BEAST Lab Research
- Advance the design and operation of building energy systems.
- Create and test novel and practical energy solutions
- Develop new modeling techniques for new and existing buildings.
- Energy efficiency and technologies in buildings
- Modeling, analysis, optimization and control of HVAC Systems
- Sustainable built environment, artificial intelligence applications and smart capabilities in building energy systems,
- Fault detection and diagnosis of cooling and heating energy systems
- Building mechanical systems and refrigeration systems
- Continuous and retro-commissioning of HVAC systems
- Renewable energy and thermal energy storage
IA single-duct variable air volume (VAV) system that serves multiple zones with different load profiles operates mostly with simultaneous cooling and heating. This is inherently inefficient. At any given condition, certain zones may request cooling and others may request heating. A single-duct VAV system supplies cold conditioned air to zones with cooling needs. The air may be reheated in other zones with heating needs. This can be avoided by using dual-duct, dual-VAV systems in which one system provides cooling and the other provides heating. The configuration will lead to significant reductions in cooling and heating loads on HVAC systems by eliminating simultaneous cooling and heating and using outdoor air for cooling and return air for heating..
Most buildings in the United States are equipped with traditional centralized variable air volume (VAV) systems that mix air returning from different zones and centrally recirculate a large portion of it back to zones. During the coronavirus pandemic, this recirculation could make the spaces less safe and increase the possibility of coronavirus transmission between zones in buildings. On other hand, distributed water source heat pumps (WSHPs) or any chilled water terminal unit integrated with a dedicated outdoor air system make the spaces safer by eliminating return air mixing and also may provide great energy efficiency if they are designed and operated properly. Thus, this research focuses on improving the design and operation of distributed WSHPs to achieve that desired higher energy efficiency.
Cooling with ice thermal storage can be the most cost-effective, reliable system approach to cooling different types of buildings. The ice thermal storage can reduce energy costs by shifting the cooling cost from on-peak to off-peak periods. The work discusses the optimal design of ice thermal storage and its impact on energy consumption, demand, and total energy cost. A tool for optimal design of ice storage is developed, considering variables such as chiller and ice storage sizes and charging and discharge times. Detailed simulation studies using real office building, including utility rate structure are presented. The study considers the effect of the ice thermal storage on the chiller performance and the associated energy cost and demonstrates the cost saving achieved from optimal ice storage design. A whole building energy simulation model is used to generate the hourly cooling load for both design day and entire year. Other collected variables such as condenser entering water temperature, chilled water leaving temperature, outdoor air dry bulb and wet bulb temperatures are used as inputs to a chiller model based on DOE-2 chiller model to determine the associated cooling energy use. The results show a significant cost energy saving can be obtained by optimal ice storage design through using the tool proposed in this research.
Advanced energy management control systems (EMCS) offer an excellent means of reducing energy consumption in heating, ventilating, and air conditioning (HVAC) systems while maintaining and improving indoor environmental conditions. This can be achieved through the use of computational intelligence and optimization with a building automation system and multiple sensors, which can be quite expensive. However, energy awareness and proper scheduling achieve the best opportunities to save energy with little to no cost for existing facilities. These “low-tech/no-cost” ideas are easily implemented and quickly reduce utility costs. This research includes actual utility data and information gathered over the past 20 years while performing energy audits at several K-12 Schools in North Carolina, discusses well known and documented control strategies that are rarely implemented in most school districts and universities, and will extrapolate savings for an entire school district based on real data. These processes can also be integrated into an EMCS to perform several intelligent functions achieving optimal system performance. This work focuses on control strategies utilizing time-of-day scheduling that can be used with 7-day programmable thermostats, electronic time controllers and a building automation system (BAS).
The research investigates the applications of CO2-based demand-controlled ventilation DCV strategy integrated with the economizer for air source heat pumps in schools, their impact on the annual energy consumption, and determines the potential savings achieved in different USA locations. The study includes detailed energy analysis on an existing middle school through whole building simulation energy software. The simulation model is first calibrated and checked for accuracy using actual monthly utility data. This model is then used for savings calculations resulted from a combination of air-side economizer and CO2-Based DCV and with various occupancy profiles and locations. The results show that a significant saving could be obtained as compared to the actual operating strategy implemented in the existing system and this saving depends mainly on the actual occupancy profile and building locations.
