Significant Parameters for Building Automation Performance

by Shariar Makarechi, Southern Polytechnic State University — ABSTRACT: Building Automation Systems (BAS) are used to increase operational effectiveness and performance. With the ultimate goal of defining a model as performance measurement tool for BAS, the objective of this paper is to identify and illustrate the major issues impacting the BAS and the key parameters necessary for measuring the BAS performance in commercial buildings. The study has found that in addition to the human factors, BAS effects the electrical power demand and energy use and the integration of building system’s automation with lighting, security, and wireless communication networks. This study also revealed the followings as key and significant parameters: cost, user needs, simplicity (of learning and operating), integration (or the level of openness to share information with other systems), and availability (of service maintenance).

KEYWORDS: Automation, Building, Commercial, Index, Parameters, Performance, and Systems.

INTRODUCTION

Buildings are dynamic and complex entities with similar needs as the living organisms. They need to stay efficient and healthy by maintaining all of their vital signs within healthy ranges. Just as a physician usually orders lab tests to evaluate a patient’s condition before prescribing a remedy for improvements, a facility manager needs to monitor and care for a building by assessing its vital systems and the level of their performances, and then take effective steps for improvements.

This paper introduces the significant parameters for such assessments in one of the most important members of the building’s anatomy, its brain and control system which is otherwise known as Building Automation System (BAS). A customized BAS, when properly implemented, is expected to improve building’s comfort level and overall performance. This paper focuses on identifying the major parameters that will help in measuring the performance of Building Automation System.

Current energy codes, standards and practices are designed to provide generic prescriptions that may be tailored into proper steps for improvement of each project’s performance compared with pre-determined recommended ranges. The set targets for performance are known as the prescribed codes or minimum acceptable standards, which by definition set only the minimum acceptable ranges.

A minimum standard prescribed by a code or client, however, becomes the maximum goal for the contractor who will aim for delivering only what has been prescribed by code. As a result, what was intended to be the minimum acceptable practice becomes the highest-possible performance level. In final assessment, many buildings may fulfill the requirements of the prescribed codes without delivering the expected performances by the clients.

A performance-based, as opposed to prescription-based approach, focuses on satisfying the client’s requirements without limiting the level of effort has shown great results. Kashiwagi in his 2005 publication (Best Value Procurement) demonstrated that improvement of building performance requires creativity and determination to push against all apparent minimum limits. He explained that, to promote creativity, one should avoid constraints naturally imposed by prescribing the details of the process.

The ultimate goal in this research is to develop a framework for defining an Automation Performance Index (API) that could indicate the level of automation necessary to deliver client’s expected performance in their facility.

THE OBJECTIVE AND ASSUMPTIONS

The objective of this paper is to identify and illustrate the major issues impacting the Building Automation Systems and the key parameters necessary for measuring its performance. The scope of this study is limited to commercial building automation systems manufactured with tolerance, and operating limits suitable for facilities, such as office, retail, academic, courthouse, and light-institutional buildings. Industrial grade controls, with higher levels of accuracy, tolerance and performance (Franklin et al. 1990), are not within the scope of this paper.

International building codes (International Code Council 2009) and other international-level facility design guidelines and standards, such as those published by the US Air Force, Army, Navy, the Whole Building Design Guide (NIBS 2006), GSA publication PQ100.1 (GSA 2004), as well as engineering guidelines of professional organizations such as (ASHRAE 2001) are used for defining the boundaries of the API model with API = 1 representing minimum and API = 5 as the optimum level of satisfaction. In this paper, it is assumed that:

• All building equipment and controls are properly sized, commissioned and calibrated, and all devices and components are designed and built for their applications. Furthermore, all building systems and associated devices are assumed to meet the needs and limitations of the application for which they are used.
• Growth in user needs will prompt proportional expansion of building automation system, which means an increase in the number of control points.
• Building automation and building controls are treated synonymously.
• Terminology used by American engineering professionals and reference manuals such as (ASHRAE 2009) is valid and commonly accepted.
• Above assumptions are necessary to focus on BAS performance aspects, rather than design issues.

LITERATURE SEARCH

The first step in this study was to conduct a comprehensive literature search. Keyword search for automation, building, evaluation, intelligent, performance, smart, systems, and other relevant keywords were performed. The parameters that were cited with the highest frequency were identified and tabulated. Major categories for the parameters were established based on the literature search in order to identify the significant groups (aspects) of the parameters.

