Review of Intelligent Control Systems for Natural Ventilation as Passive Cooling Strategy for UK Buildings and Similar Climatic Conditions

Natural ventilation is gaining more attention from architects and engineers as an alternative way of cooling and ventilating indoor spaces. Based on building types, it could save between 13 and 40% of the building cooling energy use. However, this needs to be implemented and operated with a well-designed and integrated control system to avoid triggering discomfort for occupants. This paper seeks to review, discuss, and contribute to existing knowledge on the application of control systems and optimisation theories of naturally ventilated buildings to produce the best performance. The study finally presents an outstanding theoretical context and practical implementation for researchers seeking to explore the use of intelligent controls for optimal output in the pursuit to help solve intricate control problems in the building industry and suggests advanced control systems such as fuzzy logic control as an effective control strategy for an integrated control of ventilation, heating and cooling systems

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

Performance Analysis of Data-Driven and Model-Based Control Strategies Applied to a Thermal Unit Model

The paper presents the design and the implementation of different advanced control strategies that are applied to a nonlinear model of a thermal unit. A data-driven grey-box identification approach provided the physically–meaningful nonlinear continuous-time model, which represents the benchmark exploited in this work. The control problem of this thermal unit is important, since it constitutes the key element of passive air conditioning systems. The advanced control schemes analysed in this paper are used to regulate the outflow air temperature of the thermal unit by exploiting the inflow air speed, whilst the inflow air temperature is considered as an external disturbance. The reliability and robustness issues of the suggested control methodologies are verified with a Monte Carlo (MC) analysis for simulating modelling uncertainty, disturbance and measurement errors. The achieved results serve to demonstrate the effectiveness and the viable application of the suggested control solutions to air conditioning systems. The benchmark model represents one of the key issues of this study, which is exploited for benchmarking different model-based and data-driven advanced control methodologies through extensive simulations. Moreover, this work highlights the main features of the proposed control schemes, while providing practitioners and heating, ventilating and air conditioning engineers with tools to design robust control strategies for air conditioning systems

Control of HVAC system comfort by sampling

The sampling of the users comfort, allows observing and predicting the level of comfort on the HVAC (heating, ventilation, and air conditioning) systems. The development of online sampling systems assists in the recognition of the behavior patterns that occur in the offices. This paper presents a user-friendly tool designed and developed in order to make easier knowledge extraction and representation to make possible decisions about which demand that must prevail, the user comfort or saving into a central system. This decision may depend on the occupation and feeling of comfort of its occupants. Some studies have put neutral thermal conditions outside the ranges of comfort of the ASHRAE standard. The actual rules of the HVAC systems are based on studies carried out on specific populations in a specific space, which are not valid in certain situations. This is a dynamic idea of the comfort based in real data. The methodology used provides important and useful information to be able to select the comfort set-point of the rooms of a central heating system without the need to use fixed values based on programmed time schedules or any other methodology. The response to comfort in an area of a building throughout the day can be seen in this study. The users were assessed using a standard set of key questions in order to measure the level of satisfaction with environmental factors, thanks to a questionnaire of imprecise answers. We seek an improvement in the building users, regardless of their particularities

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

Past, present and future mathematical models for buildings (i)

This is the first of two articles presenting a detailed review of the historical evolution of mathematical models applied in the development of building technology, including conventional buildings and intelligent buildings. After presenting the technical differences between conventional and intelligent buildings, this article reviews the existing mathematical models, the abstract levels of these models, and their links to the literature for intelligent buildings. The advantages and limitations of the applied mathematical models are identified and the models are classified in terms of their application range and goal. We then describe how the early mathematical models, mainly physical models applied to conventional buildings, have faced new challenges for the design and management of intelligent buildings and led to the use of models which offer more flexibility to better cope with various uncertainties. In contrast with the early modelling techniques, model approaches adopted in neural networks, expert systems, fuzzy logic and genetic models provide a promising method to accommodate these complications as intelligent buildings now need integrated technologies which involve solving complex, multi-objective and integrated decision problems

Advanced Occupancy Measurement Using Sensor Fusion

With roughly about half of the energy used in buildings attributed to Heating, Ventilation, and Air conditioning (HVAC) systems, there is clearly great potential for energy saving through improved building operations. Accurate knowledge of localised and real-time occupancy numbers can have compelling control applications for HVAC systems. However, existing technologies applied for building occupancy measurements are limited, such that a precise and reliable occupant count is difficult to obtain. For example, passive infrared (PIR) sensors commonly used for occupancy sensing in lighting control applications cannot differentiate between occupants grouped together, video sensing is often limited by privacy concerns, atmospheric gas sensors (such as CO2 sensors) may be affected by the presence of electromagnetic (EMI) interference, and may not show clear links between occupancy and sensor values. Past studies have indicated the need for a heterogeneous multi-sensory fusion approach for occupancy detection to address the short-comings of existing occupancy detection systems. The aim of this research is to develop an advanced instrumentation strategy to monitor occupancy levels in non-domestic buildings, whilst facilitating the lowering of energy use and also maintaining an acceptable indoor climate. Accordingly, a novel multi-sensor based approach for occupancy detection in open-plan office spaces is proposed. The approach combined information from various low-cost and non-intrusive indoor environmental sensors, with the aim to merge advantages of various sensors, whilst minimising their weaknesses. The proposed approach offered the potential for explicit information indicating occupancy levels to be captured. The proposed occupancy monitoring strategy has two main components; hardware system implementation and data processing. The hardware system implementation included a custom made sound sensor and refinement of CO2 sensors for EMI mitigation. Two test beds were designed and implemented for supporting the research studies, including proof-of-concept, and experimental studies. Data processing was carried out in several stages with the ultimate goal being to detect occupancy levels. Firstly, interested features were extracted from all sensory data collected, and then a symmetrical uncertainty analysis was applied to determine the predictive strength of individual sensor features. Thirdly, a candidate features subset was determined using a genetic based search. Finally, a back-propagation neural network model was adopted to fuse candidate multi-sensory features for estimation of occupancy levels. Several test cases were implemented to demonstrate and evaluate the effectiveness and feasibility of the proposed occupancy detection approach. Results have shown the potential of the proposed heterogeneous multi-sensor fusion based approach as an advanced strategy for the development of reliable occupancy detection systems in open-plan office buildings, which can be capable of facilitating improved control of building services. In summary, the proposed approach has the potential to: (1) Detect occupancy levels with an accuracy reaching 84.59% during occupied instances (2) capable of maintaining average occupancy detection accuracy of 61.01%, in the event of sensor failure or drop-off (such as CO2 sensors drop-off), (3) capable of utilising just sound and motion sensors for occupancy levels monitoring in a naturally ventilated space, (4) capable of facilitating potential daily energy savings reaching 53%, if implemented for occupancy-driven ventilation control