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Angiotensin-I-converting enzyme (ACE) inhibitory peptides are able to inhibit the activity of ACE, which is the key enzymatic factor mediating systemic hypertension. ACE-inhibitory peptides can be obtained from edible proteins and have the function of antihypertension. The amino acid sequences and the secondary structures of ACE-inhibitory peptides determine the inhibitory activities and stability. The resistance of ACE-inhibitory peptides to digestive enzymes and peptidase affect their antihypertensive bioactivity in vivo. In this paper, the mechanism of ACE-inhibition, sources of the inhibitory peptides, structure-activity relationships, stability during digestion, absorption and transportation of ACE-inhibitory peptides, and consumption of ACE-inhibitory peptides are reviewed, which provide guidance to the development of new functional foods and production of antihypertensive nutraceuticals and pharmaceuticals.

The green building concept originated from the need and desire for more energy efficient and environmentally friendly construction practices. With a boom in certified green buildings in recent decades, however, various stakeholders have raised concerns about the actual energy performance of such buildings. While studies have shown significant gaps between the expected and actual energy use of green buildings, the methods used in the analyses have been considered inappropriate. A dynamic approach has been suggested to quantify the discrepancy between the expected and actual energy use in green buildings. However, although the concept of the dynamic approach has been discussed in several studies, a process and methods for applying the approach in real applications are not available in the literature. This study introduces a process and methods for practicing the dynamic approach and provides five case studies of using this approach to assess the energy performance gap of green buildings. The analyses show that the dynamic performance gap of the five buildings ranges from 3.0% to 53.5%, with a median of 24.7%, and the average dynamic gap of the HVAC system is nearly two orders of magnitude greater than that of the non-HVAC system. The degraded controls of HVAC systems may be a main cause of the performance gap.

This paper focused on the impact of window shades’ physical characteristics and opening modes on annual air conditioning energy consumption of residential buildings in China. Three building models with different window layouts were proposed initially, and then validated by the dimensionless indicator annual energy performance (AEP) to determine the final building model adopted in this study. Harbin, Beijing, Guangzhou, Changsha and Kunming were selected to represent five different climate zones in China. The shading model has taken six physical characteristics of window shades, viz. outer emissivity, inner emissivity, solar transmittance, solar reflectance, thermal infrared transmittance, conductivity and two opening modes, viz. up & down mode as well as left & right mode, into consideration. Besides, the window shades operation schedule was summarized through 949 valid questionnaires. The annual air conditioning energy consumption was calculated via EnergyPlus software, and then the impact of the above different opening modes and parameters on energy use were investigated. The analysis results indicated that the top & bottom opening mode is better than the left & right mode when used in Harbin, and the effect of two different modes in air conditioning energy consumption in Kunming is similar. But in the other three cities, the left and right mode is preferred. Besides, the analysis results of the impact of different physical characteristics on air conditioning energy use in different climate zones could help improve the energy performance of window shades.

The deployment of thermally activated building systems (TABS) in buildings has increased to reduce energy consumption and peak loads whilst improving indoor comfort. Previous studies provided important references for the design and operation of TABS in several buildings and various climates. However, guidelines for the use of TABS design and operation in China’s buildings and climates, where TABS-related parameters can be analysed and optimised under various weather conditions, are still insufficient. Firstly, this study investigated the relationship between the design (e.g. pipe spacing S and wall area A) and operation parameters (e.g. the flow rate v and water inlet temperature Tinlet) and the heat flux using a mathematical model of a thermally activated wall (TAW) system. Results indicated that the water inlet temperature Tinlet and the indoor temperature Tin significantly affected the heat transfer rate of the TAW system. Secondly, a TAW system testbed was set up to conduct experiments for the validation of the simulation model developed in COMSOL. Lastly, a TAW design chart was presented to provide climate-based guidelines for TAW applications in buildings located in the cold regions, which could be expanded to other climates in China.

