دانلود مقاله مدیریت سیستم های تهویه هوا در ساختمان های مسکونی
Abstract
1. Introduction
2. Case study
3. Building management system strategy
4. Results
5. Conclusions
References
Abstract
There has been a rising concern in reducing the energy consumption in buildings. Heating, ventilation and air-conditioning system is the biggest consumer of energy in buildings. In this study, management of the air-conditioning system of a building for efficient energy operation and comfortable environment is investigated. The strategy used in this work depends on classifying the rooms to three different groups: very important rooms, important rooms and normal rooms. The total mass flow rate is divided between all rooms by certain percentage using a fuzzy-logic system to get the optimum performance for each room. The suggested Building Management System (BMS) was found capable of keeping errors in both temperature and humidity within the acceptable limits at different operating conditions. The BMS can save the chilled/hot water flow rate and the cooling/heating capacity of rooms.
Introduction
Building Management System (BMS) is a high-technology system installed in buildings that controls and monitors the building’s mechanical and electrical equipment such as air handling, fan-coil unit, cooling plant systems, lighting, power systems, fire systems, and security systems. The objective of a BMS is to achieve more efficient building operation at reduced labor and energy costs and to provide a safer and more comfortable working environment for building occupants [1–6]. Modern buildings and their heating, ventilating and air-conditioning (HVAC) systems are the biggest consumers of energy [7–10]. These buildings are required to be more energy efficient, while considering an ever-increasing demand for better indoor air quality, performance and environmental issues. Building automation systems have a hierarchical structure consisting of field, automation and management layers [10]. Energy management is achieved by means of schemes such as the dutycycling of loads to conserve energy; peak load management to regulate total power consumption during peak hours; scheduled start/stop of building HVAC systems at the beginning and end of each day; and real-time control of building systems in response to occupancy detection [11–17]. However, most of existing supervisory and optimal control strategies are either too mathematical or lacking generality.