Search

• Equation-based object-oriented Modelica models for data center cooling systems are built and released in the open-source Modelica Buildings library. • Together with the Electrical package in the Modelica Buildings library, the introduced package can simulate the thermal dynamics as well as the electrical dynamics for conventional and renewable data centers. • New strategies toward emergency operations for the data center cooled by chillers and waterside economizers are investigated. In this paper, we introduce a newly developed open source data center package in the Modelica Buildings library to support modeling and simulation of cooling and control systems of data centers. The data center package contains major thermal and control component models, such as Computer Room Air Handler, Computer Room Air Conditioner, models of different subsystem configurations such as chillers with differently configured waterside economizers, as well as templates for different systems. Two case studies based on this package are performed to investigate the performances of the cooling and electrical system under normal conditions and emergency situations such as a blackout: one is for a data center powered by conventional energy, and the other is for a data center powered by renewable energy. Simulation results show that the dynamic modeling and multi-domain simulation in the Modelica-based tool make it convenient for users to investigate both normal and emergent operations for conventional and renewable data centers. [ABSTRACT FROM AUTHOR]

• Comprehensively review 30 state-of-the-art energy infrastructure models using the emerging concept of resilience. • Summarize common topics and typical implementation approaches for the modeling and simulation of energy infrastructures. • Propose five resilience indicators for the evaluation of energy infrastructure models. • Offer insights and future trends for model developers and model users. Abstract Recent years have witnessed an increasing frequency of disasters, both natural and human-induced. This applies pressure to critical infrastructures (CIs). Among all the CI sectors, the energy infrastructure plays a critical role, as almost all other CIs depend on it. In this paper, 30 energy infrastructure models dedicated for the modeling and simulation of power or natural gas networks are collected and reviewed using the emerging concept of resilience. Based on the review, typical modeling approaches for energy infrastructure resilience problems are summarized and compared. The authors, then, propose five indicators for evaluating a resilience model; namely, catering to different stakeholders, intervening in development phases, dedicating to certain stressor and failure, taking into account different interdependencies, and involving socio-economic characteristics. As a supplement, other modeling features such as data needs and time scale are further discussed. Finally, the paper offers observations of existing energy infrastructure models as well as future trends for energy infrastructure modeling. [ABSTRACT FROM AUTHOR]

Highlights • Review the existing data sources of US commercial building energy usage. • Compare energy results from survey and simulation data for US commercial buildings. • Discuss the different applications of existing data sources for US commercial building energy. • Provide the cases of applications to guide the users to use the data sources. Abstract U.S. commercial buildings consumed around 18% of total primary energy in 2017 and a 2.23 EJ increase is expected by 2050. Energy-related data for commercial buildings can be used for various applications, including benchmarking, building component analysis, market potential analysis, and policy making. Although there are plenty of data sources for energy usage in commercial buildings, they have not been thoroughly reviewed and summarized. As a result, users do not have comprehensive guidelines about selections of right data sources for specific application needs. To fill this gap, this paper conducts a comprehensive review to summarize data sources for energy usage in U.S. commercial buildings and discuss their usages for different applications. First, the paper summarizes the survey and simulation data sources for energy usage. The data sources are compared in terms of their data collection methods, released information, and relevant features. Second, this paper analyzes the applications for different survey and simulation data sources. This review categorizes the applications of data sources into five categories, including energy performance benchmarks, energy use forecasts and predictions, energy use contributions of building components, supports of building energy policies, and urban-scale energy use analysis. Moreover, the paper introduces several cases to demonstrate the usages of these data sources. [ABSTRACT FROM AUTHOR]

Highlights • Equation-based Modelica is utilized to identify and evaluate operational issues for a data center cooling system. • Sequential Search Technique is used to find the most cost-effective retrofit solution. • The best energy efficiency measure together with an optimized supply air temperature setpoint can save up to 24% of cooling energy in the data center. Abstract Data center cooling accounts for about 1% of electricity usage in the United States. Computer models are pivotal in designing and operating energy-efficient cooling systems. Compared to conventional building performance simulation programs, the equation-based object-oriented modeling language Modelica is an emerging approach that can enable fast prototyping and dynamic simulation of cooling systems. In this case study, we first modeled the cooling and control systems of an actual data center located in Massachusetts using the open-source Modelica Buildings library, and then calibrated a baseline model based on measurement data. The simulation of the baseline model identified several operation-related issues in the cooling and control systems, such as degraded cooling coils, improper dead band in control settings, and simultaneous cooling and heating in air handlers. Afterwards, we used a sequential search technique as well as an optimization scheme to investigate the energy saving potentials for different energy efficiency measures aiming to address the abovementioned issues. Simulation results show potential energy savings up to 24% by resolving identified control-related issues and optimizing the supply air temperature. [ABSTRACT FROM AUTHOR]

