Intelligent Approaches For Modeling And Optimizing Hvac Systems

Advanced energy management control systems (EMCS), or building automation systems (BAS), 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. This research will evaluate model-based optimization processes (OP) for HVAC systems utilizing MATLAB, genetic algorithms and self-learning or self-tuning models (STM), which minimizes the error between measured and predicted performance data. The OP can be integrated into the EMCS to perform several intelligent functions achieving optimal system performance. The development of several self-learning HVAC models and optimizing the process (minimizing energy use) will be tested using data collected from the HVAC system servicing the Academic building on the campus of NC A&T State University. Intelligent approaches for modeling and optimizing HVAC systems are developed and validated in this research. The optimization process (OP) including the STMs with genetic algorithms (GA) enables the ideal operation of the building’s HVAC systems when running in parallel with a building automation system (BAS). Using this proposed optimization process (OP), the optimal variable set points (OVSP), such as supply air temperature (Ts), supply duct static pressure (Ps), chilled water supply temperature (Tw), minimum outdoor ventilation, reheat (or zone supply air temperature, Tz), and chilled water differential pressure set-point (Dpw) are optimized with respect to energy use of the HVAC’s cooling side including the chiller, pump, and fan. HVAC system component models were developed and validated against both simulated and monitored real data of an existing VAV system. The optimized set point variables minimize energy use and maintain thermal comfort incorporating ASHRAE’s new ventilation standard 62.1-2013. The proposed optimization process is validated on an existing VAV system for three summer months (May, June, August). This proposed research deals primarily with: on-line, self-tuning, optimization process (OLSTOP); HVAC design principles; and control strategies within a building automation system (BAS) controller. The HVAC controller will achieve the lowest energy consumption of the cooling side while maintaining occupant comfort by performing and prioritizing the appropriate actions. Recent technological advances in computing power, sensors, and databases will influence the cost savings and scalability of the system. Improved energy efficiencies of existing Variable Air Volume (VAV) HVAC systems can be achieved by optimizing the control sequence leading to advanced BAS programming. The program’s algorithms analyze multiple variables (humidity, pressure, temperature, CO2, etc.) simultaneously at key locations throughout the HVAC system (pumps, cooling coil, chiller, fan, etc.) to reach the function’s objective, which is the lowest energy consumption while maintaining occupancy comfort

Improvements and Applications of the Methodology for Potential Energy Savings Estimation from Retro-commissioning/Retrofit Measures

This thesis has improved Baltazar's methodology for potential energy savings estimation from retro-commissioning/retrofits measures. Important improvements and discussions are made on optimization parameters, limits on optimization parameter values, minimum airflow setting for VAV systems, space load calculation, simulation of buildings with more than one type of system, AHU shutdown simulation, and air-side simulation models. A prototype computer tool called the Potential Energy Savings Estimation (PESE) Toolkit is developed to implement the improved methodology and used for testing. The implemented methodology is tested in two retro-commissioned on-campus buildings with hourly measured consumption data. In the Sanders Corps of Cadets Center, the optimized profiles of parameter settings in single parameter optimizations can be explained with engineering principles. It reveals that the improved methodology is implemented correctly in the tool. The case study on the Coke Building shows that the improved methodology can be used in buildings with more than one system type. The methodology is then used to estimate annual potential energy cost savings for 14 office buildings in Austin, TX with very limited information and utility bills. The methodology has predicted an average total potential savings of 36% for SDVAV systems with electric terminal reheat, 22% for SDVAV systems with hot water reheat, and 25% for DDVAV systems. The estimations are compared with savings predicted in the Continuous Commissioning assessment report. The results show it may be helpful to study the correlation by using generalized factors of assessment predicted energy cost savings to estimated potential energy cost savings. The factors identified in this application are 0.68, 0.66, and 0.61 for each type of system. It is noted that one should be cautious in quoting these factors in future projects. In the future, it would be valuable to study the correlation between measured savings and estimated potential savings in a large number of buildings with retrocommissioning measures implemented. Additionally, further testing and modifications on the PESE Toolkit are necessary to make it a reliable software tool

