Fault Diagnosis Of Sensor And Actuator Faults In Multi-Zone Hvac Systems

Globally, the buildings sector accounts for 30% of the energy consumption and more than 55% of the electricity demand. Specifically, the Heating, Ventilation, and Air Conditioning (HVAC) system is the most extensively operated component and it is responsible alone for 40% of the final building energy usage. HVAC systems are used to provide healthy and comfortable indoor conditions, and their main objective is to maintain the thermal comfort of occupants with minimum energy usage. HVAC systems include a considerable number of sensors, controlled actuators, and other components. They are at risk of malfunctioning or failure resulting in reduced efficiency, potential interference with the execution of supervision schemes, and equipment deterioration. Hence, Fault Diagnosis (FD) of HVAC systems is essential to improve their reliability, efficiency, and performance, and to provide preventive maintenance. In this thesis work, two neural network-based methods are proposed for sensor and actuator faults in a 3-zone HVAC system. For sensor faults, an online semi-supervised sensor data validation and fault diagnosis method using an Auto-Associative Neural Network (AANN) is developed. The method is based on the implementation of Nonlinear Principal Component Analysis (NPCA) using a Back-Propagation Neural Network (BPNN) and it demonstrates notable capability in sensor fault and inaccuracy correction, measurement noise reduction, missing sensor data replacement, and in both single and multiple sensor faults diagnosis. In addition, a novel on-line supervised multi-model approach for actuator fault diagnosis using Convolutional Neural Networks (CNNs) is developed for single actuator faults. It is based a data transformation in which the 1-dimensional data are configured into a 2-dimensional representation without the use of advanced signal processing techniques. The CNN-based actuator fault diagnosis approach demonstrates improved performance capability compared with the commonly used Machine Learning-based algorithms (i.e., Support Vector Machine and standard Neural Networks). The presented schemes are compared with other commonly used HVAC fault diagnosis methods for benchmarking and they are proven to be superior, effective, accurate, and reliable. The proposed approaches can be applied to large-scale buildings with additional zones

AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives

In theory, building automation and management systems (BAMSs) can provide all the components and functionalities required for analyzing and operating buildings. However, in reality, these systems can only ensure the control of heating ventilation and air conditioning system systems. Therefore, many other tasks are left to the operator, e.g. evaluating buildings’ performance, detecting abnormal energy consumption, identifying the changes needed to improve efficiency, ensuring the security and privacy of end-users, etc. To that end, there has been a movement for developing artificial intelligence (AI) big data analytic tools as they offer various new and tailor-made solutions that are incredibly appropriate for practical buildings’ management. Typically, they can help the operator in (i) analyzing the tons of connected equipment data; and; (ii) making intelligent, efficient, and on-time decisions to improve the buildings’ performance. This paper presents a comprehensive systematic survey on using AI-big data analytics in BAMSs. It covers various AI-based tasks, e.g. load forecasting, water management, indoor environmental quality monitoring, occupancy detection, etc. The first part of this paper adopts a well-designed taxonomy to overview existing frameworks. A comprehensive review is conducted about different aspects, including the learning process, building environment, computing platforms, and application scenario. Moving on, a critical discussion is performed to identify current challenges. The second part aims at providing the reader with insights into the real-world application of AI-big data analytics. Thus, three case studies that demonstrate the use of AI-big data analytics in BAMSs are presented, focusing on energy anomaly detection in residential and office buildings and energy and performance optimization in sports facilities. Lastly, future directions and valuable recommendations are identified to improve the performance and reliability of BAMSs in intelligent buildings

Next-generation energy systems for sustainable smart cities: Roles of transfer learning

Smart cities attempt to reach net-zero emissions goals by reducing wasted energy while improving grid stability and meeting service demand. This is possible by adopting next-generation energy systems, which leverage artificial intelligence, the Internet of things (IoT), and communication technologies to collect and analyze big data in real-time and effectively run city services. However, training machine learning algorithms to perform various energy-related tasks in sustainable smart cities is a challenging data science task. These algorithms might not perform as expected, take much time in training, or do not have enough input data to generalize well. To that end, transfer learning (TL) has been proposed as a promising solution to alleviate these issues. To the best of the authors’ knowledge, this paper presents the first review of the applicability of TL for energy systems by adopting a well-defined taxonomy of existing TL frameworks. Next, an in-depth analysis is carried out to identify the pros and cons of current techniques and discuss unsolved issues. Moving on, two case studies illustrating the use of TL for (i) energy prediction with mobility data and (ii) load forecasting in sports facilities are presented. Lastly, the paper ends with a discussion of the future directions

