Building Controls Virtual Test Bed Research Articles

A Control Framework to Enable a Commercial Building HVAC System for Energy and Regulation Market Signal Tracking

Commercial buildings are great demand response resources in the energy and regulation markets. Many commercial building heating, ventilation and air conditioning (HVAC) systems are composed of a chiller producing chilled water, and multiple Air-Handling Units (AHUs) distributing cooled air to thermal zones. Demand response of such systems has the potential to closely track energy and regulation market signals. However, existing control methods are mostly focused on fan control, neglecting the impact of the air loop fan control on the power consumption of water loop pumps and chillers. This paper presents a complete modeling of a commercial building HVAC system consisting of chiller, water pump and multiple AHUs, and develops a two-level control to follow five-minute energy market signals and four-second frequency regulation signals. The first-level coarse control provides commands for both water and air loop variables, the second-level fine control adjusts the fan commands while maintaining the water loop inputs within the five-minute control period. As a result, the water-loop power serves as a basis to track energy market signals and the air-loop fan control can be adjusted flexibly for frequency regulation. A high-fidelity model in Dymola is built to validate the model and control. The validation is conducted through co-simulation between Matlab and Dymola model via the Building Controls Virtual Test Bed (BCVTB). The simulation results show significant tracking improvement with the proposed two-level control framework.

Solar Energy Compensation for Building Energy Saving with Thermal Comfort in a Cold Climate

This paper proposes an energy-saving strategy with assistance from solar thermal compensation for building energy systems. The target of the control strategy was to minimize energy consumption under thermal comfort constraints in buildings. First, the factors influential to indoor temperature in building environments were analyzed. Secondly, the internal and external factors, such as building materials; building orientation; window size; heating, ventilation, and air conditioning (HVAC) facilities; blinding device; solar irradiation; wind speed; and outdoor temperature were used to construct a building model on the platform ENERGYPLUS (E+). A controller aiming to regulate the amount of solar irradiation was developed with the Building Controls Virtual Test Bed (BCVTB) tool. Afterward, the building performance under different strategies was tested by co-simulation using both the computational platforms, E+ and BCVTB. The optimum scheme achieved 30.6% energy savings while meeting the same comfort criterion of its competition strategy. The study verified that the proposed strategy of combined heating, ventilation, and air conditioning and blind control could realize the energy savings and comfort satisfaction at the same time. The proposed method provides a reference to the development of low-/zero-energy building concepts in the field.

Application of Artificial Neural Network Model for Optimized Control of Condenser Water Temperature Set-Point in a Chilled Water System

In this study, real-time predictive control and optimization model based on an ANN (artificial neural network) was developed to evaluate the cooling energy saving performance of the optimized control of CndWT (condenser water temperature). For this purpose, the difference in TCEC (total cooling energy consumption) between the conventional control strategy when the CndWT produced by the cooling tower is fixed and the optimized control strategy when real-time control of the CndWT through the optimal ANN model is applied was compared and analyzed. For the modeling of the building to be simulated, the co-simulation of EnergyPlus and MATLAB was built through the middleware Building Controls Virtual Test Bed. For the prediction of TCEC, an ANN model was developed through MATLAB's neural network toolbox. The model accuracy of the ANN was examined through Cv(RMSE) index and as a result, Cv(RMSE) of the optimized ANN model turned out to be approximately 25 %. More importantly, the predictive control technique was able to save TCEC by 5.6 % compared to the conventional control method constantly fixing CndWT set-point to 30 °C. These results showed that the CndWT needs to be dynamically controlled using artificial intelligence technique such as ANN model and that significant energy savings were achievable compared to the conventional fixed control.

Optimization of a university timetable considering building energy efficiency: An approach based on the building controls virtual test bed platform using a genetic algorithm

It is difficult to prompt students to take energy saving behaviors proactively owing to their limited responsibilities for energy saving; thus, administrators are required to actively adopt management means to reduce building energy consumption. Reasonable arrangement of course timetables by administrators can greatly promote energy efficiency in university buildings, as the energy consumption of teaching buildings is closely related to factors such as occupancy time and number of users. Hence, this study develops a co-simulation method based on the Building Controls Virtual Test Bed (BCVTB) platform using genetic algorithm to optimize university timetable considering building energy efficiency. The timetable of a university teaching building in Xi'an, China, from September 2019 to January 2020, is simulated using the proposed method. According to the simulation results, the occupancy of classrooms in the optimized timetable is relatively concentrated timewise. As more courses are allocated in the afternoon, the operating time of lighting during the daytime would decrease accordingly. Therefore, the lighting power consumption is reduced. In addition, the cooling load of the teaching building is increased and the heating load of the teaching building decreases. The results indicate that the utilization of the proposed course timetabling method can reduce the energy consumption of teaching buildings by approximately 3.6% in the autumn semester. The developed method helps administrators arrange a timetable to achieve energy efficiency in university buildings. Moreover, it is helpful for the design and operation management of teaching buildings.