Hvac Supply Air Optimization Using Evolutionary Algorithms

Heating, ventilation, and air-conditioning account for a vast majority of energy consumption in the residential and commercial sectors. Intelligent energy management control system (EMCS) in buildings offers an excellent means of reducing energy consumption in heating, ventilation, and air-conditioning (HVAC) systems while maintaining or improving indoor environmental conditions. This can be achieved through the use of computational intelligence and optimization. This project proposes and evaluates a model-based optimization process for HVAC systems using an evolutionary algorithm. The process can be integrated into the EMCS to perform several intelligent functions and achieve optimal whole-system performance. The proposed process addresses the requirements of the latest ASHRAE Standard 62.1. A whole building simulation energy software is used to generate the sub hourly load. The simulations are performed to test the process and determine the potential energy savings achieved. In addition, simulations were conducted at peak load on July 15th and partial load on April 10th to observe the effects of genetic algorithm (GA). Through artificial intelligence utilization, the energy consumption can be better managed. Building controls are like living organisms which can be treated much like evolutionary biology during programming. The single-objective GA optimization and modernized ventilation codes have demonstrated that total energy consumed by the HVAC system can be reduced by 30.6% for the air side distribution

Hvac Supply Air Optimization Using Evolutionary Algorithms

Heating, ventilation, and air-conditioning account for a vast majority of energy consumption in the residential and commercial sectors. Intelligent energy management control system (EMCS) in buildings offers an excellent means of reducing energy consumption in heating, ventilation, and air-conditioning (HVAC) systems while maintaining or improving indoor environmental conditions. This can be achieved through the use of computational intelligence and optimization. This project proposes and evaluates a model-based optimization process for HVAC systems using an evolutionary algorithm. The process can be integrated into the EMCS to perform several intelligent functions and achieve optimal whole-system performance. The proposed process addresses the requirements of the latest ASHRAE Standard 62.1. A whole building simulation energy software is used to generate the sub hourly load. The simulations are performed to test the process and determine the potential energy savings achieved. In addition, simulations were conducted at peak load on July 15th and partial load on April 10th to observe the effects of genetic algorithm (GA). Through artificial intelligence utilization, the energy consumption can be better managed. Building controls are like living organisms which can be treated much like evolutionary biology during programming. The single-objective GA optimization and modernized ventilation codes have demonstrated that total energy consumed by the HVAC system can be reduced by 30.6% for the air side distribution

Online adaptive and intelligent control strategies for multizone VAV systems

Nearly one half of the total energy used in buildings is consumed by HVAC systems. With escalating cost of energy, several energy efficiency strategies have been implemented in buildings. Among these, the use of VAV systems, and improved method of controlling such systems have received greater attention. This thesis is devoted to design and development of online adaptive control strategies which will be augmented with optimal and intelligent-control algorithms. The considered VAV system consists of zone air temperature control, discharge air temperature control, water temperature control and air pressure control loops. Online adaptive control strategies are developed for these control loops. In order to design reliable online controls a robust RLS identification algorithm for estimating the parameters of the modeled processes is developed. It is shown that this algorithm avoids wrong estimation and requires fewer variables compared with classical RLS techniques. Three different online control strategies were designed. These are: a robust optimal control algorithm (ROCA), a simplified optimal adaptive control (SOAC) for FOPDT systems, and a two-loop adaptive control strategy which improves both temperature and airflow regulations in VAV systems. ROCA is an on-line optimal proportional-integral plus feedforward controller tuning algorithm for SISO thermal processes in HVAC systems. It was optimized by combining the H {592} based PI tuning It is shown that the two-loop adaptive control strategy has both stronger robustness to time-varying thermal loads and lower sensitivity to airflow rate changes into other zones. The developed control strategies were tested by simulation and experiments in a VAV laboratory test facility which uses existing energy management control systems used in commercial buildings. Also, an adaptive neural network controller is developed. The proposed controller was constructed by augmenting the PID control structure with a neural network control algorithm and an adaptive balance parameter. Simulation results show that the proposed controller has stronger robustness, improved regulation and tracking functions for FOPDT type plants compared to classical PID controllers. Experiments were conducted to verify the characteristics of the developed controller on the DAS in a two-zone VAV test facility. Applications of the developed control strategies to different control loops in VAV system were demonstrated by conducting several experimental tests under realistic operating condition

