Accuracy of HVAC Load Predictions: Validation of EnergyPlus and DOE-2 using FLEXLAB Measurements

The aim of the project reported here was to better understand the level of accuracy of three building energy simulation (BES) engines (‘engines’) — EnergyPlus™, DOE-2.1e, and DOE-2.2 — by identifying and investigating significant deviations between the performance predicted by these engines and actual performance as measured in the FLEXLAB® test facility at Lawrence Berkeley National Laboratory (LBNL). The specific test conditions included some of those prescribed in ANSI/ASHRAE Standard 140 - Standard Method of Test for the Evaluation of Building Energy Analysis Computer Programs. Detailed measurements of FLEXLAB performance, including indoor temperatures and heat fluxes and air-flow and water flow rates and temperatures in the Heating, Ventilating and Air Conditioning (HVAC) system, together with hourly weather data, were recorded and used in analyzing the simulation results from EnergyPlus v8.8, DOE-2.2 v3.65 and DOE-2.1e v127. These engines are commonly used in the United States for building energy code compliance, federal, state, and utility incentives programs, as well as energy efficient design of new buildings and energy retrofit of existing buildings. Seven conventional overhead mixing ventilation scenarios were tested and each engine was found to have a similar level of agreement with the measurements of space-level heating and sensible cooling loads. These results provide useful information regarding the accuracy of these engines in predicting the cooling and heating load elements of whole building energy performance. This information is intended for practitioners who are concerned about transitioning between simulation tools with different engines and for managers of utility programs leveraging these tools for evaluating and/or projecting measure savings to be incentivized under their programs. The results of the comparisons of simulated and measured performance indicate that the predictions from all three engines are not significantly different. The 24-hour average value of the absolute mean bias indicates the likely magnitude of the error in any particular case. The average mean bias is reduced by cancelation of overprediction in one case by underprediction in another. The daytime absolute mean biases, which may be more important for both energy performance and occupant comfort, are ~6%, presumably because of the greater complexity involved in simulating in the presence of solar radiation. EnergyPlus typically overpredicts the cooling load and/or underpredicts the heating load by ~1.5% and the DOE-2 engines typically underpredict the cooling load by approximately the same amount. The Root Mean Square Error is relatively more sensitive to shorter term variations in the difference between predicted and measured loads; the three engines have similar values, ~10%, suggesting that the uncertainties in their predictions of peak loads may also be similar in magnitude. The implication of these results is that users, both designers and program analysts, can use EnergyPlus, DOE-2.1e, or DOE-2.2 to model conventional commercial buildings equipped with overhead mixing ventilation with a similar level of confidence. Further work is required to better understand the variability in the level of agreement between the engine predictions and FLEXLAB measurements, where a particular engine will agree well with FLEXLAB in some cases and not so well in others and another engine will agree or disagree in different cases. As the sources of this variability are identified and eliminated or reduced significantly, it is recommended that the experimental capabilities and methods developed in the study reported here should be applied to validating heating and cooling load calculations for spaces with different types of furniture and miscellaneous loads. These methods should then be applied to low energy space conditioning systems in EnergyPlus including, in particular, radiant slab and radiant ceiling panel cooling and heating systems and ‘mixed mode’ systems that combine mechanical cooling and natural ventilation systems, focusing on controls, including control of thermal mass. The work reported here addresses the conventional method of heating and cooling occupied spaces; other methods, such as the use of radiant heating and cooling systems have the potential to provide equivalent occupant comfort, or better, with lower energy consumption. These systems are addressed more explicitly in EnergyPlus but there is a need for empirical validation to give users the same level of confidence in modeling these systems that they have, or should have, in modeling conventional systems, based on the results presented here