Although the thermal mass of floors in buildings has been demonstrated to help shift cooling load, there is still a lack of information about how floor covering can influence the floor’s load shifting capability and buildings’ demand flexibility. To fill this gap, we estimated demand flexibility based on the daily peak cooling load reduction for different floor configurations and regions, using EnergyPlus simulations. As a demand response strategy, we used precooling and global temperature adjustment. The result demonstrated an adverse impact of floor covering on the building’s demand flexibility. Specifically, under the same demand response strategy, the daily peak cooling load reductions were up to 20–34% for a concrete floor whereas they were only 17–29% for a carpet-covered concrete floor. This is because floor covering hinders convective coupling between the concrete floor surface and the zone air and reduces radiative heat transfer between the concrete floor surface and the surrounding environment. In hot climates such as Phoenix, floor covering almost negated the concrete floor’s load shifting capability and yielded low demand flexibility as a wood floor, representing low thermal mass. Sensitivity analyses showed that floor covering’s effects can be more profound with a larger carpet-covered area, a greater temperature adjustment depth, or a higher radiant heat gain. With this effect ignored for a given building, its demand flexibility would be overestimated, which could prevent grid operators from obtaining sufficient demand flexibility to maintain a grid. Our findings also imply that for more efficient grid-interactive buildings, a traditional standard for floor design could be modified with increasing renewable penetration.

This research aims to improve the performance of an office building based on a new operation strategy for an evacuated tube solar collector-U-tube ground heat exchanger system. The main problem with the traditional operation strategy is the mismatch between the chiller capacity and the real cooling demand, which leads to operation of the air-conditioner units under low load rates and causes low system efficiency. In order to solve the above problem, we propose a new operation strategy, which controls the unit based on the load distribution of the building. Under this operation mode, the unit operates at full load rate during most operating times. Therefore, it can solve the mismatch between selection and operation and improve energy efficiency. We introduce the traditional operating strategy and design an experiment to evaluate the system performance for a solar thermal collector and geothermal heat exchanger system. We investigate the traditional operation strategy in detail, adopting the TRNSYS (Transient System Simulation) tool to develop a detailed simulation model that considers the seasonal system performance. The simulation results are validated by experiments. The simulation and experimental results show good agreement. Over 80% of the absolute relative errors have a magnitude falling within ±10%. As a result, compared with the traditional strategy, the proposed operation strategy can reduce the ground thermal imbalance in cold regions. The system coefficient of performance can be improved from 3.7 to 3.92, and the total electricity consumption is decreased by 6.4%.

Bioorthogonal C-H allylation with ample scope was accomplished through a versatile manganese(I)-catalyzed C-H activation for the late-stage diversification of structurally complex peptides. The unique robustness of the manganese(I) catalysis manifold was reflected by full tolerance of sensitive functional groups, such as iodides, esters, amides, and OH-free hydroxy groups, thereby setting the stage for the racemization-free synthesis of C-H fused peptide hybrids featuring steroids, drug molecules, natural products, nucleobases, and saccharides.

This paper presents the design and evaluation of a novel spray tower which called the reversibly used cooling tower with upward spraying and downward gas flow (RUCT-UD). Unlike conventional spray towers, RUCT-UD has no high-resistance drift eliminator and no restriction on the minimum droplet diameter, which gives it the potential to achieve better performance of heat and mass transfer and particle scavenging. A mathematical model was developed and validated by field experiments. This model is highly efficient in calculation due to the simultaneous consideration of rising and falling droplets. Using the model, the heat and mass transfer characteristics were investigated by a parametric study, which provides a theoretical basis for the tower design. After that, the performance of RUCT-UD was compared with that of other spray-type RUCTs. Results show that RUCT-UD is 45% shorter than RUCT-UU (the reversibly used cooling tower with upward spraying and upward gas flow) and RUCT-DU (the reversibly used cooling tower with downward spraying and upward gas flow), meanwhile it could realize a high thermal performance like RUCT-UU. The particle collection efficiency of PM2.5 for RUCT-UD is 75.9%, which is higher than that of RUCT-UU (48.9%) and that of RUCT-DU (61.1%). The configuration of the proposed RUCT-UD might be applied to other spray towers for the performance improvement.