Achieving Better Building Performance and Savings Using Optimal Control Strategies

The Continuous Commissioning (CCSM) process has become a very important energy conservation topic for new and existing commercial buildings. This process can yield substantial operating savings, improved indoor air quality, and enhanced occupant comfort. It also provides solutions to reoccurring building maintenance problems. One tool that can be implemented during commissioning work is a nearoptimal global set point method in an Energy Management Control System (EMCS) Direct Digital Controller (DDC). This algorithm is based on mathematical models for the chillers, boilers, chilled and hot water pumps, and air handler fans that relate the power of these components as a function of the chilled water and hot water differential temperature. The algorithm will minimize the total plant power consumption. These optimal control strategies make the CC process more effective. The Texas A&M University Systems State Headquarters is an office building, with a total floor area of approximately 123,960 ft2. An integrated commissioning of the HVAC systems was performed for this building. This paper describes the commissioning activities and demonstrates how newly developed optimized control strategies improved the building comfort conditions and reduced utility costs during and after the commissioning period

Designing an occupancy flow-based controller for airport terminals

One of the most cost-effective ways to save energy in commercial buildings is through designing a dedicated controller for adjusting environmental set-points according occupancy flow. This paper presents the design of a fuzzy rule-based supervisory controller for reducing energy consumptions while simultaneously providing comfort for passengers in a large airport terminal building. The inputs to the controller are the time schedule of the arrival and departure of passenger planes as well as the expected number of passengers, zone global illuminance (daylight) and external temperature. The outputs from the controller are optimised temperature, airflow and lighting set-point profiles for the building. The supervisory controller was designed based on expert knowledge in MATLAB/Simulink, and then validated using simulation studies. The simulation results demonstrate significant potential for energy savings in the controller's ability to maintain comfort by adjusting set-points according to the flow of passengers. Practical application : The systematic approach adopted here, including the use of artificial intelligence to design supervisory controllers, can be extended to other large buildings which have variable but predictable occupancy patterns like the restricted area of the airport terminal building

Energy efficiency in California laboratory-type facilities

The central aim of this project is to provide knowledge and tools for increasing the energy efficiency and performance of new and existing laboratory-type facilities in California. We approach the task along three avenues: (1) identification of current energy use and savings potential, (2) development of a {ital Design guide for energy- Efficient Research Laboratories}, and (3) development of a research agenda for focused technology development and improving out understanding of the market. Laboratory-type facilities use a considerable amount of energy resources. They are also important to the local and state economy, and energy costs are a factor in the overall competitiveness of industries utilizing laboratory-type facilities. Although the potential for energy savings is considerable, improving energy efficiency in laboratory-type facilities is no easy task, and there are many formidable barriers to improving energy efficiency in these specialized facilities. Insufficient motivation for individual stake holders to invest in improving energy efficiency using existing technologies as well as conducting related R&D is indicative of the ``public goods`` nature of the opportunity to achieve energy savings in this sector. Due to demanding environmental control requirements and specialized processes, laboratory-type facilities epitomize the important intersection between energy demands in the buildings sector and the industrial sector. Moreover, given the high importance and value of the activities conducted in laboratory-type facilities, they represent one of the most powerful contexts in which energy efficiency improvements stand to yield abundant non-energy benefits if properly applied