Neural network-based predictive control system for energy optimization in sports facilities: a case study

Given the increased global energy demand and its associated environmental impacts, the management and optimization of sports facilities is becoming imperative as they are characterized by high energy demand and occupancy profiles. In this work, the theory of model predictive control ȋMPCȌ is combined with neural networks for temperature setpoint selection to achieve energy and performance optimization of sports facilities. It is demonstrated using the building information model ȋBIMȌ of a sports hall in the sports complex of Qatar University. MPC systems are powerful as they allow integrated dynamic optimization that accounts for the future system behavior in the decision-making process, while neural networks are advantageous for their ability to represent complex interdependencies with high accuracy. The proposed approach was able to achieve a total energy savings of around ͵͵Ψ. Considerations about the network performance, MPC settings tuning, and optimization sub-optimality or failure are essential during the design and implementation phases of the proposed system

Building energy management systems for sports facilities in the Gulf region: a focus on impacts and considerations

Sports tourism in the Gulf region started to flourish where several international sports events were secured for the next decade. This reflects on the number of sports facilities, their energy consumption, and CO2 emissions mainly due to the indoor and outdoor air conditioning requirements. This paper aims to emphasize the significance of energy management in sports facilities especially for hot climatic regions. It presents a review of the works for optimizing building management systems’ (BMSs) operation, anomaly diagnosis, and mitigation. It indicates their application scarcity for sports facilities compared to other types of buildings, and for the regions with hot and humid weather conditions compared to amiable and cold ones, in addition to the considerations for optimizing BMSs of sports facilities based on their type and regional location. An overview is presented of the impacts related to the security and the reliable operation of the BMSs of sports facilities given the advancements in the deployed technologie

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Multi-zone HVAC control system design using feedback linearization

Most buildings nowadays are equipped with Heating, Ventilation, and Air-Conditioning (HVAC) systems dedicated for processing indoor air in terms of different parameters: temperature, humidity, pressure, and quality. Recent studies show that developed countries consume up to 40% of the energy on commercial buildings and half of that is used for air-conditioning purposes, more precisely in air cooling and heating. For that, it is important to develop efficient HVAC control systems that minimize energy usage and achieve occupants' comfort. Buildings are composed of multiple interconnected zones and the simple HVAC control approach is to control zones temperatures locally based on the thermal loads of individual zones. However, it is realistic to account for the thermal interaction between the zones in the controller design to analyze the extent of its effects on the HVAC control system. This paper discusses the design of two-zone HVAC control system using feedback linearization considering the presence of zones interaction. The zones interaction is modeled using physics and thermodynamics laws. The effect of the interaction on control effort and tracking error is analyzed using simulation results.Scopu

Full-Scale Seawater Reverse Osmosis Desalination Plant Simulator

Reverse Osmosis (RO) is an efficient and clean membrane-based technology for water desalination. This work presents a full-scale Seawater Reverse Osmosis (SWRO) desalination plant simulator using MATLAB/Simulink that has been validated using the operational data from a local plant. It allows simulating the system behavior under different operating conditions with high flexibility and minimal cos

HVAC system attack detection dataset

The importance of the security of building management systems (BMSs) has increased given the advances in the technologies used. Since the Heating, Ventilation, and Air Conditioning (HVAC) system in buildings accounts for about 40% of the total energy consumption, threats targeting the HVAC system can be quite severe and costly. Given the limitations on accessing a real HVAC system for research purposes and the unavailability of public labeled datasets to investigate the cybersecurity of HVAC systems, this paper presents a dataset of a 12-zone HVAC system that was collected from a simulation model using the Transient System Simulation Tool (TRNSYS). It aims to promote and support the research in the field of cybersecurity of HVAC systems in smart buildings [1] by facilitating the validation of attack detection and mitigation strategies, benchmarking the performance of different data-driven algorithms, and studying the impact of attacks on the HVAC system

Analysis of unsupervised consumption anomaly detection in sports facilities using artificial intelligence-based data analytics: A case study

Sports facilities have exceptionally high energy demand due to the extensive operational requirements and high-occupancy seasonal rates. Towards promoting efficient energy usage and minimal losses, consumption anomaly detection in sports facilities is addressed in this work using Artificial intelligence (AI)-based analytics approaches. Traditional AI-based data analytics approaches are applied in a practical context for a local sports complex. The actual unlabeled operation data of the facility are used and a case-specific comparative analysis of the various approaches is presented where AI-based data labeling is used. The characteristics of the different algorithms are contextually discussed. It was found that the size and distribution of the training datasets influence the performance of the different algorithms. This study represents preliminary findings on the topic with a promising potential for further research