Model Predictive Control of a Radiant Floor Cooling System in an Office Space

This study presents an optimal control formulation for the operation of a radiant floor system in an open plan office space with an air-cooled chiller as a source. A simulation case study with different control schemes is used to evaluate the potential of the model predictive control for the radiant floor as well as the optimal control coordination of a radiant and air comfort delivery system. The comparison with a reference case of proportional control shows a saving potential for the radiant floor of around 10 to 15.8 %, which results from maintaining the temperature at upper bound and precooling or load shifting. Optimal control coordination of radiant floor and air system yields additional saving of around 2 %. The proposed intuitive formulation of linear programming can be implemented to other control problems with a linear building model and known COP with respect to weather prediction. The formulation is applicable to other complex systems with two or more control systems such as open-plan spaces with several control units or multiple zones (or buildings) with centralized plant

An Agent-based Control Implementation for the Optimal Coordination of Multiple Rooftop Units

Model predictive control (MPC) has been a popular advanced supervisory control approach for optimizing the operations of building heating, ventilation and air-conditioning (HVAC) systems, with the objectives of reducing energy consumption and delivering better comfort. However, centralized MPC designs are often 1) not scalable to the increasing sizes of the building systems, 2) not adaptive to subsystem addition/attrition, i.e., ‘Plug-and-Play’ implementation. Agent-based approaches, such as distributed model predictive control (DMPC), are attractive alternative. In this paper, taking a multiple rooftop units (RTU) coordination problem as a case study, we experimentally investigate the energy saving potential by implementing an agent-based DMPC strategy to coordinate the operations of multiple ‘virtual’ variable-speed RTUs with diverse unit efficiencies (COP) in an open space with multiple sub-zones. The operations of three RTUs are emulated by three groups of variable air volume (VAV) diffusers that can be individually controlled to provide continuously changing sensible cooling loads into respective zones. A multi-zone model that accurately captures the thermal interactions of different zones for control purposes is developed. This model takes the sensible cooling loads provided by the three ‘virtual’ RTUs as controllable inputs, and ambient temperature, solar radiation, internal heat gains (occupancy, plug, lighting and equipment loads) as exogenous inputs. Three laptop computers are dispatched into the three thermal zones as local agents. A server computer connected to both the Building Automation System (BAS) and the outside internet is responsible for predicting various exogenous inputs and exchanging information with the local agents. Experimental results show that the proposed agent-based DMPC design and implementation are able to achieve over 20% cost savings, in terms of electricity consumption charge with Time-of-Use pricing schedules, while at the same time maintaining local occupancy comfort. The savings can be further broken down into two parts: 1) utilizing the RTUs with higher overall unit efficiency 2) shifting the aggregate cooling load of the room into periods with lower electricity price or higher RTUs’ COP

Agent-based approach for system identification and optimal control of high performance buildings

Commercial buildings have strong impacts on humans and the environment. They not only affect occupants’ comfort, health, and well-being but also consume more than 19% of the total energy consumption in the US. High performance building designs can achieve significant energy savings, with new building technologies such as advanced building envelopes, thermally activated building systems, on-site power production and thermal storage; dynamic effects related to variability in occupancy and environmental conditions; diversity in occupant thermal preferences; and the integration of these diverse technologies into an overall control system design. Model-based predictive control (MPC) is a promising approach for the realization of high performance buildings as operations can be optimized for the specific building and climate through an estimated process model that predicts the future evolution of the system, while incorporating the most up-to-date information on weather forecast and system dynamics. Despite of the advantages, there are still significant obstacles associated with the realization of MPC implementation in actual buildings. First of all, the process of generating a control-oriented building model, which is referred as system identification, can be complex and not easily reproduced, due to the customized design of buildings and HVAC systems. Also, MPC computation could become intractable due to the large decision dimension for large-scale systems. To date, the formulation, solution, and integration of optimal controls into existing building management systems (BMS), may not be easily scalable to other buildings on account of the design customization and control intractability. It is envisioned that in the future, with new technology for sensing, information processing and communication, distributed intelligence would be embedded into devices and would be widely deployed into actual buildings. Towards the realization of this plug-and-play intelligent building operation, the research objective of this thesis is to develop a multi-agent system approach to optimal control of high performance buildings, based on new algorithms for distributed system identification and distributed model predictive control (DMPC). From the application perspective, the focus is thermal environment control of open-plan office spaces. Radiant floor systems are evaluated as high performance features and used as test-beds to demonstrate the proposed agent-based framework for zone and local environment control. As a first step, a multi-agent systems approach for data-driven grey-box building models is introduced. Each zone is divided into sub-systems (agents), and a parameter set for each subsystem is first estimated individually, and then integrated into an inverse model for the zone using the dual decomposition algorithm. Two case-studies are designed and conducted using the Living Laboratories at Purdue’s Herrick Building as test-beds to validate the estimated control-oriented models under realistic operation conditions. The results show that the model prediction accuracy of the new approach is fairly good for implementation in predictive control while models can be developed and integrated with improved efficiency, flexibility and scalability, compared to centralized approaches. In the next step, a centralized MPC strategy is developed for zone thermal environment control in an occupied office space with radiant comfort delivery along with a chiller and boiler as HVAC sources. The MPC controller deploys an optimization algorithm based on constraint quadratic programming with hard comfort bounds, which yields an exact numerical solution, and it is straight forward and robust for this application. Results from the MPC implementation during the cooling season show that more than 34% cost savings are achieved by load shifting to utilize higher chiller efficiency with lower outdoor air temperature, and lower electricity prices. In the heating application, the energy use reduction from the optimized control is around 16% compared to conventional control. In the final step, a distributed optimization algorithm, inspired by the Proximal Jacobian Alternating Direction Method of Multipliers (PJ-ADMM), is introduced. It includes multiple MPCs run iteratively while exchanging control input information until they converge. With this tractable approach, agents solve individual optimization problems in parallel, through information exchange and broadcasting, with a smaller scale of the input and constraints, facilitating optimal solutions with improved efficiency. The developed algorithm is tested using field data from an occupied open-plan office space with localized comfort delivery along with distributed sensing, control, and data communication capabilities. The radiant comfort delivery system with predictive control is capable of providing localized thermal environments, thereby improving occupant satisfaction, while achieving more than 27% reduction in electricity consumption compared to baseline feedback control. In summary, this thesis introduces a new agent-based approach for system identification and MPC, which is implemented and tested using an actual building as test-bed. The results show significantly improved performance compared to conventional systems and controls. The overall methodology could be packaged into a toolbox integrated into open-source building control platforms, existing building management systems, or embedded into new smart devices. It is a scalable solution that can be extended to other smart and connected environments, e.g., multiple building systems, multi-zone buildings, building clusters integrated with power grids and automobiles