Highlights • Physics-based models of the heating tower heat pump (HTHP) are developed. • Physics-based models of the air-source heat pump (ASHP) are developed. • Coefficient of performance of the HTHP is 23.1% higher than the ASHP in summer. • Coefficient of performance of the HTHP is 7.4% higher than the ASHP in winter. • Compared with the ASHP, the HTHP can save 9.7% cost in a 10-year period. Abstract The heating tower heat pump (HTHP) is proposed as an alternative to the conventional air-source heat pump (ASHP). To investigate the performance improvements of the HTHP over the ASHP, a comprehensive comparison between the two systems was carried out based on a simulation study. Physics-based models for the ASHP and HTHP were developed. The performance of the ASHP under frosting conditions was corrected with a newly developed frosting map, and the regeneration penalization was considered for the HTHP. Based on the models and corrections, hourly simulations were carried out in an office building in Nanjing, China. The results show that the average energy efficiency of the HTHP in summer is 23.1% higher than that of the ASHP due to the water-cooled approach adopted by the HTHP. In winter, the HTHP achieves an increase of 7.4% in efficiency due to the frost free and energy storage characteristics. While the initial cost of the HTHP is 1.2% higher than that of the ASHP, the HTHP can still save 9.7% cost in a 10-year period because of its lower power consumption. [ABSTRACT FROM AUTHOR]

Cooling Load based Control (CLC) for the chiller sequencing is a commonly used control strategy for multiple-chiller plants. To improve the energy efficiency of these chiller plants, researchers proposed various CLC optimization approaches, which can be divided into two groups: studies to optimize the load distribution and studies to identify the optimal number of operating chillers. However, both groups have their own deficiencies and do not consider the impact of each other. This paper aims to improve the CLC by proposing three new approaches. The first optimizes the load distribution by adjusting the critical points for the chiller staging, which is easier to be implemented than existing approaches. In addition, by considering the impact of the load distribution on the cooling tower energy consumption and the pump energy consumption, this approach can achieve a better energy saving. The second optimizes the number of operating chillers by modulating the critical points and the condenser water set point in order to achieve the minimal energy consumption of the entire chiller plant that may not be guaranteed by existing approaches. The third combines the first two approaches to provide a holistic solution. The proposed three approaches were evaluated via a case study. The results show that the total energy consumption saving for the studied chiller plant is 0.5%, 5.3% and 5.6% by the three approaches, respectively. An energy saving of 4.9–11.8% can be achieved for the chillers at the cost of more energy consumption by the cooling towers (increases of 5.8–43.8%). The pumps’ energy saving varies from −8.6% to 2.0%, depending on the approach. [ABSTRACT FROM AUTHOR]

Natural ventilation is a sustainable technology that can provide a well-built environment and also save energy. The application of natural ventilation to buildings requires a careful approach in the early design phase, and fast, simple design tools are greatly needed. Fast fluid dynamics (FFD) can provide useful airflow information at a speed much faster than CFD so that it is a potential design tool for natural ventilation. This study thus validated FFD with test cases representing different types of natural ventilation. The results showed that FFD was capable of predicting the main air flow feature and ventilation rate with reasonable accuracy for wind-driven or buoyancy-driven natural ventilation. FFD simulation can reflect the influence of wind direction and surrounding buildings on natural ventilation. [ABSTRACT FROM AUTHOR]

Fast fluid dynamics (FFD) can potentially be used for real-time indoor air-flow simulations. This study developed two-dimensional fast fluid dynamics (2-D FFD) into three-dimensional fast fluid dynamics (3-D FFD). The implementation of boundary conditions at the outlet was improved with a local mass conservation method. A near-wall treatment for the semi-Lagrangian scheme was also proposed. This study validated the 3-D FFD with five flows that have features of indoor air flow. The results show that the 3-D FFD can successfully capture the three dimensionality of air-flow and provide reliable and reasonably accurate simulations for indoor air flows with a speed of about 15 times faster than current computational fluid dynamics (CFD) tools. [ABSTRACT FROM PUBLISHER]

Although buildings are transitioning towards complex, dynamic, and interconnected systems, traditional engineering metrics that dominate today don't capture several important whole-network properties. To address this, this paper adopts ecological network analysis (ENA), which has numerous successes for natural ecosystems and socio-technical systems but has yet to be applied for buildings. After translating ENA into comprehensive mathematical models for engineering applications, the novel ENA method is demonstrated with building and community energy systems. For the models to suit building energy systems, which have multiple energy types and non-negligible dynamics, ENA is formulated on an exergy basis, with dynamic flows and balances (i.e., time-varying storages). To demonstrate the proposed approach, ENA is used to redesign the heating and cooling systems for an office building and data center, coupling the buildings together via ambient-loop district energy. The models are implemented using the equation-based Modelica language. Results indicate that the ENA-guided redesign reduces the source energy by 15%. The energy consumed by the heating and cooling systems is reduced by 84% with negligible sacrifice to the thermal performance. Surprisingly, the redesign also reduced the exergy efficiency of the total system from 60% to 34% due to a greater decrease in exergy output relative to exergy input with low-exergy system designs. This indicates that ENA and other network approaches that classify system organization may outperform traditional efficiency-based metrics for building and community energy systems when whole-system perspectives are desired. • The first to apply ecological network analysis (ENA) for buildings. • Proposes exergy is best for ENA of dynamic systems with multiple energy types. • Demonstrates how ENA adds novel complex network information. • Case study with office and data center achieved 84% heating/cooling energy savings. • Reduced exergy efficiency in top design shows value of new network-based analysis. [ABSTRACT FROM AUTHOR]