The literature search revealed many interesting findings. Jeanine Katzel (1998), in an article in Plant Engineering Magazine, defines BAS as a collection of equipment from sensor to software, blended together to achieve a seamless flow of data and control actions. Specific components may include end devices, controllers, networks from which data are transferred, and an information path to front-end operator interfaces. A BAS also embraces control and information strategies that allow it to function effectively. Katzel goes on to explain that few BASs are leveraged to full capacity. The systems installed in new construction often receive only cursory attention, lost in the overwhelming volume of tasks required at start-up. Although every BAS is capable of helping make more intelligent decisions, significant savings may be lost, unless “collection mechanisms” are established to gather data and “effort” is taken to analyze the data.

Katzel promotes systems “integration” and “flexibility” with emphases on plant engineers and facility managers to take time to audit facility performance, establish data collection mechanisms, and analyze reports.Katzel also promotes a standard protocol that lets a BAS exchange information simply and economically, avoid complicated proprietary or third-party gateways.

Dr. Brien Prasad (1998), in a presentation titled “Change”, speaks of controlled and regulated automation that will ultimately lead to “interoperability” and higher productivity at lower cost. Prasad also promotes “flexibility”, as well as “simplicity”. Zhi-Gang Wei (Zhi-Gang Wei 1998) writes in the context of human-machine systems, and demonstrates with experiments that high level of automation may have adverse effects, and increased automation does not necessarily result in increased benefits. Wei quantifies level of automation as a ratio of what is now automated to full potential automation.

Dr. Raja Parasuraman (2000), professor of Psychology at George Mason University, has published research on the issues of human factors and cognitive neuroscience. He explores the influence of automation and computer technology on attention, memory and vigilance of human operators. His conclusion, similar to Wei, mitigates that too much automation will be harmful (Parasurman, Sheridan et al. 2000). Research about automation and performance was also conducted by University of Iowa professors John Lee and Katrina See in 2004 with similar conclusions.

George Heath (2001) documented benefits of two-way communications for monitoring and control using the Internet. He highlighted “integration” of Heating, Ventilation and Air Conditioning (HVAC) with lighting, security and building management systems. Chris Johnson (2001), of Cisco, focused on the networking and integration of building systems, and, specifically, application of wireless technology in building security controls. Carlos Brazao (2004), of the same company, cited the projected benefits of the Internet-based operations in U.S. and Europe, and referred to a 2002 MIT study of Internet Organization, Culture and Productivity.

Continental Automated Buildings Association (CABA), a professional organization dedicated to BAS issues and initiatives, started efforts in recent years to promote development of Building Intelligence Quotient (BIQ). Intelligent buildings are those equipped with BAS, and by definition the higher the level of automation in a building, the more intelligence is associated with it. At the time of this writing, CABA was developing a Web-based ranking system for measuring the level of the building intelligence. API is not intended to measure intelligence but to measure the performance of the building’s intelligence and shall provide a tool to decide what level of intelligence would provide the optimum performance. CABA’s contributions to other aspects of BAS, such as life cycle cost (CABA 2004), networking and integration (CABA 2002) and interoperability (CABA 2004), should is noted.

Zhi-Gang Wei (1998) stated that no accepted scientific, engineering or empirical methods were available in 1998 for the purpose of evaluating Building Automation Systems. More than a decade later, this statement still holds true. Peter Manolescue (2003) tackled the practical issues of integrating security with the rest of the building automation systems and Thomas Keel (2003) of Georgia Tech has done extensive work on life cycle cost analysis of building automation systems and financial rewards of building systems integration. James Hirsh (1998) worked out simulation algorithms for integrated building automation systems, and Steven Rogers (2004) proposed control architecture with focus on the building as a power plant and its energy use.

