Building Control Virtual Test Bed Research Articles

Experimental and Co-Simulation Performance Evaluation of an Earth-to-Air Heat Exchanger System Integrated into a Smart Building

Building models and their connected subsystems are often simulated as standalone entities. However, in order to monitor a system′s reactions to changing parameters and to assess its energy efficiency, it must be exposed to the actual dynamic context of the building under study. Hence, frameworks assessing co-operative simulation of buildings and their subsystems should be used. In this study, the Building Control Virtual Test Bed (BCVTB) framework was used for co-simulation of a small-scale building (EEBLab) connected to an Earth-to-air heat exchanger (EAHE). The EnergyPlus tool was used to simulate the indoor air temperature variations within the EEBLab, and MATLAB was used to model the EAHE system and to calculate its performance based on various parameters. The HOLSYS internet of things platform was deployed to monitor and collect the experimental data from the sensors to validate the simulations. A favorable agreement between the experimental and simulation results was obtained, showing the contribution of the small-scale EAHE system in maintaining a comfortable indoor temperature range inside EEBLab. Moreover, it demonstrated the effectiveness and accuracy of the proposed approach for integrated building co-simulation and performance evaluation.

Building control virtual test bed and functional mock-up interface standard: comparison in the context of campus energy modelling and control

ABSTRACT This paper compares Building Control Virtual Test Bed (BCVTB) and Functional Mock-up Interface (FMI) standard to conduct coupled energy modelling and control for academic and research campuses. The intent is to systematically compare BCVTB and FMI. As proof-of-concept demonstration, building energy models of Colorado School of Mines are developed in EnergyPlus and coupled through BCVTB and FMI. The coupled models in BCVTB and FMI represent a virtual campus and they are compared from different perspectives such as simulation runtime, easiness of employing simple if–then–else control, easiness of integrating other programs and feasibility to employ advanced control and optimization. Overall, FMI standard is more promising due to its (a) capability to be programmed in a multithreading manner, (b) capability to be implemented as Gauβ–Seidl and Jacobi methods of loose coupling and (c) higher feasibility to implement advanced control and optimization programs.

Optimal Scheduling of Air Conditioners for Energy Efficiency

Energy saving is one of the most important research hotspots, by which operational expenditure and CO2 emission can be reduced. Optimal cooling capacity scheduling in addition to temperature control can improve energy efficiency. The main contribution of this work is modeling the telecommunication building for the fabric cooling load to schedule the operation of air conditioners. The time series data of the fabric cooling load of the building envelope is taken by simulation by using Energy Plus, Building Control Virtual Test Bed (BCVTB), and Matlab. This pre-computed data and other internal thermal loads are used for scheduling in air conditioners. Energy savings obtained for the whole year are about 4% to 6% by simulation and the field study, respectively.

Lightweight and adaptive building simulation (LABS) framework for integrated building energy and thermal comfort analysis

Coupled and distributed simulation helps in understanding the complexity arising from the combined effects of interdependent systems, by connecting and exchanging information across several software programs. In the building energy analysis domain, several tools have been created in the past to facilitate such analyses. However, the existing coupling frameworks such as Building Control Virtual Test Bed (BCVTB), MLE+, High-Level Architecture (HLA), and Functional Mockup Unit are characterized by their inherent complexity, making it a challenge for the building practitioners to widely deploy them in everyday decision-making. In addition, several of these frameworks embody tight coupling, which means they lack the flexibility to incorporate models and components of decision-makers’ choice. This study addresses these gaps by proposing a Lightweight and Adaptive Building Simulation (LABS) framework that capitalizes on Lightweight Communications and Marshalling (LCM), an inter-process communication framework widely used by the robotics community. As a case study demonstrating this new framework, a building energy simulation model is coupled with an agent-based occupant behavior model to understand the energy effects of occupants’ thermal comfort related actions (e.g., adjusting the thermostat set point). These behavioral patterns are also influenced by various interventions (e.g., peer pressure, energy-based education) that may occur in the building. Measuring these effects in a real building for a lengthy period is impractical and resource-intensive and the LABS framework can be used for understanding this system better. The results also highlight opportunities for achieving energy savings by influencing the occupants’ comfort related behavior.

A Long-Short Term Memory Recurrent Neural Network Based Reinforcement Learning Controller for Office Heating Ventilation and Air Conditioning Systems

Energy optimization in buildings by controlling the Heating Ventilation and Air Conditioning (HVAC) system is being researched extensively. In this paper, a model-free actor-critic Reinforcement Learning (RL) controller is designed using a variant of artificial recurrent neural networks called Long-Short-Term Memory (LSTM) networks. Optimization of thermal comfort alongside energy consumption is the goal in tuning this RL controller. The test platform, our office space, is designed using SketchUp. Using OpenStudio, the HVAC system is installed in the office. The control schemes (ideal thermal comfort, a traditional control and the RL control) are implemented in MATLAB. Using the Building Control Virtual Test Bed (BCVTB), the control of the thermostat schedule during each sample time is implemented for the office in EnergyPlus alongside local weather data. Results from training and validation indicate that the RL controller improves thermal comfort by an average of 15% and energy efficiency by an average of 2.5% as compared to other strategies mentioned.