In the practice of building energy performance evaluation, two types of rating systems are widely used in building energy performance evaluation: empirical baseline energy use intensity (EUI) for existing buildings and modeled baseline EUI for new buildings. Consequently, the baseline EUIs used by those systems are inconsistent. One example is ASHRAE’s Building EQ In Operation and As Designed. To support the Building EQ, the ASHRAE RP-1771 project attempts to 1) make the baselines in both Building EQ In Operation and As Designed consistent, and 2) reconcile the Building EQ As Design with ASHRAE Standard 90.1 modeled baselines. After reviewing existing building data sources, we created sets of prototypical building energy models for 18 existing commercial building types based on commercial buildings energy consumption survey data. Then, we identified sensitive program design features (PDFs) for all building types. By developing adjustment factors for sensitive PDFs, we could adjust the empirical baseline EUI of Building EQ In Operation to match the modeled baseline EUI of Building EQ As Designed, with relative errors of less than 5%. To support building energy rating for new buildings, we developed a procedure to reconcile the modeled baseline EUIs of Building EQ and ASHRAE Standard 90.1, with relative errors of less than 5%. [ABSTRACT FROM AUTHOR]

• An optimization framework based on dynamically coupled CFD-BES model is proposed. • The optimization platform is implemented using a Modelica-FFD coupled simulation and GenOpt as optimization engine. • The optimization platform is demonstrated to seek optimal thermostat placement in an office room in the design phase. • 6 hThe platform succeeds to find optimal thermostat locations and the time cost (6 h) is acceptable in the design phase. Model-based optimization can help improve the indoor thermal comfort and energy efficiency of Heating, Ventilation and Air Conditioning (HVAC) systems. The models used in previous optimization studies either omit the dynamic interaction between indoor airflow and HVAC or are too slow for model-based optimization. To address this limitation, we propose an optimization methodology using coupled simulation of the airflow and HVAC that captures the dynamics of both systems. We implement an optimization platform using the coupled models of a coarse grid Fast Fluid Dynamics model for indoor airflow and Modelica models for HVAC which is linked to the GenOpt optimization engine. Then, we demonstrate the new optimization platform by studying the optimal thermostat placement in a typical office room with a VAV terminal box in the design phase. After validating the model, we perform an optimization study, in which the VAV terminal box is dynamically controlled, and find that our optimization platform can determine the optimal location of thermostat to achieve either best thermal comfort or least energy consumption, or the combined. Finally, the time cost for performing such optimization study is about 6.2 h, which is acceptable in the design phase. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive review of the open literature on motivations, methods and applications of linking stratified airflow simulation to building energy simulation (BES). First, we reviewed the motivations for coupling prediction models for building energy and indoor environment. This review classified various exchanged data in different applications as interface data and state data , and found that choosing different data sets may lead to varying performance of stability, convergence, and speed for the co-simulation. Second, our review shows that an external coupling scheme is substantially more popular in implementations of co-simulation than an internal coupling scheme. The external coupling is shown to be generally faster in computational speed, as well as easier to implement, maintain and expand than the internal coupling. Third, the external coupling can be carried out in different data synchronization schemes, including static coupling and dynamic coupling. In comparison, the static coupling that performs data exchange only once is computationally faster and more stable than the dynamic coupling. However, concerning accuracy, the dynamic coupling that requires multiple times of data exchange is more accurate than the static coupling. Furthermore, the review identified that the implementation of the external coupling can be achieved through customized interfaces, middleware, and standard interfaces. The customized interface is straightforward but may be limited to a specific coupling application. The middleware is versatile and user-friendly but usually limited in data synchronization schemes. The standard interface is versatile and promising, but may be difficult to implement. Current applications of the co-simulation are mainly energy performance evaluation and control studies. Finally, we discussed the limitations of the current research and provided an overview for future research. [ABSTRACT FROM AUTHOR]

The evaluation of building energy performance requires a baseline for comparison. Common empirical baselines are usually used for existing buildings since they are fast and convenient. However, the same type of building at the same location will receive the same baseline despite their difference in usage. Individualized baselines by creating building energy models are possible solutions, but it is labor intensive and time-consuming. To fill the gap, this study is to develop individualized empirical baselines for existing buildings in a fast way. First, common empirical baselines are created based on survey data. Then, to get training samples, building energy models for large-scale existing buildings are created and simulated. Finally, based on simulation results, mathematical models to get individualized empirical baselines in a fast way are created. U.S. medium office buildings were used as an example to demonstrate the method. We developed 30 mathematical models for medium office buildings in two vintages (constructed before 1980 and after 1980) and 15 climate zones. The mean absolute percentage errors (MAPE) between the individualized empirical baselines and the modeled baselines for those 30 mathematical models are all lower than 5.5%. An engineer can obtain the individualized empirical baseline for an existing building in a few seconds by using the open-source tool we developed. [ABSTRACT FROM AUTHOR]