Open Automated Demand Response (OpenADR), an XML-based information exchange model, is used to facilitate continuous price-responsive operation and demand response participation for large commercial buildings in New York who are subject to the default day-ahead hourly pricing. We summarize the existing demand response programs in New York and discuss OpenADR communication, prioritization of demand response signals, and control methods. Building energy simulation models are developed and field tests are conducted to evaluate continuous energy management and demand response capabilities of two commercial buildings in New York City. Preliminary results reveal that providing machine-readable prices to commercial buildings can facilitate both demand response participation and continuous energy cost savings. Hence, efforts should be made to develop more sophisticated algorithms for building control systems to minimize customer’s utility bill based on price and reliability information from the electricity grid. Index Terms—Price response, demand response, dynamic pricing, real-time pricing, automated control, energy management, load management, load shedding, load forecasting, dynamic response.

This paper investigates energy consumption and thermal environment performance of a radiant ceiling heating system serving different types of rooms in the same building. Four types of rooms with different functions, namely restaurant, office, flight control room, and conference room, were chosen in this study. Energy consumption and thermal environment parameters were measured in different rooms, including the heating energy consumption intensity, indoor air temperature, surface temperature of the building envelope, air temperature stratification, and vertical temperature gradient. The results show that the heating energy consumption intensity is about 19.63 W/m2, which is about 30% lower than the heating index recommended in the regional standard. The heating indexes per unit area of the restaurant, office, flight control room, and conference room are 33.41, 17.36, 18.91, and 20.32 W/m2, respectively. The unit volume heating energy consumption of the flight control room, which is a large space, is significantly lower than that of the office, which is a small space. Moreover, there are also differences regarding the indoor thermal environment of the different types of rooms. Factors such as solar radiation, outdoor cold air infiltration, and indoor occupancy rate are the main factors that influence the indoor thermal environment. In general, the energy consumption level of radiant ceiling heating systems for larger spaces is lower, and the indoor thermal environment is better. Therefore, radiant ceiling heating systems are more suitable for larger rooms.

In this study, we use 28,262 charging sessions data collected from over 40 plug-in electric vehicle (PEV) charging stations during a period from March 2013 to September 2016. Following metrics are calculated for each charging session: (1) arrival and departure times; (2) plug-in and charging duration; (3) number of charging sessions; and (4) charging load flexibility. We develop two options in the discretized charging control algorithm: Option A changes only the charging start time and optimally shifts the entire period when the vehicle is actively charging within the connected charging session, and Option B splits the active charging period into 15-min segments and optimally spreads them over the connected charging session. Overall, the monthly bills savings are 21.3% and 23.5% in the summer season, 11.4% and 13.7% in the winter season, and 17.9% and 20.2% over the year for the control option 1-A and 1-B, respectively. Additionally, we develop a control algorithm in which all current charging sessions are re-optimized each time a new vehicle arrives and initiates a charging session. Greater cost savings can be achieved, e.g. 25.1% reduction for the summer season.

