2016 ASHRAE Annual Conference

Building operators can save significant amounts of time by using Direct Digital Control (DDC) system programming logic and by creating concise Building Monitoring System (BMS) screens to troubleshoot heating, ventilating, and air conditioning (HVAC) systems.  They can also drive down energy costs.  A mature energy efficiency utility has proposed the use of fundamental psychrometric formulas and parameters into DDC systems’ control logic to check sensor accuracy, improve equipment performance, and to verify valve and damper operation.  Relative humidity sensors are used to control humidifiers and dehumidification coils, and have been indirectly used to calculate enthalpy in economizers. The application of fundamental HVAC formulas into the DDC control logic identifies inaccuracies in relative humidity sensors, which, if left uncorrected can result in poor economizer performance, or unnecessary humidification and/or dehumidification. These problems frequently increase the cost of operation of the HVAC equipment. The utility also has applied outdoor air dew point calculations to check sensor performance and to optimize chiller and humidification system performance.  This paper will define dew point under several conditions, describe how it differs from relative humidity, and describe their effects on latent and sensible cooling and heating loads.  It will also present psychrometric facts and how they can be used to check the performance of HVAC systems.  For example, if the chilled water temperature is higher than the entering  air dew point temperature of a cooling coil, the coil will not remove any moisture from the entering air; therefore, the dew point will remain the same.  Programing this logic into the DDC can be done with simple “if” statements, and can be used to identify faulty sensors.  The paper will present a sample programming statement that can be modified to the facility’s DDC system programming language. The paper will also describe how, once the logic statement is entered into the DDC, it is important that the BMS screens are easy to evaluate and thus make it easy for operators to troubleshoot any system performance issues. An example of a fault detection screen for air handling unit economizers and an outdoor air weather station will also be included.

Complexities, with the modern smart buildings are continuously increasing with time. Building automation systems encompass many sensors and controllers to achieve the desired level of comfort. However, in reality it is difficult to achieve the perceived comfort because of different faults, occupant misusages and their consequences. Though, sometimes occupants do not well aware with the origin of different causes and their unusual impact on comfort and cost. In order to fill the gap between expectation or what was planned before and reality, building energy management systems (BEMS) need to be designed in such a way that they will be able to react or advise different reactive actions to the occupants. Considering real time scenarios all major anomalies primarily arises from three main sources: Different failures in buildings including HVAC, Misusage i.e. human irrelevant behaviors and Various unplanned situations related to occupancy and appliances. Above abnormal situations could cause huge penalty over occupant’s comfort and operational cost. Potential faults and their causes need to be identified. Present work considers a hazard and operability analysis (HAZOP) for the whole building system. HAZOP is used to perform a detail analysis of all possible causes of discrepancies in building operation. HAZOP is a qualitative approach but it can be quantified by using “Risk assessment matrix” based on the frequency and severity of faults. According to HAZOP analysis, building facility is divided in different sub-systems and each sub-system is studied in detail. Each sub-system assigned with a variable and deviations from the normal range of these variables are considered as symptoms. Considering the detected symptoms, a global signature table can be computed. However due to presence of non-isolable causes this table is further reduced to observable signature table. To avoid the decision error a minimal set of diagnosis is performed on the basis of difference between computed and observable signatures. Further, all the possible risks are ranked according to their degree of severity and integrated with reactive energy management. Finally, paper will address the application of the developed methodology for an office without HVAC.

Faults in heating, ventilation and air conditioning (HVAC) systems results in excessive energy waste and space comfort issues. In this study, the goal is to identify fan belt slippage and pressure setpoint override faults. These faults can be easily detected based on the correlation of head, flow and power for fans and pumps. However, due to the lack of flow meters in HVAC systems, currently, these faults have to be detected by either model-based or rule-based fault detection and diagnosis (FDD) approaches. Model based approaches generally require high computational time, which makes them unsuitable for real time applications.  The rule based approaches use other operating data rather than flow rate and cannot accurately detect these faults. On the other hand, a virtual flow meter technology, which determines flow based on the measured head and power of fans and pumps, makes the flow measurement accurately and economically without the need of physical flow meters. The purpose of this paper is to evaluate a FDD method for faults in air and water distribution systsms using the measured head and power along with a virtual flow meter. First, the correlation between power and head and the correlation between the head and flow are derived without and with faults based on fan and pump performance and system curves. Then experiment is conducted to validate develop FDD method by comparing the actual corrections with the fault free correlations. The results show the proposed FDD method can effectively detect these faults.

The paper will be a case study of the New Orleans BioInnovation Center (NOBIC) design, commissioning, controls optimization, and analytic smart building system. NOBIC was awarded the AIA Committee On The Environment (COTE) Top Ten Award at this year's AIA conference for its sustainability features and opeations. In highly sustainable buildings, three main aspects are required. First, the building's design has to support low energy operation. Second, the building must be technically commissioned. Technical commissioning requires testing of all components and sequences with personal verification. Lastly, a highly sustainable building, as with all buildings, will start to drift from optimum operations, and it will start to consume more energy over time. Analytic programs can help maintain operation at optimal levels. Sustainability goals provided the design team with an opportunity to optimize the design without creating major cost additions to the design which would result in cost overruns. Many of the ways the design team lowered energy use was by optimizing systems rather than high inital capital outlays. Technical commissioning verified that the installed systems met the intent of the design team as well as providing a baseline performance of the building that was truly in an optimal condition. Technical commissioning also utilizes the facilities staff in the commissioning process so that they can fully understand the building and design, as well as know how to bring the building back to an optimal state. An analytics/ongoing commissioing program was added to the control system in order to maintain optimal performance. The analytics platform looks at a number of different rules from simple comparisons to complex analysis in order to provide the operators with actionable data to maintain their building. Smart Building Systems go beyond next level software analytics, controls packages, or day one installations. Smart Building Systems require an integrated approach in order to design, commission, and operate highly sustainable buildings long-term.