Microarray technology plays an important role in drawing useful biological conclusions by analyzing thousands of gene expressions simultaneously. Especially, image analysis is a key step in microarray analysis and its accuracy strongly depends on segmentation. The pioneering works of clustering based segmentation have shown that k-means clustering algorithm and moving k-means clustering algorithm are two commonly used methods in microarray image processing. However, they usually face unsatisfactory results because the real microarray image contains noise, artifacts and spots that vary in size, shape and contrast. To improve the segmentation accuracy, in this article we present a combination clustering based segmentation approach that may be more reliable and able to segment spots automatically. First, this new method starts with a very simple but effective contrast enhancement operation to improve the image quality. Then, an automatic gridding based on the maximum between-class variance is applied to separate the spots into independent areas. Next, among each spot region, the moving k-means clustering is first conducted to separate the spot from background and then the k-means clustering algorithms are combined for those spots failing to obtain the entire boundary. Finally, a refinement step is used to replace the false segmentation and the inseparable ones of missing spots. In addition, quantitative comparisons between the improved method and the other four segmentation algorithms--edge detection, thresholding, k-means clustering and moving k-means clustering--are carried out on cDNA microarray images from six different data sets. Experiments on six different data sets, 1) Stanford Microarray Database (SMD), 2) Gene Expression Omnibus (GEO), 3) Baylor College of Medicine (BCM), 4) Swiss Institute of Bioinformatics (SIB), 5) Joe DeRisi’s individual tiff files (DeRisi), and 6) University of California, San Francisco (UCSF), indicate that the improved approach is more robust and sensitive to weak spots. More importantly, it can obtain higher segmentation accuracy in the presence of noise, artifacts and weakly expressed spots compared with the other four methods. [ABSTRACT FROM AUTHOR]

• Propose an occupant thermal preference-aware load scheduler for resilient communities. • Quantify the impact of occupant thermostat changing behavior on resilient community optimal scheduling through selected key resilience indicators. • Adopt the chance-constrained method for addressing the occupant behavior uncertainty in the community optimal control problem. • Provide a case study for controller performance evaluation on a virtual community tesebed. The load scheduling of resilient communities in the islanded mode is subject to many uncertainties such as weather forecast errors and occupant behavior stochasticity. To date, it remains unclear how occupant preferences affect the effectiveness of the load scheduling of resilient communities. This paper proposes an occupant preference-aware load scheduler for resilient communities operating in the islanded mode. The load scheduling framework is formulated as a model predictive control problem. Based on this framework, a deterministic load scheduler is adopted as the baseline. Then, a chance-constrained scheduler is proposed to address the occupant-induced uncertainty in room temperature setpoints. Key resilience indicators are selected to quantify the impacts of the uncertainties on community load scheduling. Finally, the proposed preference-aware scheduler is compared with the deterministic scheduler on a virtual testbed based on a real-world net-zero energy community in Florida, USA. Results show that the proposed scheduler performs better in terms of serving the occupants' thermal preference and reducing the required battery size, given the presence of the assumed stochastic occupant behavior. This work indicates that it is necessary to consider the stochasticity of occupant behavior when designing optimal load schedulers for resilient communities. [ABSTRACT FROM AUTHOR]

Stakeholders such as urban planners and energy policymakers use building energy performance modeling and analysis to develop strategic sustainable energy plans with the aim of reducing energy consumption and emissions from the built environment. However, inconsistent energy data and the lack of scalable building models create a gap between building energy modeling and traditional planning practices. An alternative approach is to conduct a large-scale energy usage survey, which is time-consuming. Similarly, existing studies rely on traditional machine learning or statistical approaches for calculating large-scale energy performance. This paper proposes a solution that employs a data-driven machine learning approach to predict the energy performance of urban residential buildings, using both ensemble-based machine learning and end-use demand segregation methods. The proposed methodology consists of five steps: data collection, archetype development, physics-based parametric modeling, machine learning modeling, and urban building energy performance analysis. The devised methodology is tested on the Irish residential building stock and generates a synthetic building dataset of one million buildings through the parametric modeling of 19 identified vital variables for four residential building archetypes. As a part of the machine learning modeling process, the study implemented an end-use demand segregation method, including heating, lighting, equipment, photovoltaic, and hot water, to predict the energy performance of buildings at an urban scale. Furthermore, the model's performance is enhanced by employing an ensemble-based machine learning approach, achieving 91% accuracy compared to the traditional approach's 76%. Accurate prediction of building energy performance enables stakeholders, including energy policymakers and urban planners, to make informed decisions when planning large-scale retrofit measures. • Utilization of ensemble-based machine learning modeling. • Implementation of an end-use demand segregation. • Identification of key building features for each end-use demand prediction. • Analysis of the impact of retrofit measures using future weather data. [ABSTRACT FROM AUTHOR]

• Design a reasonable electricity pricing programs for the high renewable penetration scenario. • Reveal the importance of electricity pricing programs on EEMs selection in U.S. medium office buildings. • Help building owners to select optimal EEMs under different electricity pricing programs. Building owners usually select energy efficiency measures (EEMs) by referring to return on investment (ROI). Current studies tend to apply static energy price to estimate ROI. However, more and more buildings are adopting dynamic electricity pricing programs. To understand how electricity pricing programs impact the selection of EEMs, this paper presents an analysis of the ROIs of EEMs under different pricing programs using U.S. medium office buildings as an example. Eight EEMs in four typical cities are selected as case studies. Considering five electricity pricing programs scenarios (one static program and four dynamic programs), EEMs are selected based on their ROIs. The main findings are: (1) The ROIs of EEMs change under different pricing programs. (2) In Honolulu, Buffalo, and Denver, replacing interior fixtures with higher-efficiency fixtures has a significantly higher ROI than the rest EEMs under all five pricing programs. However, the ROI of this EEM in Honolulu ranges from 28% to 47% for different pricing programs. (3) Similarly, in Fairbanks, replace heating coil with higher-efficiency coil produce higher ROI than the rest under all five pricing programs. (4) For other EEMs, their ROI rankings vary according to electricity pricing programs. [ABSTRACT FROM AUTHOR]