Electric vehicle (EV), as a significant type of distributed energy resources, provides extra flexibilities for microgrid operators to achieve a myriad of objectives. In the commercial microgrids, EVs are considered as controllable mobile batteries in the global optimization problem together with other microgrid components, such as the building load, renewable generations, and battery energy storage system. However, the patterns of the EV charging behaviors, including the public and the fleet EVs, result in further complexity of the microgrid management plans. We introduced a demonstration project with real-world EV driver behaviors collected in north California and integrated the EV charging control with the microgrid energy management problem with the objective to minimize the monthly energy bills. This chapter will analyze the cost-saving performance of smart charging controls, provided the tariff structures and the patterns of other microgrid components from the historical datasets. As an evaluation approach, we provide deterministic problem formulations to optimize the monthly energy bills under multiple energy and demand response markets. In addition, a more realistic day-by-day operation strategy is proposed and implemented with the real-world EV usage data and the building load data. The day-by-day algorithm adaptively minimizes the impact of demand charges caused by the monthly load peaks in a decentralized fashion based on the alternating direction method of multipliers (ADMM), which is later extended to an asynchronous version. In summary the highlights of this chapter are threefold: (1) introduction of a real-world smart charging project in a real-world demonstration site, which is AlCoPark; (2) comprehensive modeling and decision-making framework for commercial microgrids with EVs under multiple California demand response markets; (3) distribute and asynchronous operations in the real-time energy-allocation stage(s) to preserve the customers’ privacy and improve the system robustness.

Demand responsive industrial loads with high thermal inertia have potential to provide ancillary service for frequency regulation in the power market. To capture the benefit, this study proposes a new hierarchical framework to coordinate the demand responsive industrial loads with thermal power plants in an industrial park for secondary frequency control. In the proposed framework, demand responsive loads and generating resources are coordinated for optimal dispatch in two-time scales: (1) the regulation reserve of the industrial park is optimally scheduled in a day-ahead manner. The stochastic regulation signal is replaced by the specific extremely trajectories. Furthermore, the extremely trajectories are achieved by the day-ahead predicted regulation mileage. The resulting benefit is to transform the stochastic reserve scheduling problem into a deterministic optimization; (2) a model predictive control strategy is proposed to dispatch the industry park in real time with an objective to maximize the revenue. The proposed technology is tested using a real-world industrial electrolysis power system based upon Pennsylvania, Jersey, and Maryland (PJM) power market. Various scenarios are simulated to study the performance of the proposed approach to enable industry parks to provide ancillary service into the power market. The simulation results indicate that an industrial park with a capacity of 500 MW can provide up to 40 MW ancillary service for participation in the secondary frequency regulation. The proposed strategy is demonstrated to be capable of maintaining the economic and secure operation of the industrial park while satisfying performance requirements from the real world regulation market.

Author(s): Yin, Rongxin; Ghatikar, Girish; Piette, Mary | Abstract: This report describes research to characterize the status of, and trends in, big-data analytics for the electricity grid. The research focused on 1) identifying power-grid big-data sources, types, and characteristics; and 2) characterizing big-data architecture, analytic methods, technology applications, and challenges. The first part of this report describes the main sources and characteristics of big data for the smart grid and comprehensively reviews big-data architecture, technologies, and applications in the power sector. The second part of this report presents case studies of big data applications in the power industry: (1) a smart-meter data and predictive analytics method for demand response (DR), (2) synchrophasor data analytics for the distribution grid, and (3) utility data for peak-demand management. For the predictive analytics case study, smart-meter-data-driven and physical models were developed to predict the potential kilowatt (kW) capacity reduction from DR. The DR estimation framework that was developed works for both small and large-scale customers. The synchrophasor case study demonstrates use of an algorithm applied to time-series data to detect events that appear as significant changes, known as “edges,” in voltage magnitude measurements. The synchrophasor case study also introduces an approach for clustering sets of events to reveal unique features that distinguish them (e.g., distinguishing capacitor bank switching from transformer tap changes). The peak-demand management case study describes the use of the data analytics to enable DR programs to limit forecasted peak demand, resulting in cost savings to the utility. The findings from the research described in this report support identification of opportunities and technologies for big-data and analytics applications for demand-side management in the power sector as well as other approaches to modernizing the electricity grid.

