Model-based performance monitoring: Review of diagnostic methods and chiller case study

The paper commences by reviewing the variety of technical approaches to the problem of detecting and diagnosing faulty operation in order to improve the actual performance of buildings. The review covers manual and automated methods, active testing and passive monitoring, the different classes of models used in fault detection, and methods of diagnosis. The process of model-based fault detection is then illustrated by describing the use of relatively simple empirical models of chiller energy performance to monitor equipment degradation and control problems. The CoolTools(trademark) chiller model identification package is used to fit the DOE-2 chiller model to on-site measurements from a building instrumented with high quality sensors. The need for simple algorithms to reject transient data, detect power surges and identify control problems is discussed, as is the use of energy balance checks to detect sensor problems. The accuracy with which the chiller model can be expected to predict performance is assessed from the goodness of fit obtained and the implications for fault detection sensitivity and sensor accuracy requirements are discussed. A case study is described in which the model was applied retroactively to high-quality data collected in a San Francisco office building as part of a related project (Piette et al. 1999)

Model-based performance monitoring: Review of diagnostic methods and chiller case study

The paper commences by reviewing the variety of technical approaches to the problem of detecting and diagnosing faulty operation in order to improve the actual performance of buildings. The review covers manual and automated methods, active testing and passive monitoring, the different classes of models used in fault detection, and methods of diagnosis. The process of model-based fault detection is then illustrated by describing the use of relatively simple empirical models of chiller energy performance to monitor equipment degradation and control problems. The CoolTools(trademark) chiller model identification package is used to fit the DOE-2 chiller model to on-site measurements from a building instrumented with high quality sensors. The need for simple algorithms to reject transient data, detect power surges and identify control problems is discussed, as is the use of energy balance checks to detect sensor problems. The accuracy with which the chiller model can be expected to predict performance is assessed from the goodness of fit obtained and the implications for fault detection sensitivity and sensor accuracy requirements are discussed. A case study is described in which the model was applied retroactively to high-quality data collected in a San Francisco office building as part of a related project (Piette et al. 1999)

Application of a stochastic window use model in EnergyPlus

Natural ventilation, used appropriately, has the potential to provide both significant HVAC energy savings, and improvements in occupant satisfaction. Central to the development of natural ventilation models is the need to accurately represent the behavior of building occupants. The work covered in this paper describes a method of implementing a stochastic window model in EnergyPlus. Simulated window use data from three stochastic window opening models was then compared to measured window opening behavior, collected in a naturally-ventilated office in California. Recommendations regarding the selection of stochastic window use models, and their implementation in EnergyPlus, are presented

Application of a stochastic window use model in EnergyPlus

Natural ventilation, used appropriately, has the potential to provide both significant HVAC energy savings, and improvements in occupant satisfaction. Central to the development of natural ventilation models is the need to accurately represent the behavior of building occupants. The work covered in this paper describes a method of implementing a stochastic window model in EnergyPlus. Simulated window use data from three stochastic window opening models was then compared to measured window opening behavior, collected in a naturally-ventilated office in California. Recommendations regarding the selection of stochastic window use models, and their implementation in EnergyPlus, are presented

R&D AND IMPLEMENTATION OUTCOMES FROM THE U.S.-INDIA BILATERAL CENTER FOR BUILDING ENERGY RESEARCH AND DEVELOPMENT PROGRAM

This paper explores the role of international partnerships to facilitate low-energy building design, construction, and operations. We present the strategic approach, joint research and development outcomes, and implementation activities of a unique U.S.-India program on buildings energy efficiency, the Center for Building Energy Research and Development. We discuss the collaboration successes in both countries despite their dissimilar building contexts, implementation challenges and opportunities. We highlight a range of R&D outcomes, such as novel tools and technologies developed and tested by the joint teams, with their technical energy savings potential, as well as results of capacity building and technology demonstrations. A deep-dive into key new scientific methods around building energy monitoring and benchmarking that could have a significant impact on high-performanceof buildings in both countries is also provided. Finally, in addition to joint R&D successes, pathways to deployment, and lessons learned are discussed as key takeaways

Natural Ventilation for Energy Savings in California Commercial Buildings

This research program investigated the potential energy savings to be gained by retrofitting non-domestic buildings in California with natural ventilation for cooling. The simplest and most cost effective retrofit is to open windows on the façade and turn off any mechanical ventilation. To make the problem tractable attention was restricted to wind-driven natural ventilation. Stack-driven ventilation is likely to also be present in practice, and usually improves the cooling potential.The program was split into three major projects. Project 1 assessed the potential of and the barriers to the implementation of natural ventilation. Project 2 examined induced air movement and the possible ingress of outdoor pollutants. Project 3 produced new tools for predicting the energy performance of naturally ventilated buildings, and provided training in their use.The major barriers to the introduction are the lack of specific design guidance and a lack to easy-to-use modeling tools. These are compounded by a lack of design experience and case studies and the mandatory requirements for the amount and location of openable area specified in Title 24.Research on wind-driven natural ventilation using computational fluid dynamics and wind tunnel tests provided new algorithms for cross ventilation, single-sided ventilation and corner ventilation, accounting for opening size, location and number, and the effects of sheltering by neighboring buildings. These algorithms were implemented in EnergyPlus, and the new version of the code was used in three training sessions to provide the design and engineering community with some familiarity in the new modules that calculate natural ventilation.The overall outcome of this program is a comprehensive study of the current issues concerning retrofitting commercial buildings in California and an assessment of the potential risks and benefits. It has also significantly extended the capabilities for modeling, design, and operation of naturally ventilated buildings in California