A Semi-Automated Functional Test Data Analysis Tool

Synopsis The growing interest in commissioning is creating a demand that will increasingly be met by mechanical contractors and less experienced commissioning agents. They will need tools to help them perform commissioning effectively and efficiently. The widespread availability of standardized procedures, accessible in the field, will allow commissioning to be specified with greater certainty as to what will be delivered, enhancing the acceptance and credibility of commissioning. In response, a functional test data analysis tool is being developed to analyze the data collected during functional tests for air-handling units. The functional test data analysis tool is designed to analyze test data, assess performance of the unit under test and identify the likely causes of the failure. The tool has a convenient user interface to facilitate manual entry of measurements made during a test. A graphical display shows the measured performance versus the expected performance, highlighting significant differences that indicate the unit is not able to pass the test. The tool is described as semiautomated because the measured data need to be entered manually, instead of being passed from the building control system automatically. However, the data analysis and visualization are fully automated. The tool is designed to be used by commissioning providers conducting functional tests as part of either new building commissioning or retro-commissioning, as well as building owners and operators interested in conducting routine tests periodically to check the performance of their HVAC systems

Model-based condition monitoring of a HVAC cooling coil sub-system in a real building

A comparison of the performance of two fault detection and diagnosis methods applied to a cooling coil subsystem in an air-handling unit installed in a real building is presented. Both methods employ a rst principles based reference model of the target system. One scheme carries out diagnosis using expert rules and the other recursively re-estimates selected parameters of the system model that correspond to particular faults. The procedures and information required to con- gure the schemes for condition monitoring are discussed. The results of testing the methods on an HVAC cooling coil subsystem in a commercial of ce building in the UK over an entire cooling season are reported. Both methods were able to both detect faults and provide some diagnosis. The expert rule method, however, appears to be more robust. Issues associated with the con guration and implementation of both methods are discussed in terms of performance and cost

A model-based approach to the commissioning of HVAC systems

The paper describes how first principles models can be used to assist in the commissioning of HVAC systems. The techniques utilise models that are extended to treat different types of faults. A sequence of test signals is applied to the system under test and the measured sensor and control signals are used to estimate parameters of the models relating to certain faults. These parameter estimates are compared with values calculated from design information. Differences are taken to be evidence of faulty or unsuitable equipment, incorrect installation, or inadequate commissioning. Results are presented from tests carried out on an air handling unit test rig at Loughborough University. The work has been performed as part of a UK collaborative research project on the practical application of fault detection and diagnosis to HVAC systems and as part of IEA Annex 34

A Modular Building Controls Virtual Test Bed for the Integrations of Heterogeneous Systems

This paper describes the Building Controls Virtual Test Bed (BCVTB) that is currently under development at Lawrence Berkeley National Laboratory. An earlier prototype linked EnergyPlus with controls hardware through embedded SPARK models and demonstrated its value in more cost-effective envelope design and improved controls sequences for the San Francisco Federal Building. The BCVTB presented here is a more modular design based on a middleware that we built using Ptolemy II, a modular software environment for design and analysis of heterogeneous systems. Ptolemy II provides a graphical model building environment, synchronizes the exchanged data and visualizes the system evolution during run-time. Our additions to Ptolemy II allow users to couple to Ptolemy II a prototype version of EnergyPlus,MATLAB/Simulink or other simulation programs for data exchange during run-time. In future work we will also implement a BACnet interface that allows coupling BACnet compliant building automation systems to Ptolemy II. We will present the architecture of the BCVTB and explain how users can add their own simulation programs to the BCVTB. We will then present an example application in which the building envelope and the HVAC system was simulated in EnergyPlus, the supervisory control logic was simulated in MATLAB/Simulink and Ptolemy II was used to exchange data during run-time and to provide realtime visualization as the simulation progresses