Visualized Co-Simulation of Adaptive Human Behavior and Dynamic Building Performance: An Agent-Based Model (ABM) and Artificial Intelligence (AI) Approach for Smart Architectural Design

Human (occupant) behavior has been a topic of active research in the study of architecture and energy. To integrate the work of architectural design with techniques of building performance simulation in the presence of responsive human behavior, this study proposes a computational framework that can visualize and evaluate space occupancy, energy use, and generative envelope design given a space outline. A design simulation platform based on the visual programming language (VPL) of Rhino Grasshopper (GH) and Python is presented so that users (architects) can monitor real-time occupant response to space morphology, environmental building operation, and the formal optimization of three-dimensional (3D) building space. For dynamic co-simulation, the Building Controls Virtual Test Bed, Energy Plus, and Radiance were interfaced, and the agent-based model (ABM) approach and Gaussian process (GP) were applied to represent agents’ self-learning adaptation, feedback, and impact on room temperature and illuminance. Hypothetical behavior scenarios of virtual agents with experimental building geometry were produced to validate the framework and its effectiveness in supporting dynamic simulation. The study’s findings show that building energy and temperature largely depend on ABMs and geometry configuration, which demonstrates the importance of coupled simulation in design decision-making.

Building Performance Evaluation Using Coupled Simulation of EnergyPlus™ and an Occupant Behavior Model

Building energy simulation programs are used for optimal sizing of building systems to reduce excessive energy wastage. Such programs employ thermo-dynamic algorithms to estimate every aspect of the target building with a certain level of accuracy. Currently, almost all building simulation tools capture static features of a building including the envelope, geometry, and Heating, Ventilation, and Air Conditioning (HVAC) systems, etc. However, building performance also relies on dynamic features such as occupants’ interactions with the building. Such interactions have not been fully implemented in building energy simulation tools, which potentially influences the comprehensiveness and accuracy of estimations. This paper discusses an information exchange mechanism via coupling of EnergyPlus™, a building energy simulation engine and PMFServ, an occupant behavior modeling tool, to alleviate this issue. The simulation process is conducted in Building Controls Virtual Testbed (BCVTB), a virtual simulation coupling tool that connects the two separate simulation engines on a time-step basis. This approach adds a critical dimension to the traditional building energy simulation programs to seamlessly integrate occupants’ interactions with building components to improve the modeling capability, thereby improving building performance evaluation. The results analysis of this paper reveals a need to consider metrics that measure different types of comfort for building occupants.

Building temperature regulation in a multi-zone HVAC system using distributed adaptive control

During recent years there have been considerable research efforts on improving energy efficiency of buildings. Since Heating, Ventilation and Air-Conditioning (HVAC) systems are responsible for a big part of energy consumption, developing efficient HVAC control systems is crucial. In most of the developed approaches, precise knowledge of system parameters and/or adequate historical data is required. However, these approaches may not perform as well in the presence of dynamic parameter changes due to human activity, material degradation, and wear and tear, or disturbances and other operational uncertainties due to occupancy, solar gains, electrical equipment, and weather conditions. In this paper, we consider buildings with several climate zones and propose a distributed adaptive control scheme for a multi-zone HVAC system which can effectively regulate zone temperature by applying on-line learning and assuming exchange of information between neighboring zones. The controller of each zone achieves the local objective of controlling zone temperature by compensating for the effects of neighboring zones as well as for possible changes in the parameters of the system. Despite the exchange of information, each local controller does not know how the control actions and temperature of a neighboring zone affect the temperature of its own zone. For this reason, each local controller is estimating the parameters of the interconnections in real time and uses them together with the exchanged information to provide a more accurate local zone temperature control. The proposed method is illustrated using an example of temperature control in a six-zone building as well as a large school building, which are implemented in a Building Controls Virtual Test Bed (BCVTB) environment using EnergyPlus and MATLAB/Simulink.