A large portion of energy use in buildings is attributed to air movement devices. Accurate estimation of fan performance is a key element in maximizing fan efficiency. This research proposes a new fan model that can be used in several applications such as optimization and fault detection, and can also be incorporated into any commercial building models. The model uses a numerical analysis based on an interpolation technique for the data generated by basic fan laws. It can use any two variables among all four variables of airflow rate, total fan pressure, speed, and power as inputs or outputs. Another advantage of this model is the flexibility of using any size of data for calibration, obtained either from manufacturers or field measured data. The model was tested for accuracy using two different manufacturers’ data of roof top unit packages with capacity ranging from 2 tons to 20 tons. Furthermore, the model was evaluated and tested on an actual VAV system using three months’ worth of measured data. The results show that the model can provide accurate estimation with the coefficient of variance (CV) less than 2% and it can be used for several applications.
Different types of air-to-air heat recovery technologies such as coil loop, heat pipe, sensible heat exchanger, and total energy wheel have been used in serval HVAC system applications. The selection of appropriate recovery technologies and operating strategy is very important to achieve the anticipated energy and cost savings. This work examines various types of exhaust energy recovery technologies that use to cool or heat ventilation airstream to reduce cooling or heating energy use and explores different capacity control strategies and their impacts on fan, cooling, and heating energy uses. The study includes detailed energy analysis for typical HVAC systems, equipped with ventilation and economizer arrangements and for different energy recovery types and control strategies at different climate zones. The study provides a useful guidance to select the right heat recovery types for particular applications with achieving the intended energy saving target. The results show that the optimal selection and operation of exhaust heat recovery technology leads to maximize energy savings by reducing the ventilation cooling and heating loads.
As our energy concerns continue to grow, the need for creating more efficient building systems with accurate modeling techniques has increased. Most modern buildings are equipped with electric power meters recording electric power data that can be used for model accuracy improvements. This resarch discuses typical data-based building energy models and proposes new improvements by utilizing data classifications. Six different data-based models for estimating sub-hourly and hourly electric energy consumptions are presented and discussed. Six different data-based models for estimating sub-hourly and hourly electric energy consumptions are presented and discussed.
Those models are three typical single to multiple regression models, two proposed regression models, and artificial neural network model with recommended classifications. Power data collected from existing buildings at 15 min interval are used to build and test the models. Additional hourly energy data obtained from a well-known energy simulation program are also used for detailed analysis. The results show that the proposed regression models and ANN model with recommended data classifications can provide very accurate results as compared to the traditional modeling techniques. Significant improvements in statistic index R-squared values are resulted by using the proposed regression and ANN models for all tested buildings.
This work presents modeling and optimization methodologies for a chilled water HVAC system using system identification methods. The developed model can be used for several applications such as control optimization, energy assessment, state estimation, and fault detection and diagnosis. The models can predict system responses for the performance of a chilled water air-handling unit. To train and test the models, data were collected from an existing building.
Different model structures were then tested along with various time delays and orders to determine the most optimal structure. An optimization method is also developed to automate the process of finding the best model structure that can produce the best accurate prediction against the actual data. The results show that the proposed models can yield accurate results for various energy efficient and saving estimation applications. The four model structures explored in this paper are the Autoregressive (AR) model, Nonlinear Autoregressive (NLAR) model, Autoregressive Moving Average (ARMA) model, and the State Space (SS) model. This section discusses the model structures for the four model structures candidates.
This research work proposes new measurement techniques for estimating the total and local leakages in residential buildings. Accurate estimations of the total and local leakages can help to focus the choice for both the right house and location in the duct system for performing the potential repair job. Proper information as to where the leakages are located inside the duct system can reduce the time required for the duct sealing task. This study uses detailed laboratory measurements to validate thetechniques. The laboratory, called air duct leakage laboratory ADLL, has two different air duct configurations and a wide range of leakage levels controlled by holes created at several locations in the ductwork. This work also includes a set of simulation results to provide an insight intodiffrent tecniques.
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