A Semi-Automated Functional Test Data Analysis Tool

Synopsis The growing interest in commissioning is creating a demand that will increasingly be met by mechanical contractors and less experienced commissioning agents. They will need tools to help them perform commissioning effectively and efficiently. The widespread availability of standardized procedures, accessible in the field, will allow commissioning to be specified with greater certainty as to what will be delivered, enhancing the acceptance and credibility of commissioning. In response, a functional test data analysis tool is being developed to analyze the data collected during functional tests for air-handling units. The functional test data analysis tool is designed to analyze test data, assess performance of the unit under test and identify the likely causes of the failure. The tool has a convenient user interface to facilitate manual entry of measurements made during a test. A graphical display shows the measured performance versus the expected performance, highlighting significant differences that indicate the unit is not able to pass the test. The tool is described as semiautomated because the measured data need to be entered manually, instead of being passed from the building control system automatically. However, the data analysis and visualization are fully automated. The tool is designed to be used by commissioning providers conducting functional tests as part of either new building commissioning or retro-commissioning, as well as building owners and operators interested in conducting routine tests periodically to check the performance of their HVAC systems

A Semi-Automated Functional Test Data Analysis Tool

Synopsis The growing interest in commissioning is creating a demand that will increasingly be met by mechanical contractors and less experienced commissioning agents. They will need tools to help them perform commissioning effectively and efficiently. The widespread availability of standardized procedures, accessible in the field, will allow commissioning to be specified with greater certainty as to what will be delivered, enhancing the acceptance and credibility of commissioning. In response, a functional test data analysis tool is being developed to analyze the data collected during functional tests for air-handling units. The functional test data analysis tool is designed to analyze test data, assess performance of the unit under test and identify the likely causes of the failure. The tool has a convenient user interface to facilitate manual entry of measurements made during a test. A graphical display shows the measured performance versus the expected performance, highlighting significant differences that indicate the unit is not able to pass the test. The tool is described as semiautomated because the measured data need to be entered manually, instead of being passed from the building control system automatically. However, the data analysis and visualization are fully automated. The tool is designed to be used by commissioning providers conducting functional tests as part of either new building commissioning or retro-commissioning, as well as building owners and operators interested in conducting routine tests periodically to check the performance of their HVAC systems

EnergyPlus Run Time Analysis

EnergyPlus is a new generation building performance simulation program offering many new modeling capabilities and more accurate performance calculations integrating building components in sub-hourly time steps. However, EnergyPlus runs much slower than the current generation simulation programs. This has become a major barrier to its widespread adoption by the industry. This paper analyzed EnergyPlus run time from comprehensive perspectives to identify key issues and challenges of speeding up EnergyPlus: studying the historical trends of EnergyPlus run time based on the advancement of computers and code improvements to EnergyPlus, comparing EnergyPlus with DOE-2 to understand and quantify the run time differences, identifying key simulation settings and model features that have significant impacts on run time, and performing code profiling to identify which EnergyPlus subroutines consume the most amount of run time. This paper provides recommendations to improve EnergyPlus run time from the modeler?s perspective and adequate computing platforms. Suggestions of software code and architecture changes to improve EnergyPlus run time based on the code profiling results are also discussed

EnergyPlus Analysis Capabilities for Use in California Building Energy Efficiency Standards Development and Compliance Calculations

