Construction, application and validation of selection evaluation model (SEM) for intelligent HVAC control system

Abstract

Design teams are confronted with the quandary of choosing apposite building control systems to suit the needs of particular intelligent building projects, due to the availability of innumerable ‘intelligent’ building products and a dearth of inclusive evaluation tools. This paper is organised to develop a model for facilitating the selection evaluation for intelligent HVAC control systems for commercial intelligent buildings. To achieve these objectives, systematic research activities have been conducted to first develop, test and refine the general conceptual model using consecutive surveys; then, to convert the developed conceptual framework into a practical model; and, finally, to evaluate the effectiveness of the model by means of expert validation. The results of the surveys are that ‘total energy use’ is perceived as the top selection criterion, followed by the ‘system reliability and stability’, ‘operating and maintenance costs’, and ‘control of indoor humidity and temperature’. This research not only presents a systematic and structured approach to evaluate candidate intelligent HVAC control system against the critical selection criteria (CSC), but it also suggests a benchmark for the selection of one control system candidate against another.

Introduction

Few would dispute that there has been widespread implementation of intelligent building technologies in many building developments over the past decade or so, and that this trend has been particularly notable in certain Asian cities as building developers desire to create product differentiation and to project their ‘signature’ building image by creating highly integrated and intelligent buildings [1]. The desire for an effective and supportive environment within which an organisation can reduce energy consumption, improve worker productivity, and promote maximum profitability for their own business has further stimulated the growth of highly adaptable and responsive buildings [2]. Consequently, intelligent buildings have been advocated as a building form that helps to promote an environment that maximises the effectiveness of its end-users and facilitates the efficient management of resources [3].

Recent years have witnessed a variety of intelligent building control products developed and introduced into the market, designed to enhance building ‘intelligence’ performance and environmental sustainability, and to satisfy a variety of human needs. These are designed to provide environmental control, mobility, communications, facilities, fire protection and security within the building. Each of the control systems plays a dominant role in the building as it acts as the balance between the organisations and services that jointly determine if the value objectives of developers or end-users are obtained [2]. The buildings are designed to enable all the individual systems to interrelate in a natural way, thus allowing for interaction between the systems and the control of the systems [2], [3]. These control systems have to be able to respond flexibly to changing conditions and user requirements throughout the lifespan of the intelligent building. If the systems become unserviceable due to breakdowns, lack of control, misuse, ineffective maintenance, human discomfort and so on, this negatively affects business operations, and end-users may turn to other buildings which are able to fulfil their requirements or offer them more sophisticated services. The costs associated with system maintenance and the potential plunge in revenue arising from a loss of tenants will eventually have an adverse effect on the financial viability of the building [2]. As a result, the inability to match end-users' or developers' expectations may lead to disenchantment and a serious decline in interest and confidence in the intelligent building [4]. It is for this reason that the meticulous selection of building control systems is one of the most important decisions to be made in the creation of an efficient and well-performing intelligent building.

A challenge to project design teams is posed by the plethora of intelligent building control products that have become available over the last decade. Project design teams need to choose the optimum amalgamation of technologies and features from available building control system packages to form a configuration that meets or exceeds the expectations of developers and end-users, as well as the unique requirements of development projects [5]. The complexities of selection decisions are further exacerbated by the high aggregation of the multi-criteria and multi-dimensional perspectives of building performance; including user friendliness, international standard protocols, business and commercial needs of end-users, ability of multiple systems integration, energy-saving properties, technological advancement, scalability, future proofing, and system flexibility [1]. As a result, design teams need to strike a balance between these considerations and the goals and expectations of the people paying for and/or intending to occupy the building [5]. With such increasing complexities involved in the evaluation and selection of the building control systems for intelligent buildings, the need for good decision-making and selection evaluation tools is recognised.

In contemporary buildings, the heating, ventilation and air-conditioning (HVAC) system is usually a critical service, which provides a comfortable indoor environment for people to live and work [6]. The HVAC system has a significant impact on the external environment as it consumes energy to maintain a comfortable and healthy internal environment [2]. Research on building energy usage found that HVAC systems alone generally account for between 25 to 30% of the total building energy usage [7]. The study of So and Chan [6] found the HVAC system consumes up to 50% of the total electricity consumption of a building. This suggests that good holistic control of the HVAC system is critical. In recent years, although efforts have been made by scholars in investigating the significance of environmental and user comfort factors (i.e. thermal comfort) in the consideration of the intelligent HVAC control system (for example, Djuric et al. [8]; Zheng and Zaheer-Uddin, [9]; Wang and Xu [10]), existing analytical methods and techniques pay most attention to the financial aspects of system selection [11]. Insufficient attention has been paid to criteria such as human comfort, environmental sustainability and building flexibility, which are not easily expressed or quantified. In many cases, advanced building systems that prioritise cost savings are generally chosen, which may lead to a biased selection process. A review of intelligent building literature indicates that even fewer studies have been conducted to understand the factors or criteria of building control system selection in conjunction with the development of a selection evaluation model to ascertain their suitability [11]. These knowledge gaps and practical deficiencies have in the past prevented practitioners from selecting appropriate building control systems. They have not been able to access a comprehensive list of criteria to evaluate building control systems, and there has also been a lack of a rational and systematic approach to facilitate the selection of appropriate building control systems. The lack of research into the process of building control systems selection and the resulting inefficiency of the selection evaluation approach would possibly lead to a less than optimal selection of building control system candidates, which might fail to satisfy the expectations of developers or end-users.

With the limitations and deficiencies of the current research in mind, the purpose of this paper is to develop, apply and validate the proposed selection evaluation models for the control system of the commercial intelligent building. This research is developed from the earlier works in Wong and Li [12], [13], [15]. The study of Wong and Li [15] focuses on the development of an evaluation model to analyse the system intelligence of the integrated building management system in the intelligent building. The aim of the current research focuses on demonstrating the applicability of the selection evaluation model to the intelligent HVAC control system in the intelligent building. These two studies cover different perspectives and objectives, as well as dissimilar intelligent control systems.

The specific objectives of the research in this paper are to perform the following:

• develop a general conceptual model that incorporates critical selection factors and criteria for the optimum intelligent HVAC control system (Steps 1 and 2);

• test and refine the general conceptual model developed above by testing the level of importance of the selection criteria (Step 3);

• develop a practical evaluation model (Step 4); and,

• validate and check the robustness of the practical model (Step 5).

The methodology used to satisfy the aims and specific objectives of this research is set out in five steps (i.e. Steps 1–5 above), which are illustrated in Fig. 1 by means of a flow chart diagram.