Ted Smalley Bowen (2005) covers energy savings expected by incorporation of simulation techniques in intelligent buildings among the efforts to make buildings more manageable. He refers to study led by Carnegie Mellon University scientists on building monitoring and control schemes that includes HVAC, power production, lighting, elevators, safety, and security and concludes such schemes can be complex involving computer simulations tied into building control systems and updated by sensor feedback and performance data. Sensors keep tabs on virtually anything that can be monitored, whether mechanically, magnetically, electromagnetically, thermally, optically, chemically, biologically, or acoustically. And the conglomeration of sensors packed into intelligent buildings is increasingly accessed via wireless networks. Bowen also quotes Michelle Addison of Harvard, who stated that many schemes amount to overkill, deploying too many sensors and gathering too much data, rather than narrowing in on key performance measures.

“There’s a concern that the technology is coming in before we have the sophistication to know how best to deploy it.”

A Department of Energy-sponsored study done by Hansen (2005) and others provides background material on BAS and divides it into four areas: Applications, Hardware, Communications and Oversight. The paper promotes automated commissioning and diagnostic technologies for building systems and equipment to help improve building operation, automatically and continuously the performance problems should be detected and maintenance requirements brought to the attention of facility managers. Aaron Hansen has also introduced the open Building Information Exchange (oBIX) Web services.

Mary Ann Piette and her colleagues at Lawrence Livermore National Lab recommend the use of wireless network technology for integration of building systems and managing its electrical demand (Piette, Watson et al. 2005), as well as energy consumption. Jiri Skopec (2005) summarized the benefits of Building Intelligence Quotient (BIQ) earlier discussed. Ken Sinclair of Automated Buildings (Sinclair 2005) declares a renaissance type revival is happening in the world of building automation interconnectivity, and Ron Zimmer (2005) highlights the evolution and convergence of Communication, Life Safety and Automation (CLA).

Anwer Bashi’s article, titled “Artificial Intelligence in Building Automation” (Bashi 2006), explains simple fuzzy logic modeling for BAS. Paul Ehrlich (2006) has discussed intelligent building construction and operation issues, including building management tools and tenant portals, and Thomas Hartman (2006) illustrates the potential energy savings of a network-integrated system.

FINDINGS AND ANALYSIS

The literature search shows that the studies list above focused on a limited parameters, and failed to investigate the issue in the course of a comprehensive and systematic list of significant parameters affecting BAS performance. Although many of them acknowledged that a number of parameters exist, none produced a comprehensive list. This paper has collected key issues impacting BAS, and a comprehensive list of major parameters from surveys, practical observations, and comprehensive literature reviews.

For major issues impacting building automation systems based on literature review See Table 1

Human Factor: There is a general body of work focused on the impacts of automation on human beings. Most of this work evolves on psychological and medical themes regarding performance predictions of man and machine interfaces. Although this body of work does not directly address building construction or building automation industry, it offers valuable additional information on parameters, such as system cost, complexity and flexibilities that have been summarized in Table 1.

BAS Effects on Power Demand: Electrical power demand, which is an indication of the peak power requirements for buildings, measured in kilowatts or kW, is metered separately from the building’s energy consumption measured in kilowatt-hour or kWh. Electrical companies charge their clients for both of these components, and are generally more concerned to be able to meet the peak kW demand that is put on them. The highest electrical power demand usually occurs in the hottest days of the summer, due to the added electrical refrigeration equipment load. As a result, the demand charges for commercial clients are based on time of day and month ratings, and are usually the larger component of the electrical bills. Creative ways to save on the electrical demand are usually planned in building automation systems to produce, store and dispense the building cooling energy. A few of these techniques are identified here as:

• Alternative Refrigeration Systems: Refrigeration machines using steam-driven turbine-type compressors in lieu of electrical compressors and Absorption refrigeration machines are two examples.
• Peak Saving: The use of local fossil fuel fired generators during peak electrical demand to reduce the kW draw from the electrical power network.
• Ice or Chilled Water Storage: Insulated storage tanks are added to the building cooling water circulation. The tanks are charged during off and non-peak times and assists the refrigeration system during the peak demand time.
• Battery Packs: Batteries charged at off-peak time and assist the power network during peak demand time. In addition to the conventional methods, the charging mechanisms could be:
  1. Solar Power Cells.
  2. Wind Mills.

BAS Effects on Energy Use: Building energy use can be monitored and managed by BAS, and it is important to realize that the amount of energy used by a building is one of the most important indicators of its performance. If all building energy systems are managed by BAS, energy use can also be an indicator of BAS performance.

