/outdoor temperatures and simplicity of RLF make it suitable for use in this model. Furthermore the parameters of model can be calculated room by room and thats proper for variable air volume (VAV). Today a VAV system is universally accepted as means of achieving energy efficient and comfortable building environment. The model what we get verified with different method, by manual or software program calculation.

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.

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

Dynamic indoor thermal comfort model identification based on neural computing PMV index

This paper focuses on modelling and simulation of building dynamic thermal comfort control for non-linear HVAC system. Thermal comfort in general refers to temperature and also humidity. However in reality, temperature or humidity is just one of the factors affecting the thermal comfort but not the main measures. Besides, as HVAC control system has the characteristic of time delay, large inertia, and highly nonlinear behaviour, it is difficult to determine the thermal comfort sensation accurately if we use traditional Fangers PMV index. Hence, Artificial Neural Network (ANN) has been introduced due to its ability to approximate any nonlinear mapping. Using ANN to train, we can get the input-output mapping of HVAC control system or in other word; we can propose a practical approach to identify thermal comfort of a building. Simulations were carried out to validate and verify the proposed method. Results show that the proposed ANN method can track down the desired thermal sensation for a specified condition space.