Uncertainty of Energy and Economic Performance of Manual Solar Shades in Hot Summer and Cold Winter Regions of China

Occupant behavior is recognized as a major source of discrepancy between simulated and actual energy consumption. This study investigates the uncertainty of energy and economic performance of manual solar shades for the south facade. A developed stochastic model for manual solar shades based on a discrete-time Markov chain method was constructed in Building Controls Virtual Test Bed (BCVTB) for co-simulation with EnergyPlus. The stochastic shade model was compared with deterministic models concerning energy savings potential and life cycle economic performance at different building scales (i.e., from a single room to a whole building). The results show that annual energy uncertainty, due to occupant behavior, on manual shades can be neglected at the building level, whereas for sizing heating equipment, energy uncertainty should be considered. The payback period for manual shades is about 10 years and, in general, a larger building has a higher economic performance. Comparative analysis shows that there is a relatively big performance overestimation or underestimation by commonly used deterministic models in building simulation tools, and thus may lead to a biased economic analysis or even an inappropriate design decision when comparing different energy-saving measures.

Agent-Based Modelling of Occupants’ Clothing and Activity Behaviour and Their Impact on Thermal Comfort in Buildings

This paper proposes a new agent-based approach to model the clothing and activity behaviour of occupants in buildings. An autonomous agent is created to interact with the surrounding thermal environment and make decisions based on its comfort level. The agent-based behaviour model was implemented by MATLAB and was linked with building energy simulation program EnergyPlus via Building Controls Virtual Test Bed (BCVTB) and MLE+. A simulation experiment was conducted to see how an agent adapts to the dynamic thermal changes in different seasons and how it affects the annual thermal comfort. Obvious variation in the predicted annual discomfort time can be observed when using the agent-based method compared with a traditional commonly used method with static parameters. The paper concludes with a discussion of the model’s current limitations and possibilities for future development.

Application of artificial neural networks for optimized AHU discharge air temperature set-point and minimized cooling energy in VAV system

Chillers and boilers based air handling unit (AHU) system is one of the most widely used heating and cooling systems in office buildings in Korea. However, in most conventional forced-air systems, the guidelines for the AHU discharge air temperature (DAT) are not fully established and thus AHU DAT are constantly fixed to a particular set-point, regardless of dynamic changes of operating variables. In this circumstance, this study aimed at developing a control algorithm that can operate a conventional VAV system with optimal set-points for the AHU DAT. Three-story office building was modeled using co-simulation technique between EnergyPlus and Matlab via BCVTB (Building Controls Virtual Test Bed). In addition, artificial neural network (ANN) model, which was designed to predict the cooling energy consumption for the upcoming next time-step, was embedded into the control algorithm using neural network toolbox within Matlab. By comparing the predicted energy for the different set-points of the AHU DAT, the control algorithm can determine the most energy-effective AHU DAT set-point to minimize the cooling energy. The results showed that the prediction accuracy between simulated and predicted outcomes turned out to have a low coefficient of variation root mean square error (CvRMSE) value of approximately 24%. In addition, the predictive control algorithm was able to significantly reduce cooling energy consumption by approximately 10%, compared to a conventional control strategy of fixing AHU DAT to 14 °C. These findings suggest that the ANN model and the control algorithm showed energy saving potential for various types of forced air systems by taking dynamic operating conditions into account in each time-step.

Optimal Control of Operable Windows for Mixed Mode Building Simulation in EnergyPlus

A well-designed mixed mode building allows natural ventilation when the outside weather conditions are favorable and deploys air-conditioning when natural ventilation is not able to provide sufficient comfort. In this study, a methodology is proposed to enable real time control of operable windows for mixed mode ventilation building. EnergyPlus is used for building energy simulations. A real-time simulation is performed using Building Controls Virtual Test Bed (BCVTB). In this methodology, AERIS Weather Data is used to forecast hourly weather parameters. Seasonal Autoregressive Integrated Moving Average (SARIMA) Models are used to model a day-ahead prediction for the lighting load, electric load and the occupancy profiles. Co-simulation of EnergyPlus is carried out with BCVTB to enable real-time simulation wherein; the EnergyPlus weather file parameters and input building loads are updated every hour. Based on the indoor and outdoor conditions, EnergyPlus calculates the schedule for opening/closing of windows. A simulation based case study for Hyderabad, India is performed to demonstrate the working of the proposed methodology. Reduction of 25% in cooling energy demand was estimated in a mixed mode building as compared to the fully air-conditioned building during the study period.