California has been using DOE-2 as the main building energy analysis tool in the development of building energy efficiency standards (Title 24) and the code compliance calculations. However, DOE-2.1E is a mature program that is no longer supported by LBNL on contract to the USDOE, or by any other public or private entity. With no more significant updates in the modeling capabilities of DOE-2.1E during recent years, DOE-2.1E lacks the ability to model, with the necessary accuracy, a number of building technologies that have the potential to reduce significantly the energy consumption of buildings in California. DOE-2's legacy software code makes it difficult and time consuming to add new or enhance existing modeling features in DOE-2. Therefore the USDOE proposed to develop a new tool, EnergyPlus, which is intended to replace DOE-2 as the next generation building simulation tool. EnergyPlus inherited most of the useful features from DOE-2 and BLAST, and more significantly added new modeling capabilities far beyond DOE-2, BLAST, and other simulations tools currently available. With California's net zero energy goals for new residential buildings in 2020 and for new commercial buildings in 2030, California needs to evaluate and promote currently available best practice and emerging technologies to significantly reduce energy use of buildings for space cooling and heating, ventilating, refrigerating, lighting, and water heating. The California Energy Commission (CEC) needs to adopt a new building energy simulation program for developing and maintaining future versions of Title 24. Therefore, EnergyPlus became a good candidate to CEC for its use in developing and complying with future Title 24 upgrades. In 2004, the Pacific Gas and Electric Company contracted with ArchitecturalEnergy Corporation (AEC), Taylor Engineering, and GARD Analytics to evaluate EnergyPlus in its ability to model those energy efficiency measures specified in both the residential and nonresidential Alternative Calculation Method (ACM) of the Title-24 Standards. The AEC team identified gaps between EnergyPlus modeling capabilities and the requirements of Title 24 and ACMs. AEC's evaluation was based on the 2005 version of Title 24 and ACMs and the version 1.2.1 of EnergyPlus released on October 1, 2004. AEC's evaluation is useful for understanding the functionality and technical merits of EnergyPlus for implementing the performance-based compliance methods described in the ACMs. However, it did not study the performance of EnergyPlus in actually making building energy simulations for both the standard and proposed building designs, as is required for any software program to be certified by the CEC for use in doing Title-24 compliance calculations. In 2005, CEC funded LBNL to evaluate the use of EnergyPlus for compliance calculations by comparing the ACM accuracy test runs between DOE-2.1E and EnergyPlus. LBNL team identified key technical issues that must be addressed before EnergyPlus can be considered by the CEC for use in developing future Nonresidential Title-24 Standards or as an ACM tool. With Title 24 being updated to the 2008 version (which adds new requirements to the standards and ACMs), and EnergyPlus having been through several update cycles from version 1.2.1 to 2.1, it becomes crucial to review and update the previously identified gaps of EnergyPlus for use in Title 24, and more importantly to close the gaps which would help pave the way for EnergyPlus to be adopted as a Title 24 compliance ACM. With this as the key driving force, CEC funded LBNL in 2008 through this PIER (Public Interest Energy Research) project with the overall technical goal to expand development of EnergyPlus to provide for its use in Title-24 standard compliance and by CEC staff

Benchmarking and Equipment and Controls Assessment for a 'Big Box' Retail Chain

The paper describes work to enable improved energy performance of existing and new retail stores belonging to a national chain and thereby also identify measures and tools that would improve the performance of 'big box' stores generally. A detailed energy simulation model of a standard store design was developed and used to: (1) demonstrate the benefits of benchmarking the energy performance of retail stores of relatively standard design using baselines derived from simulation, (2) identify cost-effective improvements in the efficiency of components to be incorporated in the next design cycle, and (3) use simulation to identify potential control strategy improvements that could be adopted in all stores, improving operational efficiency. The core enabling task of the project was to develop an energy model of the current standard design using the EnergyPlus simulation program. For the purpose of verification of the model against actual utility bills, the model was reconfigured to represent twelve existing stores (seven relatively new stores and five older stores) in different US climates and simulations were performed using weather data obtained from the National Weather Service. The results of this exercise, which showed generally good agreement between predicted and measured total energy use, suggest that dynamic benchmarking based on energy simulation would be an effective tool for identifying operational problems that affect whole building energy use. The models of the seven newer stores were then configured with manufacturers performance data for the equipment specified in the current design and used to assess the energy and cost benefits of increasing the efficiency of selected HVAC, lighting and envelope components. The greatest potential for cost-effective energy savings appears to be a substantial increase in the efficiency of the blowers in the roof top units and improvements in the efficiency of the lighting. The energy benefits of economizers on the roof-top units were analyzed and found to be very sensitive to the operation of the exhaust fans used to control building pressurization