Simulation case study of stack pressure impact on thermal load in high-rise buildings

The reference building models in the EnergyPlus tool do not consider the stack effect. This is not an issue for small and low-rise buildings. However, this is significant in evaluating the heating and cooling load of high-rise buildings. This study evaluates and quantifies this stack effect in building energy simulation, EnergyPlus. The high-rise commercial building model in the EnergyPlus reference building set was used as a simulation test bed. The vertical multi-stories shaft zones were added inside the core zone in the existing building model. Then, the airflow network model was set in EnergyPlus so the internal airflow including the vertical and horizontal directions through the shaft zone as well as the core and perimeter zones were configured. The simulation model was calibrated against the actual pressure distribution between the core and perimeter zones in all floors of high-rise buildings from the literature. This model calibration was conducted manually by changing the discharge coefficient of the openings between thermal zones. The heating and cooling load of the proposed model were compared to the existing model which does not consider the vertical airflow through the shaft zones. The quantification with relative load mismatch was analyzed by floors and environment including the temperature difference between indoor and outdoor, and wind speed. Moreover, the impact of the air mixing and infiltration on the heating and cooling load was quantified. The result of this study reveals the significance of the stack effect in calculating the heating and cooling load

A Sensing-Based Visualization Method for Representing Pressure Distribution in a Multi-Zone Building by Floor

Airflow in a multi-zone building can be a major cause of pollutant transfer, excessive energy consumption, and occupants discomfort. The key to monitoring airflows and mitigating related problems is to obtain a comprehensive understanding of pressure relationships within the buildings. This study proposes a visualization method for representing pressure distribution within a multi-zone building by using a novel pressure-sensing system. The system consists of a Master device and a couple of Slave devices that are connected with each other by a wireless sensor network. A 4-story office building and a 49-story residential building were installed with the system to detect pressure variations. The spatial and numerical mapping relationships of each zone were further determined through grid-forming and coordinate-establishing processes for the building floor plan. Lastly, 2D and 3D visualized pressure mappings of each floor were generated, illustrating the pressure difference and spatial relationship between adjacent zones. It is expected that the pressure mappings derived from this study will allow building operators to intuitively perceive the pressure variations and the spatial layouts of the zones. These mappings also make it possible for operators to diagnose the differences in pressure conditions between adjacent zones and plan a control scheme for the HVAC system more efficiently

Simulation-based comparative analysis of U-value of field measurement methods

Accurate analysis of building energy performance necessitates methodologies that can diagnose the thermal performance of a building's envelope. The building envelope contains various uncertainties and poses difficulties in verifying the measurement results, making it crucial to ensure the reliability of the measurement method. This study used dynamic simulations to verify the accuracy and reproducibility of commonly used field measurement methods. The simulation data were applied to the heat flux meter (HFM) and infrared thermography (IRT) methods to calculate the thermal transmittance of the building envelope and confirm their suitability as a field measurement method. According to the accuracy verification results, the HFM and indoor IRT (IRTi) methods, which are less affected by the external environment, evaluated the actual thermal performance of the wall close to the theoretical value with average relative errors of 3.3% and 4.2%, respectively. In the reproducibility evaluation, the HFM method exhibited similar levels of deviation over time. Additionally, to reduce the deviation in the reproducibility of the thermal transmittance derived from the IRT method, the average method was applied for data analysis, leading to a decrease in the reproducibility deviation from 36.5% to 13.3% for IRTi and from 107.3% to 71.8% for IRT outdoors (IRTo)

Development of Simplified Building Energy Prediction Model to Support Policymaking in South Korea—Case Study for Office Buildings

This study aims to support building energy policymaking for office buildings in South Korea through regression models by considering the global temperature rise. The key variables representing building energy standards and codes are selected, and their impact on the annual energy consumption is simulated using EnergyPlus reference models. Then, simplified regression models are built on the basis of the annual energy consumption using the selected variables. The prediction performance of the developed model for forecasting the annual energy consumption of each reference building is good, and the prediction error is negligible. An additional global coefficient is estimated to address the impact of increased outdoor air temperature in the future. The final model shows fair prediction performance with global coefficients of 1.27 and 0.9 for cooling and heating, respectively. It is expected that the proposed simplified model can be leveraged by non-expert policymakers to predict building energy consumption and corresponding greenhouse gas emissions for the target year