ANN Based Optimized AHU Discharge Air Temperature Control of Conventional VAV System for Minimized Cooling Energy in an Office Building

In most conventional forced-air systems, the guidelines for the air handling unit(AHU) discharge air temperature(DAT) are not fully established and thus AHU DAT are constantly fixed to a particular set-point, regardless of dynamic changes of operating variables. In this circumstance, this study aimed at developing a control algorithm that can operate a conventional VAV system with optimal set-points for the AHU DAT. Three-story office building was modeled using co-simulation technique between EnergyPlus and Matlab via BCVTB(Building Controls Virtual Test Bed). In addition, artificial neural network(ANN) model, which was designed to predict the cooling energy consumption for the upcoming next time-step, was embedded into the control algorithm using neural network toolbox within Matlab. By comparing the predicted energy for the different set-points of the AHU DAT, the control algorithm can determine the most energy-effective AHU DAT set-point to minimize the cooling energy. The results showed that the prediction accuracy between simulated and predicted outcomes turned out to have a low coefficient of variation root mean square error (CvRMSE) value of approximately 24%. In addition, the predictive control algorithm was able to significantly reduce cooling energy consumption by approximately 10%, compared to a conventional control strategy of fixing AHU DAT to 14°C.

Transactive Control of Commercial Buildings for Demand Response

Transactive control is a type of distributed control strategy that uses market mechanisms to engage self-interested responsive loads to achieve power balance in the electrical power grid. In this paper, we propose a transactive control approach of commercial building heating, ventilation, and air-conditioning (HVAC) systems for demand response. We first describe the system models, and identify their model parameters using data collected from systems engineering building (SEB) located on our Pacific Northwest National Laboratory campus. We next present a transactive control market structure for commercial building HVAC systems, and describe its agent bidding and market clearing strategies. Several case studies are performed in a simulation environment using building controls virtual test bed (BCVTB) and calibrated SEB EnergyPlus model. We show that the proposed transactive control approach is very effective at peak shaving, load shifting, and strategic conservation for commercial building HVAC systems.

Co-simulation of a HVAC system-integrated phase change material thermal storage unit

A co-simulation environment, consisting of a detailed mathematical model of a thermal energy storage unit which is incorporated with an EnergyPlus simulation model of a full building HVAC system, is described. The two models are integrated using the user-defined plant component feature in EnergyPlus and the Building Controls Virtual Test Bed (BCVTB) environment. The thermal energy storage unit, which consists of encapsulated phase change material in a series of flat plates and a heat transfer working fluid (water), is modelled using a transient one-dimensional forward finite difference method. The thermal storage model is executed within MATLAB and is verified against experimental data, showing a discharging heat transfer accuracy to within 2.5%. The building model, which incorporates a retrofitted ground source heat pump system within a thermally massive building, is simulated in the EnergyPlus environment. The co-simulation arrangement allows for in-depth analysis of the integrated system under dynamic operating conditions, which is currently not possible within the EnergyPlus environment. Moreover, the overall adopted approach, based on generic integration of a detailed mathematical model, using a third party generalised programming environment, into an established building simulation environment, serves as a successful exemplar for other researchers and practitioners working in the field.

Impact of Manually Controlled Solar Shades on Indoor Visual Comfort

Daylight plays a significant role in sustainable building design. The purpose of this paper was to investigate the impact of manual solar shades on indoor visual comfort. A developed stochastic model for manual solar shades was modeled in Building Controls Virtual Test Bed, which was coupled with EnergyPlus for co-simulation. Movable solar shades were compared with two unshaded windows. Results show that movable solar shades have more than half of the working hours with a comfortable illuminance level, which is about twice higher than low-e windows, with a less significant daylight illuminance fluctuation. For glare protection, movable solar shades increase comfortable visual conditions by about 20% compared to low-e windows. Moreover, the intolerable glare perception could be reduced by more than 20% for movable solar shades.

Development of a method of real-time building energy simulation for efficient predictive control

This study is to develop a real-time building energy simulation method for efficient predictive control of building. One of the most critical point in predictive control is that accurate prediction should take precedence before reaching to control value. Some researches using model predictive control (MPC) based on simulations may result in error due to uncertain data input. In order to reduce error, current circumstances should be applied to the simulation; improving weather forecast data can increase the accuracy. Thus, this study focuses on developing simulation method using daily updated weather forecast data. The novel building energy simulation method proposed in this study is real-time building energy simulation in which generates a real-time weather data file. The data file consists of calculations from web-based forecast data, equations, built-in functions of EnergyPlus, and other weather elements (default) from building controls virtual test bed (BCVTB). In other words, the building energy simulation, designed in this study is an EnergyPlus-specific real-time weather data file generation method. The method generates a 24-h weather data file every day. When the weather is predicted using the proposed method, mean bias error (MBE) and coefficient of variation of root mean squared error (Cv(RMSE)) are calculated to be 1.3% and 20.1%, respectively.A case study for real-time building energy simulation is carried out, finding optimal set-point temperature of chilled water and condenser water of 8.5°C and 27.75°C, respectively. The implementation of the values results 23.5kWh potential savings during the day time, compared to the predicted energy consumption. Thus, it is expected to draw more reasonable predicted control value, using the real-time building energy simulation.