Building energy estimation for the building sector under various scenarios are needed for building energy regulation and policy making. This often starts with representative baselines (either empirical baseline or modeled baseline). Commercial Buildings Energy Consumption Survey (CBECS) data is a widely used empirical baseline for U.S. commercial buildings, but none of the existing baseline model are developed to represent the CBECS data. This paper aims to develop new baseline models for the U.S. medium office buildings, which can produce modeled baselines consistent with the CBECS data. First, we introduced the methodology to create baseline models and the criteria to evaluate the performance of baseline models. The methodology consists of three phases: (1) identification of model inputs, (2) model calibration, and (3) model validation with uncertainty analysis. The evaluation index is the coefficient of variation of the root-mean-square deviation (CV(RMSD)) of site energy use intensities (EUIs) between the modeled baseline and empirical baseline. Then 30 new baseline models for two vintages (pre- and post-1980) and 15 climate zones were created. The evaluation shows that the CV(RMSD) is lower than 0.05 for the modeled baselines produced by the new baseline models. As a comparison, the CV(RMSD) is higher than 0.1 for the existing modeled baselines generated by DOE Commercial Reference Building Models. Further analysis shows that the new baseline models are able to capture the uncertainties of the representative features of existing buildings. [ABSTRACT FROM AUTHOR]

• Propose a methodology to create prototypical building energy models for existing buildings. • Develop prototypical building energy models for existing US religious worship buildings. • Provide a case of applications to guide the users to use the models. Prototypical building energy models are of great significance because they are the starting point in conducting analyses for various applications, such as building energy saving potential analysis, building design, building energy market evaluation, and building energy policy-making. However, current prototypical building energy models only represent limited types of buildings in certain countries. To fill the gap, this paper proposes a methodology to systematically create prototypical building energy models. First, a six-step methodology is introduced: model input identification, data collection, data cleaning, data conversion, model simulation, and model calibration. Then, the methodology is demonstrated by a case study of creating 30 prototypical energy models for U.S. religious worship buildings, representing buildings in 15 climate zones and 2 vintages (pre- and post-1980). Finally, to show the applications of the models, the building energy saving potentials from six efficiency measures are analyzed for pre-1980 U.S. religious worship buildings in three ASHRAE Climate Zones. The results show that the maximum energy saving potentials are approximately 30% for the religious worship buildings in all three climate zones investigated, indicating significant opportunities for energy savings in these buildings through their prototypical building model development. [ABSTRACT FROM AUTHOR]

Highlights • The models of the heating tower heat pumps (HTHPs) are developed and validated. • A large-scale performance evaluation of the HTHPs in 869 locations are carried out. • The distributions of seven performance indices are presented through color maps. • The HTHPs have excellent performance in the warm and mixed climate zones. Abstract Heating tower heat pumps (HTHPs) are proposed as an alternative to the conventional heat pumps. However, lacking performance evaluation of the HTHPs in different regions limits their applications worldwide. To address this issue, this paper carries out a large-scale comprehensive performance evaluation of the HTHPs in 869 typical locations. These locations are in the warm, mixed, and cool climate zones, where buildings need both cooling and heating supply. Seven performance indices are adopted, including the annual coefficient of performance (COP), COP in cooling season, COP in heating season, regeneration ratio, number of unsatisfactory hours, matching degree of heat pump, and matching degree of heating tower. The performance evaluation of the HTHPs is conducted by the processes of location selection, building load calculation, system sizing, simulation, and evaluation. The results show that the HTHPs have excellent performance in the warm and mixed climate zones, where the average annual COPs are 4.67 and 3.68, respectively. The HTHPs are also applicable in the cool climate zone with an average annual COP of 3.11. The distributions of all the performance indices are presented through color maps, and the results are analyzed considering the air temperature and relative humidity data of the locations. [ABSTRACT FROM AUTHOR]

Highlights • Hardware-in-the-loop (HIL) simulation is increasingly used for building controls. • HIL simulation combines the advantages of pure simulation and field tests. • The agent-based framework for HIL simulation has several distinctive advantages. • An example was used to illustrate the use of the HIL simulation framework. Abstract In general, a hardware-in-the-loop (HIL) building simulation has lower cost and fewer practical limitations (e.g., scheduling issues) than field tests in occupied buildings, while also overcoming limitations of simulations alone by capturing the full behavior of some physical systems, equipment, and components. However, the implementation of an HIL can be difficult due to the scarcity of appropriate tools. This paper presents an agent-based framework for HIL simulation. It can be used for investigation of controller performance via controller-in-the-loop simulations and also HIL for system synthesis. In the latter case, both controllers and major equipment participate in tests to ensure that dynamics of equipment operation are correctly captured in addition to controller performance. The HIL simulation framework presented allows such actual physical parts to be included in the framework while representing others for which behaviors are better known and modeled in simulation models. The mechanism implemented in the framework to synchronize simulations in software with real-time operation of physical equipment is described. As an example, use of the HIL simulation framework is illustrated through a brief study of speed control of the supply fan in the air handling unit of a variable-air-volume building heating, ventilating and air-conditioning system. [ABSTRACT FROM AUTHOR]