BAS Integration with Lighting: Lighting accounts for 40-60% of building energy use (DOE 2005) and is the largest component of the energy profile of commercial building projects (Energy-Star 2005). BAS as a tool for building energy monitoring and management can coordinate the lighting system’s operation with other building systems, such as HVAC and Security.

BAS Integration with Security: Building security systems, due to their nature, would not normally share most of their features, such as access control information, people identification and video surveillance information, with any other systems. However, some basic input regarding occupied or non-occupied spaces can be used to trigger lighting, HVAC and other utilities in each space by BAS and help in saving both energy cost and demand size.

BAS Integration and Wireless Technology: Integration of Building Automation Systems using data communication networking has been discussed as part of the API parameters; however, the use of wired versus wireless technology is a fairly new and highly evolving area of research and development in building technology. Wireless networking is approaching the speed, reliability and security levels that have rendered most of the currently wired data communication systems redundant because of the immense flexibility they offer at very low costs. As a result, wireless networks are already in place or being installed in commercial, industrial, and even residential building projects. The addition of wireless systems does not totally eliminate the hard wired or fiber optic data networking that has emerged as the fourth utility (Murchison 2005) in building construction projects. Once data and Internet connectivity is established within the structure, using the wire, cable or fiber, then wireless provides the complementary distribution coverage. Many wireless devices rely heavily on battery-powered components, and this reliance has sparked discussions about reliability and robustness of the wireless networks for uninterrupted connectivity to critical building systems that provide life safety and emergency services.

Significant API Parameters

The results of this study as shown in Table 1 concludes the following parameters are frequently cited in literature. These parameters are then categorized and described as follows:

• Cost
• User Needs
• Simplicity (of learning and operating)
• Integration (or the level of openness to share information with other systems)
• Availability (of service maintenance)

An important observation is made that the ‘Integration’ has the highest number of citations compared to all the other parameters that have been identified.

The information gathered by the literature review was complemented with the information received from selected facility managers as experts in order to finalize the selection of significant parameters.

INPUT FROM EXPERTS

Seminars and conference proceedings related to building controls, HVAC industry, building performance and engineering societies were identified, selected and attended to get in touch with industry experts and professionals and to solicit their participation in this study. Those with extensive working experience, practical and theoretical knowledge of various aspects of building operation, automation and performance were selected for the survey in this research with the following two major conditions:

• Recent professional experience in one of the aspects of building systems operations and performance.
• Willingness to participate and provide expert knowledge to this study.

Based on the above guidelines, a list of potential candidates for identifying relevant parameters influencing the performance of building automation systems were identified. These facility managers formed our expert panel who were asked to provide answers based on their experience at the field. Table 2 summarizes the areas of the expertise of the panel members selected. Each panel member was asked individually and in isolation from other members by e-mail to provide recommendations regarding the most significant parameters that influence performance of BAS.

Expert Input Process

After reviewing the list of parameters cited with high frequency by experts a final list of key parameters were produced. Based on how frequent each parameter or category of parameters were cited, the most significant ones were identified.

Forming the Expert Panel: To establish a basic understanding for the state of the industry and also to meet experts in the subject, the following seminars related to this study were attended:

1. Converging Building Systems Technologies (BuilConn 2004).
2. Performance Based Procurement (Kashiwagi 2005).
3. Building Futures Council (BFC 2005).
4. International Council of Research and Innovation in Building and Construction (CIB 2005), W92 Construction Procurement Systems Symposium.
5. High Performance Buildings (ACG 2006).

From the above seminars, and also from the list of building automation professional contacts, experts who were qualified in assisting with the study were identified.

Approval for Research Using Human Subjects: Human-based research requires special review and approval from the Institutional Review Board (IRB 2005). The protocol of this study was submitted and approved (IRB H05151) and the required training to obtain research certification was completed by the authors. A total of twenty nine highly qualified experts with industry and academic qualifications and a minimum of 15 years of combined recent experience with building systems, building controls, facility commissioning and facility operations were surveyed.

Designing the Questionnaire for the Panel: A questionnaire was designed and developed to capture knowledge from expert panel’s input. The questions were designed to be relevant to this study, short and precise without leading the experts in any direction. A total of three initial questions were sent by e-mail. The first question was intended to get an overall view for the current trends in the BAS industry:

Question #1: Rank the performance of the following between 1 and 3 using 1 for the best combination:

• Integration of BAS with Lighting.
• Integration of BAS with Security.
• BAS using wireless web based technology to collect information.