Development of a whole building model predictive control strategy for a LEED silver community college

A model predictive control strategy method is developed for a LEED silver community college building that contains an advanced common loop heat pump HVAC system. MPC is performed using persistence based climate forecasting with two scenarios examined: fixed temperature setpoint pairs; and a deadband of 1°C between the heating/cooling setpoint. EnergyPlus acts as the “real” building and is linked with R software for model predictive control via the Building Controls Virtual Test Bed. A statistical based building response model is created in R using training data from a calibrated EnergyPlus model. A brute force optimization strategy is used for solving the objective function. A piece-wise objective function maintains thermal comfort during occupied periods with a focus on reducing energy consumption during other periods. Results when using fixed setpoint pairs exhibit a 4% reduction in HVAC energy consumption, and deadband setpoints exhibit a 9% reduction in HVAC energy consumption, compared with reactive rule based control. Of interest is that the type of energy savings (thermal energy vs electricity) varies depending upon the setpoint options. The results are promising given the strict thermal comfort requirement employed, minimal search space, use of simplistic persistence forecasting, and the sophisticated HVAC system.

Modeling and simulation of the heating circuit of a multi-functional building

In this study, the fossil-fueled heating circuit and the corresponding automation system of a multi-functional building are simulated to lay the foundation for in-depth analysis and optimization of the building energy concept.The considered building is the E.ON Energy Research Center main building which is equipped with a highly integrated energy generation, storage and distribution system. The thermo-hydraulic components and control units are developed in the modeling language MODELICA whereas the relevant part of the global Building Automation System is implemented in MATLAB/SIMULINK. The interaction between the two simulation environments is established using the non-commercial co-simulation platform Building Controls Virtual Test Bed (BCVTB).Data stored in the comprehensive building monitoring system is used for the parametrization of the hydraulic model. Moreover, the data is analyzed to deduct information on the control system such as temperature set-points.Both the single subsystems and the model as a whole are validated by comparing simulation results to monitoring data. The evaluation reveals that the behavior of the hydraulic components and the control system is successfully reproduced and deviations remain in an acceptable range. The reasons for the deviations are identified and mitigation measures are discussed.

Building energy simulation in real time through an open standard interface

Building energy models (BEMs) are typically used for design and code compliance for new buildings and in the renovation of existing buildings to predict energy use. The increasing adoption of BEM as standard practice in the building industry presents an opportunity to extend the use of BEMs into construction, commissioning and operation. In 2009, the authors developed a real-time simulation framework to execute an EnergyPlus model in real time to improve building operation. This paper reports an enhancement of that real-time energy simulation framework. The previous version only works with software tools that implement the custom co-simulation interface of the Building Controls Virtual Test Bed (BCVTB), such as EnergyPlus, Dymola and TRNSYS. The new version uses an open standard interface, the Functional Mockup Interface (FMI), to provide a generic interface to any application that supports the FMI protocol. In addition, the new version utilizes the Simple Measurement and Actuation Profile (sMAP) tool as the data acquisition system to acquire, store and present data. This paper introduces the updated architecture of the real-time simulation framework using FMI and presents proof-of-concept demonstration results which validate the new framework.

Quantifying the human–building interaction: Considering the active, adaptive occupant in building performance simulation

This paper introduces a Human and Building Interaction Toolkit (HABIT) for simulating the thermally adaptive behaviors and comfort of office occupants alongside building energy consumption. The toolkit uses the Building Controls Virtual Test Bed (BCVTB) to co-simulate a field-tested, agent-based behavior model with an EnergyPlus medium office model. The usefulness of the toolkit is demonstrated through a series of zone and building-level case study simulations that examine the wisdom of pairing local heating and cooling options with strategic thermostat set point offsets, judging from the energy, Indoor Environmental Quality (IEQ), and cost perspectives.Results generally suggest that trading efficient local heating/cooling options for whole space conditioning has both energy and comfort benefits, saving up to 28% of monthly HVAC energy while improving the acceptability of thermal conditions in a Philadelphia climate. Nevertheless, cost analysis shows that the fuel source of conserved energy must be considered – particularly in the case of personal heater use, which adds to electric plug loads and associated utility and CO2 emissions cost penalties. Moreover, costs from even small changes in simulated occupant productivity tend to overwhelm energy costs, suggesting the need to improve the accuracy and precision of available productivity models across multiple seasons and climates.