School buildings have a great potential for carbon emission reduction since their annual emission is about 72 million metric tons. Currently, more than 30 % of school buildings were built before 1960 and are underperforming. To effectively reduce carbon emissions via school building retrofits, it is critical for policymakers to understand the carbon intensity reduction potential of retrofitting school buildings in different regions. Hence, this study develops a method to comprehensively assess the long-term carbon intensity reduction potential of aggregated commercial buildings on a county-by-county basis in the continental U.S. We apply this method to the K-12 school buildings including primary and secondary school buildings. This paper predicts the carbon intensity reduction potential of K-12 school buildings with eight building retrofit measures from 2022 to 2050 in the continental U.S. The results reveal several interesting findings: (1) In the approximately 3,000 counties of the U.S. from 2022 to 2050, the carbon intensity reduction potential of retrofitting K-12 school buildings in each county ranges from 0.41 kg/m2 to 40.00 kg/m2. (2) Even in the same climate zone, the trends of carbon intensity reduction potential from 2022 to 2050 are different depending on their electricity sources. For example, in a hot and humid climate zone, the carbon intensity reduction potential in Florida will decrease from 2044 to 2048. However, in Mississippi, the carbon intensity reduction potential from 2044 to 2046 will increase due to the termination of the nuclear energy usage. (3) Generally, reducing lighting power density leads to more carbon intensity reduction in most states, but it might not be applicable for states with high clean energy penetration, such as Washington. [ABSTRACT FROM AUTHOR]

In U.S. district heating (DH) systems, steam is the most common heat transport medium. Industry demand for new advanced modeling capabilities of complete steam DH systems is increasing; however, the existing models for water/steam thermodynamics are too slow for large system simulations because of computationally expensive algebraic loops that require the solution to nonlinear systems of equations. For practical applications, this work presents a novel split-medium approach that implements numerically efficient liquid water models alongside various water/steam models, breaking costly algebraic loops by decoupling mass and energy balance equations. New component models for steam DH systems are also presented. We implemented the models in the equation based Modelica language and evaluated accuracy and computing speed across multiple scales: from fundamental thermodynamic properties to complete districts featuring 10 to 200 buildings. Compared to district models with the IF97 water/steam model and equipment models from the Modelica Standard Library, the new implementation improves the scaling rate for large districts from cubic to quadratic with negligible compromise to accuracy. For an annual simulation with 180 buildings, this translates to a computing time reduction from 33 to 1–1.5 h. These results are critically important for industry practitioners to simulate steam DH systems at large scales. • A first study to simulate a steam plant, network, and buildings in closed fluid loops. • Makes complete steam district heating system simulations practical for industry. • Evaluates accuracy and computing speed from thermodynamic property to district scales. • Maintains accuracy while reducing the computing scaling rate from cubic to quadratic. • Time savings from 33 to 1.3 h possible for annual simulations with 180 buildings. [ABSTRACT FROM AUTHOR]

• Develop a new strategy to evaluate GAN accuracy for building power demand prediction. • Identify and evaluate five GANs for predicting large-scale building power demand. • Recommend Original GAN and cGAN for large-scale building power demand prediction. As an unsupervised-learning data-driven model, Generative Adversarial Networks (GANs) have recently attracted a lot of attention for various applications. There is potential to apply GANs for large-scale building power demand prediction, which is needed for power grid operation. However, there are many GAN variations and it is unclear which GAN is suitable for this application. To answer this question, this paper identifies five promising GANs (Original GAN, cGAN, SGAN, InfoGAN, and ACGAN) and evaluates their performance for predicting building power demand at a large scale. Physics-based building energy models are developed to generate training and reference data. A new evaluation indicator that combines accuracy and reproducibility is proposed to evaluate the performance of different GANs in predicting building power demand. The results show that SGAN and InfoGAN are not suitable because they cannot control the number of generated building samples for different building types. The prediction performance among the Original GAN, cGAN, and ACGAN can vary depending on training sample sizes and number of building types. If the training sample size is sufficiently large, Original GAN and cGAN can predict building power demand more accurately than ACGAN with the same number of samples. If training samples are limited, Original GAN provides better accuracy than cGAN and ACGAN. When the number of building types increase, the prediction accuracy increases for cGAN, decreases for ACGAN, and remains the same for Original GAN. As a result, cGAN and Original GAN are recommended for large-scale building power demand prediction. [ABSTRACT FROM AUTHOR]