Semantic Inference-Based Control Strategies for Building HVAC Systems Using Modelica-Based Physical Models

The focus of this paper is on an integrated approach for HVAC modeling (semantic and physical), optimization, and control. It utilizes the Building Control Virtual Test Bed (BCVTB) platform to integrate ontologies along with rule sets into Model Predictive Control (MPC) routines and Modelica-based simulations. The ontologies represent the data in the form of semantic models of the domain. They include concepts and the relationships between them, i.e., mechanical equipment, zones, and sensors. While the MPC ensures optimized physical control, the ontologies and rule sets are responsible for data-driven and inference-based semantic control. The Web Ontology Language (OWL) is used to describe the ontologies, and Jena API was used as the framework to create the ontologies and define the rule sets.

기상 예보 데이터와 일사 예측 모델식을 활용한 실시간 에너지 수요예측

This study was designed to investigate a method for short-term, real-time energy demand prediction, to cope with changing loads for the effective operation and management of buildings. Through a case study, a novel methodology for real-time energy demand prediction with the use of weather forecasting data was suggested. To perform the input and output operations of weather data, and to calculate solar radiation and EnergyPlus, the BCVTB (Building Control Virtual Test Bed) was designed. Through the BCVTB, energy demand prediction for the next 24 hours was carried out, based on 4 real-time weather data and 2 solar radiation calculations. The weather parameters used in a model equation to calculate solar radiation were sourced from the weather data of the KMA (Korea Meteorological Administration). Depending on the local weather forecast data, the results showed their corresponding predicted values. Thus, this methodology was successfully applicable to anywhere that local weather forecast data is available.

Simulation-Based BEMS Control Through BCVTB and Its Application to a Ventilation System Under Operation

This paper aims to establish a simulation-based control system for better building performance with low energy cost during the operational stage. This integrated system utilizes BCVTB (Building Control Virtual Test Bed) as a middleware to connect a BEMS(Building Energy Management System) and simulation programs, including monitoring and measurement devices, an analysis model (EnergyPlus), and control algorithms. In addition, the co-simulation between EnergyPlus and Matlab/Simulink is conducted as well. The integrated system is applied to a mechanical ventilation system in a residential building. This simulation-based control approach shows energy saving potentials while maintaining good indoor air quality in the case study.

Feasibility study on a novel methodology for short-term real-time energy demand prediction using weather forecasting data

This study was designed to investigate a method for short-term, real-time energy demand prediction to cope with changing loads for the effective operation and management of buildings. Through a case study, a novel methodology for real-time energy demand prediction with the use of weather forecasting data was suggested. To perform the input and output operations of weather data, and to calculate solar radiation and EnergyPlus, a BCVTB (Building Control Virtual Test Bed) was designed. The BCVTB was used to predict daily energy demand, based on four kinds of real-time weather data and two kinds of solar radiation calculations. Weather parameters that were used in a model equation to calculate solar radiation were sourced from weather data of the KMA (Korea Meteorological Administration). After conducting energy demand prediction for four days, it was found that all inputted weather data have an effect on the prediction results. These data were applied to real buildings in order to examine their validity. The information data exchange between real-time weather data and simulation data was carried out fairly through the BCVTB.

A Virtual Model for Civil Building Electric Control Based on Matlab Controller

The purpose of this study is correct and effective model predictive control (MPC), in reduce energy demand and cost of buildings in power network and use pricing -- a demand and charges. A virtual model, for a single floor, the article commercial buildings equipped with variable air volume (VAV) based on Energyplus cooling system. Real-time data between the realization of the switch Energyplus and Matlab into building control virtual test bed (BCVTB) as a middleware. System identification technology is to realize get zone temperature and the power function model, which is used in the MPC framework. With economic target, target function is formulated as a linear programming problem and solves the problem. In the rush hour pre-cooling effect and independent cooling unloaded from building thermal mass in supply and demand can be observed during a consecutive weekly simulation. The MPC brings the cost comparison, the control strategy and other are preprogrammed baseline.

Comparison of building load performance between first principle based and implementable shading control algorithms

This paper presents the findings on the impact of two window shading control strategies on office building thermal and lighting loads over a year’s period through energy simulation in four different climatic contexts in the United States. While it is well known that window shades affect heat gain and loss through windows and thus building energy loads, there is limited information on the actual magnitude and specific behavior of their effects on the building thermal and lighting loads. The objective of the described study is to generalize the magnitude of the impact from window shading control strategies on building thermal and lighting loads over a year by modeling a representative office building construction in indicative geographical locations as well as internal window shades representative of those that are commonly used. Two control algorithms are developed and evaluated which are the ideal algorithm and implementable. The basic principle of both algorithms is to maximize heat gain and minimize heat loss when the building is in heating condition, and performs the opposite when the building is in cooling condition, subject to glare control. Some of the information that is used in the ideal algorithm is not available for a “real world” controller. Therefore, an implementable shading control algorithm is also described, which will take sensor measurable conditions as input. EnergyPlus is used to conduct the thermal and lighting loads computation. The control algorithm is implemented through the BCVTB (Building Control Virtual Test Bed). BCVTB is a software environment that allows expert users to couple different simulation programs for distributed simulation.