Adaptive-predictive control strategy for HVAC systems in smart buildings – A review

Abstract High share of energy consumption in buildings and subsequent increase in greenhouse gas emissions along with stricter legislations have motivated researchers to look for sustainable solutions in order to reduce energy consumption by using alternative renewable energy resources and improving the efficiency in this sector. Today, the smart building and socially resilient city concepts have been introduced where building automation technologies are implemented to manage and control the energy generation/consumption/storage. Building automation and control systems can be roughly classified into traditional and advanced control strategies. Traditional strategies are not a viable choice for more sophisticated features required in smart buildings. The main focus of this paper is to review advanced control strategies and their impact on buildings and technical systems with respect to energy/cost saving. These strategies should be predictive/responsive/adaptive against weather, user, grid and thermal mass. In this context, special attention is paid to model predictive control and adaptive control strategies. Although model predictive control is the most common type used in buildings, it is not well suited for systems consisting of uncertainties and unpredictable data. Thus, adaptive predictive control strategies are being developed to address these shortcomings. Despite great progress in this field, the quantified results of these strategies reported in literature showed a high level of inconsistency. This is due to the application of different control modes, various boundary conditions, hypotheses, fields of application, and type of energy consumption in different studies. Thus, this review assesses the implementations and configurations of advanced control solutions and highlights research gaps in this field that need further investigations

Internet Predictions

More than a dozen leading experts give their opinions on where the Internet is headed and where it will be in the next decade in terms of technology, policy, and applications. They cover topics ranging from the Internet of Things to climate change to the digital storage of the future. A summary of the articles is available in the Web extras section

Sensor Systems For Combustion Control

This project is the first iteration of a larger multi-year project working towards utilizing ammonia (NH3) as an alternative fuel. The purpose of this iteration is a proof of concept for the creation of a control loop that can measure oxygen levels in a system and control the flow rates of nitrogen and oxygen in order to maintain an ideal ratio metric flow to reduce the overall emissions of the system. It is the hope of this project that the concept can eventually be used to maximally reduce to the point of near complete elimination of NOx emissions from burning ammonia as an alternative fuel. The scope of this project is to act as a starting point for research and testing towards the end goal of a cleaner more sustainable alternative fuel. This project confirms the ability to control flow rates of gases to maintain an ideal ratio of nitrogen to oxygen inside of a heating chamber test system through the measuring of voltage of an O2 sensor and adjusting of the flow rates of each gas based on the voltage value returned by the sensor

Distributed environmental monitoring

With increasingly ubiquitous use of web-based technologies in society today, autonomous sensor networks represent the future in large-scale information acquisition for applications ranging from environmental monitoring to in vivo sensing. This chapter presents a range of on-going projects with an emphasis on environmental sensing; relevant literature pertaining to sensor networks is reviewed, validated sensing applications are described and the contribution of high-resolution temporal data to better decision-making is discussed

Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools

This research topic aims at enterprise-wide modeling and optimization (EWMO) through the development and application of integrated modeling, simulation and optimization methodologies, and computer-aided tools for reliable and sustainable improvement opportunities within the entire manufacturing network (raw materials, production plants, distribution, retailers, and customers) and its components. This integrated approach incorporates information from the local primary control and supervisory modules into the scheduling/planning formulation. That makes it possible to dynamically react to incidents that occur in the network components at the appropriate decision-making level, requiring fewer resources, emitting less waste, and allowing for better responsiveness in changing market requirements and operational variations, reducing cost, waste, energy consumption and environmental impact, and increasing the benefits. More recently, the exploitation of new technology integration, such as through semantic models in formal knowledge models, allows for the capture and utilization of domain knowledge, human knowledge, and expert knowledge toward comprehensive intelligent management. Otherwise, the development of advanced technologies and tools, such as cyber-physical systems, the Internet of Things, the Industrial Internet of Things, Artificial Intelligence, Big Data, Cloud Computing, Blockchain, etc., have captured the attention of manufacturing enterprises toward intelligent manufacturing systems

