Computational intelligence techniques for HVAC systems: a review

Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO2 emissions. The drive to reduce energy use and associated greenhouse gas emissions from buildings has acted as a catalyst in the development of advanced computational methods for energy efficient design, management and control of buildings and systems. Heating, ventilation and air conditioning (HVAC) systems are the major source of energy consumption in buildings and an ideal candidate for substantial reductions in energy demand. Significant advances have been made in the past decades on the application of computational intelligence (CI) techniques for HVAC design, control, management, optimization, and fault detection and diagnosis. This article presents a comprehensive and critical review on the theory and applications of CI techniques for prediction, optimization, control and diagnosis of HVAC systems.The analysis of trends reveals the minimization of energy consumption was the key optimization objective in the reviewed research, closely followed by the optimization of thermal comfort, indoor air quality and occupant preferences. Hardcoded Matlab program was the most widely used simulation tool, followed by TRNSYS, EnergyPlus, DOE–2, HVACSim+ and ESP–r. Metaheuristic algorithms were the preferred CI method for solving HVAC related problems and in particular genetic algorithms were applied in most of the studies. Despite the low number of studies focussing on MAS, as compared to the other CI techniques, interest in the technique is increasing due to their ability of dividing and conquering an HVAC optimization problem with enhanced overall performance. The paper also identifies prospective future advancements and research directions

Temperature controller optimization by computational intelligence

In this paper a temperature control system for an automated educational classroom is optimized with several advanced computationally intelligent methods. Controller development and optimization has been based on developed and extensively tested mathematical and simulation model of the observed object. For the observed object cascade P-PI temperature controller has been designed and conventionally tuned. To improve performance and energy efficiency of the system, several meta heuristic optimizations of the controller have been attempted, namely genetic algorithm optimization, simulated annealing optimization, particle swarm optimization and ant colony optimization. Efficiency of the best results obtained with proposed computationally intelligent optimization methods has been compared with conventional controller tuning. Results presented in this paper demonstrate that heuristic optimization of advanced temperature controller can provide improved energy efficiency along with other performance improvements and improvements regarding equipment wear. Not only that presented methodology provides for determination and tuning of the core controller, but it also allows that advanced control concepts such as anti-windup controller gain are optimized simultaneously, which is of significant importance since interrelation of all control system parameters has important influence on the stability and performance of the system as a whole. Based on the results obtained, general conclusions are presented indicating that meta heuristic computationally intelligent optimization of heating, ventilation, and air conditioning control systems is a feasible concept with strong potential in providing improved performance, comfort and energy efficiency

Temperature controller optimization by computational intelligence

In this paper a temperature control system for an automated educational classroom is optimized with several advanced computationally intelligent methods. Controller development and optimization has been based on developed and extensively tested mathematical and simulation model of the observed object. For the observed object cascade P-PI temperature controller has been designed and conventionally tuned. To improve performance and energy efficiency of the system, several meta heuristic optimizations of the controller have been attempted, namely genetic algorithm optimization, simulated annealing optimization, particle swarm optimization and ant colony optimization. Efficiency of the best results obtained with proposed computationally intelligent optimization methods has been compared with conventional controller tuning. Results presented in this paper demonstrate that heuristic optimization of advanced temperature controller can provide improved energy efficiency along with other performance improvements and improvements regarding equipment wear. Not only that presented methodology provides for determination and tuning of the core controller, but it also allows that advanced control concepts such as anti-windup controller gain are optimized simultaneously, which is of significant importance since interrelation of all control system parameters has important influence on the stability and performance of the system as a whole. Based on the results obtained, general conclusions are presented indicating that meta heuristic computationally intelligent optimization of heating, ventilation, and air conditioning control systems is a feasible concept with strong potential in providing improved performance, comfort and energy efficiency

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

Building Temperature Control with Intelligent Methods

Temperature control is important for both human comfort and the need in industry. In the thesis, two good intelligent control methods are compared to find their advantages and disadvantages. Matlab is used as the tool to make models and process calculations. The building model is one simple room in Akwesasne in New York State and the target is to keep the temperature indoor around 22 degree Centigrade from 12/29/2013 to 12/31/2013. Heat pump is used to provide or absorb heat. All data in the experiments is from JRibal Environmental eXchange network and PJM. The first method is fuzzy neural network (FNN). With the control from fuzzy logic and the learning process in neural network, the temperature is kept around 22 degree Centigrade. Another method is model predictive control (MPC) with genetic algorithm (GA). And the temperature is also controlled around 22 degree Centigrade by predicting the temperature and solar radiation. In addition, the cost is saved by using genetic algorithm with an energy storage system added in the building model. In summary, FNN is easy to build but the result is not very accurate; while the result of MPC is more accurate but the model is hard to develop. And GA is a good optimization method