Demand response (DR) is an important component for the establishment of smart electricity grids. It can decrease the system peaks through load shedding or shifting and optimize the utilization of the existing grid assets, which delays the need for costly upgrades. DR can also enable the integration of intermittent and distributed energy resources (DER) into the existing electricity grid. Fast DR from aggregated flexible loads can provide ancillary services (AS) to absorb grid disruptions and may replace the expensive fast-ramping reserve generation units. This study presents a methodology for load aggregation based on the prioritization of loads according to their flexibility. Different flexible load types are categorized as thermostatically controlled loads (TCL), urgent non-TCL, non-urgent non-TCL, and battery-based loads. Models based on their physical behaviour are developed and simulations performed to apply the proposed aggregation and control algorithm. Results show that the loads during peak hours can be shed off without rebound demand spikes after the DR event commonly seen in other types of DR programs. The algorithm also automatically adjusts the power demand according to the output of the distributed renewable generation, mitigating disruptions due to variations of the DER output. Additionally, the algorithm is able to adjust the load demand dynamically according to the fluctuations of electricity price.

This paper presents a novel demand response estimation framework for residential and commercial buildings using a combination of EnergyPlus and two-state models for thermostatically controlled loads. Specifically, EnergyPlus models for commercial and multi-dwelling residential units are applied to construct exhaustive datasets (i.e., with more than 300M data points) that capture the detailed load response and complex thermodynamics of several building types. Subsequently, regression models are fit to each dataset to predict DR potential based on key inputs, including hour of day, set point change and outside air temperature. For single residential units, and residential thermostatically controlled loads (i.e. water heaters and refrigerators) a two-state model from the literature is applied. For commercial office building and Multiple Dwelling Units (MDUs) building, the fitted regression model can predict DR potential with 80–90% accuracy for more than 90% of data points. The coefficients of determination (i.e. R 2 value) range between 0.54 and 0.78 for the office buildings and 0.39–0.81 for MDUs, respectively. The proposed framework is then validated for commercial buildings through a comparison with a dataset composed of 11 buildings during 12 demand response events. In addition, the use of the proposed simplified DR estimation framework is presented in terms of two cases (1) peak load shed prediction in an individual building and (2) aggregated DR up/down capacity from a large-scale group of different buildings.

Distributed power systems in the U.S. and globally are evolving to provide reliable and clean energy to consumers. In California, existing regulations require significant increases in renewable generation, as well as identification of customer-side Distributed Energy Resources (DER) controls, communication technologies, and standards for interconnection with the electric grid systems. As DER deployment expands, customer-side DER control and optimization will be critical for system flexibility and demand response (DR) participation, which improves the economic viability of DER systems. Current DER systems integration and communication challenges include leveraging the existing DER and DR technology and systems infrastructure, and enabling optimized cost, energy and carbon choices for customers to deploy interoperable grid transactions and renewable energy systems at scale. This paper presents a cost-effective solution to these challenges by exploring communication technologies and information models for DER system integration and interoperability. This system uses open standards and optimization models for resource planning based on dynamic-pricing notifications and autonomous operations within various domains of the smart grid energy system. It identifies architectures and customer engagement strategies in dynamic DR pricing transactions to generate feedback information models for load flexibility, load profiles, and participation schedules. The models are tested at a real site in California—Fort Hunter Liggett (FHL). The results for FHL show that the model fits within the existing and new DR business models and networked systems for transactive energy concepts. Integrated energy systems, communication networks, and modeling tools that coordinate supply-side networks and DER will enable electric grid system operators to use DER for grid transactions in an integrated system.

Cosimulation platforms are necessary to study the interactions of complex systems integrated in future smart grids. The Virtual Grid Integration Laboratory (VirGIL) is a modular cosimulation platform designed to study interactions between demand-response (DR) strategies, building comfort, communication networks, and power system operation. This paper presents the coupling of power systems, buildings, communications, and control under a master algorithm. There are two objectives: first, to use a modular architecture for VirGIL, based on the functional mockup interface (FMI), where several different modules can be added, exchanged, and tested; and second, to use a commercial power system simulation platform, familiar to power system operators, such as DIgSILENT PowerFactory. This will help reduce the barriers to the industry for adopting such platforms, investigate and subsequently deploy DR strategies in their daily operation. VirGIL further introduces the integration of the quantized state system (QSS) methods for simulation in this cosimulation platform. Results on how these systems interact using a real network and consumption data are also presented.