Lessons Learned and Next Steps in Energy Efficiency Measurement and Attribution: Energy Savings, Net to Gross, Non-Energy Benefits, and Persistence of Energy Efficiency Behavior

This white paper examines four topics addressing evaluation, measurement, and attribution of direct and indirect effects to energy efficiency and behavioral programs: Estimates of program savings (gross); Net savings derivation through free ridership / net to gross analyses; Indirect non-energy benefits / impacts (e.g., comfort, convenience, emissions, jobs); and, Persistence of savings

Digital Twins for Lithium-Ion Battery Health Monitoring with Linked Clustering Model using VGG 16 for Enhanced Security Levels

Digital Twin (DT) has only been widely used since the   early 2000s. The concept of DT refers to the act of creating a  computerized replica of a physical item or physical process. There is   the physical world, the cyber world, a bridge between them, and a portal from the cyber world to the physical world. The goal of DT is   to create an accurate digital replica of a previously existent physical object by combining AI, IoT, deep learning, and data analytics. Using   the virtual copy in real time, DTs attempt to describe the actions of the physical object. Battery based DT's viability as a solution to the   industry's growing problems of degradation evaluation, usage  optimization, manufacturing irregularities, and possible second-life  applications, among others, are of fundamental importance. Through       the integration of real-time checking and DT elaboration, data can be   collected that could be used to determine which sensors/data used in a batteries to analyze their performance. This research proposes a          Linked Clustering Model using VGG 16 for Lithium-ion batteries   health condition monitoring (LCM-VGG-Li-ion-BHM). This work           explored the use of deep learning to extract battery information by           selecting the most important features gathered from the sensors. Data           from a digital twin analyzed using deep learning allowed us to         anticipate both typical and abnormal conditions, as well as those that   required closer attention. The proposed model when contrasted with            the existing models performs better in health condition monitoring

Exploring the epistemic politics of urban niche experiments

Urban experiments have been initiated in several locations to purposively initiate and shape transitions to more sustainable urban socio-technical systems, e.g. for energy, water, mobility. Although knowledges produced within such learning spaces are often presented as logical, technical and rational (Vanolo, 2013 ; Kitchin, 2014), the actors and mechanisms which shape decisions are far from obvious, involving cultures, power relations and multiple logics that are profoundly political (Machin, 2013). This research presents a case study founded in a phronetic perspective (Flyvbjerg, 2001; Avelino and Grin, 2017), unpacking the epistemological politics of an urban experiment taking place within a ‘smart city’ programme. A ‘smart transport’ application for mobile phones, ‘MotionMap’ was developed to transform the mobility system of Milton Keynes, an expanding city located 80 km to the north of London, UK. The case study recognises power relations and reveals how various actors engaged in the development of this application have further rendered the MK mobility socio-technical system an object of urban governance

Smart Manufacturing in Rolling Process Based on Thermal Safety Monitoring by Fiber Optics Sensors Equipping Mill Bearings

The steel rolling process is critical for safety and maintenance because of loading and thermal operating conditions. Machinery condition monitoring (MCM) increases the system’s safety, preventing the risk of fire, failure, and rupture. Equipping the mill bearings with sensors allows monitoring of the system in service and controls the heating of mill components. Fiber optic sensors detect loading condition, vibration, and irregular heating. In several systems, access to machinery is rather limited. Therefore, this paper preliminarily investigates how fiber optics can be effectively embedded within the mill cage to set up a smart manufacturing system. The fiber Bragg gratings (FBG) technology allows embedding sensors inside the pins of backup bearings and performing some prognosis and diagnosis activities. The study starts from the rolling mill layout and defines its accessibility, considering some real industrial cases. Testing of an FBG sensor prototype checks thermal monitoring capability inside a closed cavity, obtained on the surface of either the fixed pin of the backup bearing or the stator surrounding the outer ring. Results encourage the development of the whole prototype of the MCM system to be tested on a real mill cage in full operation