Development of an adaptive fuzzy logic controller for HVAC system

An adaptive approach to control a cooling coil chilled water valve operation, called adaptive fuzzy logic control (AFLC), is developed and validated in this study. The AFLC calculates the error between the supply air temperature and supply air temperature set point for air in an air handling unit (AHU) of a heating, ventilating, and air conditioning (HVAC) system and determines optimal fuzzy rule matrix to minimize the hydronic energy consumption while maintaining occupant comfort. The AFLC uses genetic algorithms and evolutionary strategies to determine the fuzzy rule matrix and fuzzy membership functions for an AHU in HVAC systems;Cooling coil models are developed using neural network, general regression neural network and lump capacitance methods to predict the supply air temperature. These models helped with the development of the adaptive fuzzy logic controller;Two types of validation experiments were conducted, one with cyclically changing supply air temperatures and the second with cyclically changing supply air flow rates. Experiments conducted on two identical real HVAC systems were used to compare the performances of the AFLC to a conventional proportional, integral and derivative (PID) controller. To remove bias between the testing systems, the controllers were switched from one system to the other;The validation experiments indicate that the HVAC system operated under the AFLC consumes 1 to 7 % less hydronic energy when compared with a conventional PID controlled system. More actuator travel distance was observed when using the AFLC. The AFLC maintained better occupant comfort conditions when compared with the conventional PID controller. It was observed that the controlled variable for the AFLC system required 0 to 185% more rise time, had 9 to 68% less overshoot and required 11 to 45% less settling time as compared to the conventional PID controlled system

Online HVAC Temperature and Air Quality Control for Cost-efficient Commercial Buildings Based on Lyapunov Optimization Technique

Commercial buildings consume up to 35.5% of total electricity consumed in the United States. As a subsystem in the smart building management system, Heating, Ventilation, and Air Conditioning (HVAC) systems are responsible for 45% of electricity consumption in commercial buildings. Therefore, energy management of HVAC systems is of interest. The HVAC system brings thermal and air quality comfort to the occupants of the building, designing a controller that maximizes this comfort is the first objective. Inevitably, ideal comfort tracking means more energy consumption and energy cost. Hence, the more advanced objective is balancing the comfort-cost tradeoff. Since HVAC systems have nonlinear, complex and MIMO characteristics, modeling the system and formulating an optimization problem for them is challenging. Moreover, there are physical and comfort constraints to be satisfied, and randomness of parameters such as thermal disturbances, number of occupants in the building that affects the air quality, thermal and air quality setpoints we want to track, electricity price and outside temperature to be considered. Adding real time analysis to this problem furthers the challenge. In this thesis, utilizing Lyapunov optimization technique, we first transform the constraints to stability equations, and formulate a stochastic optimization problem, then we minimize the time average of the expected cost of the system while the cost is a weighted sum of the discomfort and energy cost. Results show that using the proposed algorithm and real data, the algorithm is feasible, and an optimal solution for the problem is achieved

Advances in PID Control

Since the foundation and up to the current state-of-the-art in control engineering, the problems of PID control steadily attract great attention of numerous researchers and remain inexhaustible source of new ideas for process of control system design and industrial applications. PID control effectiveness is usually caused by the nature of dynamical processes, conditioned that the majority of the industrial dynamical processes are well described by simple dynamic model of the first or second order. The efficacy of PID controllers vastly falls in case of complicated dynamics, nonlinearities, and varying parameters of the plant. This gives a pulse to further researches in the field of PID control. Consequently, the problems of advanced PID control system design methodologies, rules of adaptive PID control, self-tuning procedures, and particularly robustness and transient performance for nonlinear systems, still remain as the areas of the lively interests for many scientists and researchers at the present time. The recent research results presented in this book provide new ideas for improved performance of PID control applications

Optimal tuning of proportional integral derivative controller for simplified heating ventilation and air conditioning system

A Heating Ventilation and Air Conditioning system (HVAC) is an equipment that is designed to adapt and adjust the humidity as well as temperature in various places. To control the temperature and humidity of the HVAC system, various tuning methods such as Ziegler–Nichols (Z-N), Chien-Hrones-Reswick (CHR), trial and error, robust response time, particle swarm optimization (PSO) and radial basis function neural network (RBF-NN) were used. PID is the most commonly used controller due to its competitive pricing and ease of tuning and operation. However, to effectively control the HVAC system using the PID controller, the PID control parameters must be optimized. In this work, the epsilon constraint via radial basis function neural network method is proposed to optimize the PID controller parameters. The advantages of using this method include fast and accurate response and follow the target values compared to other tuning methods. This work also involves the estimation of the dynamic model of the HVAC system. The non-linear decoupling method is used to modify the model of HVAC system. The benefits of using the proposed simplification technique rather than other techniques such as the relative gain array techniques (RGA) is because of its simplification, accuracy, and reduced non-linear components and interconnection effect of the HVAC system. It is observed that the amount of integral absolute error (IAE) for temperature and humidity based on the simplified model are decreased by 18% and 20% respectively. Moreover, it is revealed that optimization of PID controller through multi objective epsilon constraint method via RBF NN of the simplified HVAC system based on non-linear decoupling method shows better transient response and reaches better dynamic performance with high precision than other PID control tuning techniques. The proposed optimum PID controller and estimation of dynamical model of the HVAC system are compared with the different tuning techniques such as RBF and ZN based on original system. It is observed that the energy cost function due to temperature (JT) and humidity (JRH) are lowered by 15.7% and 4.8% respectively; whereas the energy cost functions reflect the energy consumptions of temperature and humidity which are produced by the humidifier and heating coil. Therefore, based on the new optimization method the energy efficiency of the system is increased. The unique combination of epsilon constraint method and RBF NN has shown that this optimization method is promising method for the tuning of PID controller for non-linear systems