Although the response to this question would not affect the process of forming the API model, it would help in validating it. Questions 2 and 3 were the key questions for establishing the significant parameters and forming the model.

Question #2: What three factors indicate a good BAS performance? Examples are: comfort, operating cost, maintenance cost, simplicity of operation, integration with other systems, incorporation of occupant’s needs, etc.

Question #3: How would you recommend measuring the above factors? The expert survey was conducted in two stages. In stage one, a group of thirteen experts were surveyed in 2005. In stage two, involving seven members of stage one was completed in 2006. Data from both stages are summarized and tabulated in Table 3. Each member of the panel of experts was approached independently and in isolation via unshared e-mail correspondence. In other words, the experts were not informed of the responses of the other members of the panel so they could not influence each other.

Discussion

Building automation systems originally were focused on the HVAC trade and, in the past 30 years, they have evolved into overall Energy Management and Control Systems (EMCS). The additional management and monitoring capabilities of EMCS includes trend and demand analysis, tenant billing, scheduling and maintenance record keeping. These features and capabilities have become essential tools for facility managers to operate the buildings.

Lighting system accounting for up to 60% of commercial building energy consumption (DOE 2005) and integration of lighting controls through EMCS with the BAS in Question #2 also could be extended to the possibility of integration of BAS with security systems. Although security systems require exclusive provisions to prevent any access compromise, some basic information produced by the security network can be safely shared with other systems to help in verifying the occupied and un-occupied spaces within the building. This information may interactively be utilized for scheduling of the operation of the other systems such as lighting and HVAC that serve those spaces. Responses to Question 1 are presented in Table 3.

Question 1 Discussion: The main purpose for this survey question was to get information from the expert panel regarding the ‘Integration’ possibilities. BAS, which is an Energy Management and Control Systems (EMCS) tool for facility managers at its most-basic level, consists of a network of sensors and operators and time-clocks for the Heating Ventilating and Air Conditioning (HVAC) systems. Similar networks exist for commercial lighting controls and security systems. Each one of these network control systems can operate independently from each other; yet, by sharing a few scheduling and sensory items, they can coordinate the on-off sequencing, as well as zone indexing between each other. A zone can be indexed simply as occupied or un-occupied by security sensors, and this information may be used in conjunction with the time-clock schedule to initiate mode of operations for HVAC and lighting. The overall priorities for applying integration starts with HVAC as the basic system, then lighting as the second integrated system and then security as the third integrated system, according to the overall rankings proposed in Table 3 by the experts.

Wireless technology seems to have earned a respectable level of confidence in being recommended for BAS by experts. Issues, such as wave noise, interference and interruption of service and limitations of battery-operated wireless devices, were brought up in the body of the responses, but the overall vote of the panel on the use of wireless technology was an above-average ranking and on the positive side. Table 3 also records the responses of the expert panel regarding the use of wireless system for BAS networks.

Question 2 Discussion: This question of the survey requested identification of the most-significant parameters, without any suggestion or direction by the researcher. The responses included the following items:

• Maintenance Cost
• Integration of Systems Capabilities
• Simplicity of BAS Operation and Training
• Accuracy of Sensor and Actuators
• Flexibility
• Alarms
• Comfort
• Local Service and Maintenance Capability
• Ease of Data Gathering
• First Cost
• Reliability
• On-Demand Service Response
• System Self-Learning Capabilities
• Automated Upgrade Capabilities
• Life-Cycle Costs
• Waiting Time for Parts

In literature survey, five categories of parameters were identified. A study of the list of 16 items suggested by the experts revealed that quite a few of them might be put in similar classifications. In the above list, the items may be classified into the five categories earlier identified as follows:

• Items 1, 10 and 15 fall under ‘Cost’.
• Items 4, 5, 6, 7 and 11 fall under ‘User Needs’.
• Items 3 and 9 fall under ‘Simplicity’.
• Items 2 and 13 fall under ‘Integration’.
• Items 8, 12, 14 and 16 fall under ‘Availability of Service and Maintenance’.

After the above consolidation was applied, the results of the survey were recorded in Table 4, where the recommended parameters by each expert are indicated by symbol ‘X’ in the row that bears their names. The overall citation levels for all parameters are indicated in the bottom two rows of Table 4.