In the United States, buildings account for 35% of total energy-related carbon dioxide emissions, making them important contributors to decarbonization. Carbon intensities in the power grid are time-varying and can fluctuate significantly within hours, so shifting building loads in response to the carbon intensities can reduce a building's operational carbon emissions. This paper presents a rule-based carbon responsive control framework that controls the setpoints of thermostatically controlled loads responding to the grid's carbon emission signals in real time. Based on this framework, four controllers are proposed with different combinations of carbon accounting methods and control rules. To evaluate their performance, we performed simulation studies using models of a 27-home, all-electric, net zero energy residential community located in Basalt, Colorado, United States. The carbon intensity data of four future years from the Cambium data set are adopted to account for the evolving resource mix in the power grid. Various performance metrics, including energy consumption, carbon emission, energy cost, and thermal discomfort, were used to evaluate the performance of the controllers. Sensitivity analysis was also conducted to determine how the control thresholds and intervals affect the controllers' performance. Simulation results indicate that the carbon responsive controllers can reduce the homes' annual carbon emissions by 6.0% to 20.5%. However, the energy consumption increased by 0.9% to 6.7%, except in one scenario where it decreased by 2.2%. Compared to the baseline, the change in energy cost was between −2.9% and 3.4%, and thermal discomfort was also maintained within an acceptable range. The little impact on energy cost and thermal discomfort indicates there are no potential roadblocks for customer acceptance when rolling out the controllers in utility programs. [ABSTRACT FROM AUTHOR]

Building retrofits have great potential to reduce CO 2 emissions since buildings are responsible for 36% of emissions in the United States. Several existing studies have examined the effect of building retrofit measures on CO 2 emission reduction. However, these studies oversimplified emission factors of electricity by adopting constant annual emission factors. This study uses hourly emission factors of electricity to analyze the effect of building retrofit measures on emission reduction using U.S. medium office buildings as an example. We analyzed the CO 2 emission reduction effects of eight building retrofit measures that related to envelope and mechanical systems in five locations: Tampa, San Diego, Denver, Great Falls, and International Falls. The main findings are: (1) estimating CO 2 emission reduction with constant emission factors overestimates the emission reduction for most measures in San Diego, while it underestimates the emission reduction for most measures in Denver and International Falls; (2) The same retrofit measure may have different effects on CO 2 emission reduction depending on the climate. For instance, improving lighting efficiency and improving equipment efficiency have less impact in emission reduction in cold climates than hot climates; and (3) The most energy efficient measure may not be the most efficient emission measure. For example, in Great Falls, the most energy efficient measure is improving equipment efficiency , but the most efficient emission measure is improving heating efficiency. [ABSTRACT FROM AUTHOR]

• Open-source Modelica models for chiller plants with water-side economizer were developed. • The developed model can evaluate the control performance of multiple control sequences. • Flexible capability of the developed chiller plants with WSE models was demonstrated. • Opportunities in improving the control performance of water-side economizer were identified. There are several cooling mode control sequences for chiller plants with water-side economizers adopted in industry and academia, and it is widely known that this supervisory control significantly affects energy consumption; however, there is a lack of a modeling resource to allow multiple control sequences to be evaluated systematically under different settings, such as system configurations, load types, and climate locations. To fill this gap, this paper develops open-source Modelica models to simulate the control and energy performance of multiple cooling mode control sequences for chiller plants with water-side economizers. These models allow users to develop and test their advanced control sequences for chiller plants with water-side economizers for their target climates and system configurations. To demonstrate how these models can be utilized, a chiller plant with an integrated water-side economizer is simulated using two advanced cooling mode control sequences, two cooling load types, and six climates, for a total of 24 simulation cases. This study revealed that the energy saving potential varied from 8.6% to 36.8% for constant load profiles in all of the considered climates, and from 6.3% to 25.8% for variable load profiles in most of the climates. Results also showed that the developed system models are able to capture transient control details and reveal counterintuitive energy performance. [ABSTRACT FROM AUTHOR]

Understanding the varying characteristics and aggregate potential of power flexibility from different building types considering regional diversity is critically important to actively engaging building resources in future eco-friendly, low-cost, and sustainable power systems. This paper presents a comprehensive characteristics analysis and potential assessment of the power flexibility from heating, ventilation, and air conditioning loads in commercial buildings in the U.S. using a simulation-based method. Commercial buildings are first grouped by building type and climate region. The U.S. Department of Energy Commercial Prototype Building Models are used to represent an average building in each group and are simulated to characterize power flexibility. Based on building survey data, the number of commercial buildings in each group is estimated and used to calculate aggregate power flexibility. It is found that cooling loads in commercial buildings offer more flexibility for increasing power consumption than for decreasing it. The power consumption of commercial buildings in the U.S. can be increased by 46 GW and decreased by 40 GW on peak summer days. Among all commercial building types, standalone retail buildings provide the most absolute flexibility while medium office buildings have the most flexibility as a percentage of the rated power consumption. • A simulation-based method is proposed to assess flexibility of commercial buildings. • High-fidelity building models in EnergyPlus™ are employed. • Key factors that affect commercial building power flexibility are identified and analyzed. • Aggregate flexibility is estimated by building type and by region for the entire U.S. [ABSTRACT FROM AUTHOR]