The enantioselective cobalt(III)-catalyzed C-H alkylation was achieved through the design of a novel chiral acid. The cobalt(III)-catalyzed enantioselective C-H activation was characterized by high position-, regio- and enantio-control under exceedingly mild reaction conditions. Thereby, the robust cooperative cobalt(III) catalysis proved tolerant of valuable electrophilic functional groups, including hydroxyl, bromo, and iodo substituents. Mechanistic studies revealed a considerable additive effect on kinetics and on a negative non-linear-effect.

The charging load from Electric vehicles (EVs) is modeled as deferrable load, meaning that the power consumption can be shifted to different time windows to achieve various grid objectives. In local community scenarios, EVs are considered as controllable storage devices in a global optimization problem together with other microgrid components, such as the building load, renewable generations, and battery energy storage system, etc. Various objectives have been investigated before, such as following a given load profile, reducing load fluctuation and minimizing the total operational cost. However, the uncertainties inside the behaviors of individual drivers have tremendous impact on the cost effectiveness of microgrid operations, which have not been carefully studied. In this paper, we propose a predictive management strategy in a community microgrid, using datasets of system base load, solar generation and EV charging behaviors from real-world cases. Two stage operations are modeled for cost-effective EV management, i.e. wholesale market partition in the first stage and load profile following in the second stage. Predictive control strategies, including receding horizon control, are adapted to solve the energy allocation problem in a decentralized fashion. The experimental results indicate the proposed approach can considerably reduce the total energy cost and decrease the ramping index of total system load up to 56.3%.

The performance of Heating, Ventilation and Air Conditioning (HVAC) systems may fail to satisfy design expectations due to improper equipment installation, equipment degradation, sensor failures, or incorrect control sequences. Commissioning identifies and implements cost-effective operational and maintenance measures in buildings to bring them up to the design intent or optimum operation. An existing office building is used as a case study to demonstrate the process of commissioning. Building energy benchmarking tools are applied to evaluate the energy performance for screening opportunities at the whole building level. A large natural gas saving potential was indicated by the building benchmarking results. Faulty operations in the HVAC systems, such as improper operations of air-side economizers, simultaneous heating and cooling, and ineffective optimal start, were identified through trend data analyses and functional testing. The energy saving potential for each commissioning measure is quantified with a calibrated building simulation model. An actual energy saving of 10% was realized after the implementations of cost-effective measures.

The objective of this study was to understand the energy savings from applying solar window films in a commercial building with large, curtain wall areas in Shanghai, China. eQUEST was used to simulate the annual building performance with and without the solar window film. The simulation model was calibrated against the measured monthly and daily electrical consumption. The simulation results indicated that two factors significantly influence the effect of the window film. These factors include the position of the installed window film and the configuration of the original glazing system. The effect of the window film on the performance of the curtain wall glazing system varies greatly, depending on the type of film and how it is applied. The film can decrease the shading coefficient and solar heat gain coefficient by 44% and 22% if applied on the outside and inside of the existing windows, respectively. For a double pane, low-E glazing system, the building cooling load through the windows on design day is reduced by 27.5% and 2.2% for outside and inside window films, separately. Adding the window film inside of the curtain wall was not effective because the increased window conductive heat transfer offsets the decreased cooling load from solar radiation.