Table 4 Discussions: This table highlights the significant category of parameters that influence BAS performance. These five categories of parameters had been identified through literature and Internet investigation of existing work as well. However, as noted earlier, these represent five classifications in which the 16 original categories suggested by the experts were placed.

It should be no surprise that each of the identified parameters is really a complex category that encompasses several aspects. For example, the parameter labeled ‘Cost’ includes the initial cost of BAS system, the maintenance and operating cost, as well as periodical replacement and upgrading cost. For a given period of time and given financing interest rates, the combined total net present value of the above costs will represent the BAS system’s Life Cycle Cost (LCC). This cost, normalized for a representative unit of area such as 100 square meters, may be used for the parameter labeled ‘Cost’.

The second parameter represents a classification labeled ‘User Needs’ which includes expert suggested aspects, such as accuracy, flexibility, alarms, comfort and reliability. Each one of these items in turn represents a complex performance aspect of the Building Automation System. User Needs should also be normalized and represented by a quantifiable representative variable. The third parameter is labeled ‘Simplicity’ and represents categories such as operational ease and training requirements, as well as the ease of data gathering. This parameter is also discussed further in the response to Question 3. The fourth parameter covers issues related to ‘Integration’, or simply the number of systems that share data for their control operation. The fifth parameter deals with ‘Availability of Service and Maintenance’ covering items such as availability of repair service and replacement parts and possibility of on-demand service. It addresses issues such as desire for minimizing the facility downtime.

Question 3 Discussion: This question deals with the challenge of quantification of the parameters suggested by the experts for the five significant parameters of Cost (C), User Needs (N), Simplicity (S), Integration (I), and Availability (A) of Service and Maintenance. The suggestions by the experts are tabulated in Table 4. All suggestions, except for those under the ‘User Needs’ and ‘Simplicity’ are normalized per unit area and applied to the modeling techniques. For ‘User Needs’ and ‘Simplicity’, a representative quantifiable parameter may be defined for numerical analysis. This parameter is the number of control points per unit area, ‘P’ and its relationship with ‘N’ and ‘S’ were verified by a second survey for which the results are tabulated in Table 5.

Concluding Discussions: Tables 1, 3, 4, and 5 summarize the responses from the expert panel. Each table is organized to show the responses of the expert panel members to one of the three key questions of the survey. The names of the individuals and their company names are provided with their permission on each table. All experts were in the United States except one member of academia who moved from the United States to South Korea. A review of the Tables 1, 3, 4, and 5 indicates that similar expertise or companies have not resulted in similar responses, confirming that the data provided is not biased and each expert has shared his personal experience and not the company’s stance. Table 2 summarizes the categories of industries represented by the expert panel.

Table 6 summarizes the results of Tables 1 and 3, indicating the overall significant parameters for API modeling through the combined literature search and expert survey. It should be noted that although ‘L’ indicates low citation levels in two of the parameters, they are still significant parameters. In the case of availability of service vendors, one observation regarding the low citations from both the literature and expert sources is that the BAS industry has addressed this significant issue by encouraging most service vendors to maintain and service multiple manufacturer products.

SUMMARY AND CONCLUSIONS

A total of five key parameters were identified: cost, user needs, simplicity, integration, and availability of service and maintenance. In addition to these parameters summarized in Table 6, significant issues such as: ‘Human Factor’ addressed by facility managers and BAS effect on building power demand on energy use, were noted and summarized in Table 3. The effect on ‘Energy Use’ (energy management) and the ‘Power Management’ were found to be a function of the BAS which has direct relationship with the ‘User Needs’ and it proportionally affects the ‘Cost’ and the level of ‘Simplicity’ of the system. ‘Reliability’ and ‘Accuracy’ were also cited with low frequencies by the experts, however, these aspects of the various BAS alternatives can be considered compatible from one system to another, just like the different brand names in the personal computer industry that offer compatible reliability and operational accuracy. ‘Flexibility’ and ‘Accuracy’ are noted as proportional to ‘User Needs.’ ‘Availability of Service and Maintenance’ may be represented by the number of available vendors that can provide service to the system and is readily quantifiable. So the higher the value of ‘A’, the better is the expected BAS performance.

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