• An open source fast fluid dynamics (FFD) model for data center thermal management. • Validation with two simple cases and a real middle-size data center case. • Saving 53.4–58.8% of annual energy through optimal design and operation. • Reducing the computing time from 464.8 h with CFD to 7.6 h by FFD. Although computational fluid dynamics (CFD) has been widely adopted to improve data center thermal management, the high computational demand limits its applications, such as multivariate optimal design and operation. Fast fluid dynamics (FFD), which has been applied for fast airflow simulation, shows great potential. However, few research applied FFD for optimal design and operation of data center thermal management. This research improves the FFD model for data centers and conducts a comprehensive evaluation and demonstration. First, the FFD model is improved by solving the advection and diffusion equations together using an upwind scheme instead of a semi-Lagrangian advection solver in the conventional FFD model. Second, new features for data centers are added, such as a pressure correction method to simulate plenum airflow and dynamic boundary conditions for IT racks. The new FFD model is first validated with two indoor environment cases and the results show that the new FFD model has slightly better overall prediction accuracy and faster speed compared to the conventional FFD model. It is also observed that both FFD models achieve acceptable accuracy, except for a few localized disparities with experimental data, which might be due to simplified handling of turbulence viscosity near the boundaries. Furthermore, validation with a real data center shows that the FFD model achieves a similar level of accuracy as CFD when compared to the experimental measurements with some level of uncertainties. It is then demonstrated for data center optimal design and operation, which saves 53.4–58.8% of annual energy while still meeting the thermal requirements. With a much faster speed and comparable accuracy compared to CFD, the FFD model parallelized on a graphics processing unit is promising for practical model-based data center early design and operation. [ABSTRACT FROM AUTHOR]

There are numerous opportunities for data centers to participate in demand response programs considering their large energy capacities, flexible working environments and workloads, redundant design and operation, etc. As a type of demand response, frequency regulation requires fast response, and its potential is not fully explored by data centers yet. This paper proposes a synergistic control strategy for data center frequency regulation which uses both IT and cooling systems. It combines power management techniques at the server level with control of the chilled water supply temperature to track the regulation signal from the electrical market. A frequency regulation flexibility factor is also proposed to increase the IT capacity for frequency regulation. The performance of the control strategy is studied through numerical simulations using an equation-based object-oriented Modelica platform designed for data centers. Simulation results show that with well-tuned control parameters, data centers can provide frequency regulation service in both regulation up and down. The performance of data centers in providing frequency regulation service is largely influenced by the regulation capacity bid, frequency regulation flexibility factor, workload condition, and cooling mode of the cooling system, and not significantly influenced by the time constant of chillers. In addition, compared with a server-only control strategy, the proposed synergistic control strategy can provide an extra regulation capacity of 3% of the design power when chillers are activated. When chillers are deactivated, both strategies have a similar regulation capacity. • A novel synergistic control strategy that combines IT system and cooling system in the data center is proposed to provide frequency regulation service to the grid. • A frequency regulation flexibility factor is proposed to improve the frequency regulation performance. • A parametric study is conducted to identify the most influential factors that can affect frequency regulation performance in the PJM market. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for enhancing community resilience through optimal renewable resource allocation and load scheduling in order to minimize unserved load and thermal discomfort. The proposed control architecture distributes the computational effort and is easier to be scaled up than traditional centralized control. The decentralized control architecture consists of two layers: The community operator layer (COL) allocates the limited amount of renewable energy resource according to the power flexibility of each building. The building agent layer (BAL) addresses the optimal load scheduling problem for each building with the allowable load determined by the COL. Both layers are formulated as a model predictive control (MPC) based optimization. Simulation scenarios are designed to compare different combinations of building weighting methods and objective functions to provide guidance for real-world deployment by community and microgrid operators. The results indicate that the impact of power flexibility is more prominent than the weighting factor to the resource allocation process. Allocation based purely on occupancy status could lead to an increase of PV curtailment. Further, it is necessary for the building agent to have multi-objective optimization to minimize unserved load ratio and maximize comfort simultaneously. [ABSTRACT FROM AUTHOR]

Short-term forecasting of building energy consumption (BEC) is significant for building energy reduction and real-time demand response. In this study, we propose a new method to realize half-hourly BEC prediction. In this new method, to fully utilize the existing data features and to further promote the forecasting performance, we divide the BEC data into the stable (cyclic) and stochastic components, and propose a novel hybrid model to model the stable and stochastic components respectively. The cyclic feature (CF) is extracted via the spectrum analysis, while the stochastic component is approximated by a novel Deep Belief Network (DBN) and Extreme Learning Machine (ELM) based ensembled model (DEEM). This novel hybrid model is named DEEM + CF. Furthermore, two real-world BEC experiments are performed to verify the proposed method. Also, to display the superiorities of the proposed DEEM + CF, this model is compared with the DBN, DBN + CF, ELM, ELM + CF, Support Vector Regression (SVR) and SVR + CF. Experimental results indicate that the CF has a great influence on the promotion of forecasting accuracy for approximately 20%, and DEEM + CF performance is the best among the comparative models, with at least 3%, 6%, 10% better accuracy than the DBN + CF, ELM + CF and SVR + CF respectively under the criteria of MAE. • Building energy consumption data is divided to the stable and stochastic parts. • Cyclic feature (CF) is extracted as the stable component via spectrum analysis. • DBN and ELM based ensembled model (DEEM) is proposed to predict stochastic part. • Two real-world experiments verify the validation of DEEM + CF. [ABSTRACT FROM AUTHOR]