GLOBAL BUILDING INTELLIGENCE SYSTEMS

Global Building Intelligence Systems

Table of Contents

Global Building Intelligence Systems3

Introduction3

Literature3

Methodology5

Discussion: Model development11

Architecture11

Development of rule sets13

A Pilot Appraisal15

Conclusions20

References22

Global Building Intelligence Systems

Introduction

Buildings are one of fastest growing energy consuming sectors. It is estimated that amount of energy consumed in Vietnam's local buildings reaches 40-45% of total energy consumption, about two-thirds of which is used in dwellings. In current decade, energy demand of tertiary and residential sectors are increasing 1.2% and 1.0%/annual, respectively. As the result, energy usage in above sectors of Vietnam Company is responsible for approximately 50% of greenhouse gas (GHG) emissions.

In addition, requirements for assurance of necessary thermal comfort, visual comfort and indoor air quality are increased, especially in prevailing situation of price fluctuations, rapid population and technology's evolution. In this context, efforts are currently focused on satisfaction of energy needs for energy efficient buildings, by assuring operational needs with minimum possible energy cost and environmental protection. (Lee 1995)

Towards this direction, role of building energy management systems (BEMS) is known and significant, since these systems can contribute to continuous energy management and therefore to achievement of possible energy and cost savings. The BEMS are generally applied to control of active systems, i.e. heating, ventilation, and air-conditioning (HVAC) systems, while also determining their operating times. In above efforts, performance of BEMS is directly related to amount of energy consumed in buildings and comfort of buildings' occupants. (Dong 1998)

Literature

The majority of recent developments in BEMS have followed advances made in computer technology, telecommunications and information technology. In this context, the number of modern techniques and methods have been proposed in international literature for improving specific systems' controls. To best of our knowledge, techniques for HVAC control, such as pole-placement, optimal regulator and adaptive control and have been presented. More computerized methods, such as genetic algorithms and neural networks have been proposed for control optimization of specific HVAC systems, too. Other methods for optimized building's systems control have, also, been proposed including empirical models, weighted linguistic fuzzy rules, simulation optimization and online adaptive control. Integrated control systems utilizing genetic algorithms, optimized fuzzy controllers for indoor environmental management and occupancy prediction with knowledge-based system have been developed, applied and tested. (Fatima 1998)

In addition, BEMS are currently being developed to be applied in buildings, namely “ intelligent buildings ” and the number of studies have been presented for modern intelligent buildings and control systems, revealing ongoing interest of scientific community on this topic. Following above studies, evident is need for existence of the integrated “decision support model” for management of daily energy operations of the typical building, which can incorporate following requirements in best possible way: (the) guarantee of desirable levels of living quality in all building's rooms and (b) necessity for energy savings. In this context, the intelligent decision support model, that could control how building operational data deviates from settings as well as carry out diagnosis of internal conditions and optimize building's energy operation, is not present in literature. In addition, methods and techniques of rule sets can represent the very ...