This paper discusses how to optimize pre-cooling strategies for buildings in a hot California climate zone with the Demand Response Quick Assessment Tool (DRQAT), a building energy simulation tool. This paper outlines the procedure used to develop and calibrate DRQAT simulation models, and applies this procedure to eleven field test buildings. The results of a comparison between the measured demand savings during the peak period and the savings predicted by the simulation model indicate that the predicted demand shed match well with measured data for the corresponding auto-demand response (Auto-DR) days. The study shows that the accuracy of the simulation models is greatly improved after calibrating the initial models with measured data. These improved models can be used to predict load reductions for automated demand response events. The simulation results were compared with field test data to confirm the actual effect of demand response strategies. Results indicate that the optimal demand response strategies worked well for most of the buildings tested in this hot climate zone. Published by Elsevier B.V.

Energy simulation models are developed with EnergyPlus for two office buildings in a R&D center in Shanghai, China to evaluate the energy cost savings of green building design options compared with the baseline building. As a R&D center of an international IT corporation, there are data centers in the two buildings, which make them different from typical office buildings. The data centers house high energy consuming IT equipments and need 24 h air-conditioning every day all year round. In order to achieve energy cost savings, multiple energy efficiency strategies are employed for design proposed building, encompassing high performance building envelope, lighting system, and HVAC system. Through energy modeling, the design proposed options are compared to an ASHRAE 90.1-2004 compliant budget model to highlight energy cost savings versus “standard practice” and show the potential LEED™ Credit EA1—Optimize Energy Performance. Meanwhile, they are also compared to China Code model to figure out the energy cost savings versus the most popular practice conforming to China Public Building Energy Saving Design Standard. The whole building energy simulation results show that the yearly energy cost saving of the proposed design will be approximately 27% from China Code building and 21% from ASHRAE budget building, which can achieve 4 points for LEED credit due to energy performance optimization.

Thermal mass in buildings can be used to protect the building against temperature fluctuations. As batteries store energy chemically, buildings store heat (or retain coolness) in their thermal mass. Use of thermal mass allows buildings to act as energy storage devices. In addition, the use of thermal mass has enormous potential to increase the effectiveness of building systems for load shifting and peak energy demand reduction. This article introduces the basic concept of thermal mass in buildings and presents several techniques for measuring and modeling thermal mass. The use of thermal mass in buildings is characterized into two groups based on its passive and active uses. With a focus on the modeling of precooling using thermal mass, this article discusses two modeling methods for simulating the precooling control in buildings: data-driven (inverse) modeling and forward (physical) modeling, in terms of each approach's methodology and challenges. Finally, a case study of precooling a building in a warm climate is presented for demonstrating the effect of various precooling control strategies on the energy and demand savings and those impacts on thermal comfort. Keywords: data-driven modeling; cold-climate areas; building material; conduction transfer function; phase change material

The use of simulation to evaluate energy-efficient operations, commissioning problems, and demand-response (DR) strategies offers important insights into building operations. This paper describes a step-by-step procedure for using measured end-use energy data from a campus building to calibrate a simulation model developed in EnergyPlus. This process included identification of key input parameters for reducing uncertainties in the model. The building geometry and internal thermal zones were modeled to match the actual heating ventilation and air conditioning (HVAC) zoning for each individual variable air-volume (VAV) zone. We evaluated most key building and HVAC system components, including space loads (actual occupancy number, lighting and plug loads), HVAC air-side components (VAV terminals, supply and return fans) and water-side components (chillers, pumps, and cooling towers). Comparison of the pre- and post-calibration model shows that the calibration process greatly improves the model's accuracy for each end use. We propose an automated model calibration procedure that links the model to a real-time data monitoring system, allowing the model to be updated any time. The approach enables the automated data feed from sMAP into the EnergyPlus model to create realistic schedules of space loads (occupancy, lighting and plug), performance curves of fans, chillers and cooling towers. We also field-tested DR control strategies to evaluate the model's performance in predicting dynamic response effects. Finally, this paper describes application of the calibrated model to analyze control systems and DR strategies with the goal of reducing peak demand. We compare end-use data from modeled and actual DR events.