2,160 research outputs found
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
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State-of-the-art on research and applications of machine learning in the building life cycle
Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science
Enhanching Security in the Future Cyber Physical Systems
Cyber Physical System (CPS) is a system where cyber and physical components work in a complex co-ordination to provide better performance. By exploiting the communication infrastructure among the sensors, actuators, and control systems, attackers may compromise the security of a CPS. In this dissertation, security measures for different types of attacks/ faults in two CPSs, water supply system (WSS) and smart grid system, are presented. In this context, I also present my study on energy management in Smart Grid. The techniques for detecting attacks/faults in both WSS and Smart grid system adopt Kalman Filter (KF) and χ2 detector. The χ2 -detector can detect myriad of system fault- s/attacks such as Denial of Service (DoS) attack, short term and long term random attacks. However, the study shows that the χ2 -detector is unable to detect the intelligent False Data Injection attack (FDI). To overcome this limitation, I present a Euclidean detector for smart grid which can effectively detect such injection attacks. Along with detecting attack/faults I also present the isolation of the attacked/faulty nodes for smart grid. For isolation the Gen- eralized Observer Scheme (GOS) implementing Kalman Filter is used. As GOS is effective in isolating attacks/faults on a single sensor, it is unable to isolate simultaneous attacks/faults on multiple sensors. To address this issue, an Iterative Observer Scheme (IOS) is presented which is able to detect attack on multiple sensors. Since network is an integral part of the future CPSs, I also present a scheme for pre- serving privacy in the future Internet architecture, namely MobilityFirst architecture. The proposed scheme, called Anonymity in MobilityFirst (AMF), utilizes the three-tiered ap- proach to effectively exploit the inherent properties of MF Network such as Globally Unique Flat Identifier (GUID) and Global Name Resolution Service (GNRS) to provide anonymity to the users. While employing new proposed schemes in exchanging of keys between different tiers of routers to alleviate trust issues, the proposed scheme uses multiple routers in each tier to avoid collaboration amongst the routers in the three tiers to expose the end users
Precision stellar radial velocity measurements with FIDEOS at the ESO 1-m telescope of La Silla
We present results from the commissioning and early science programs of
FIDEOS, the new high-resolution echelle spectrograph developed at the Centre of
Astro Engineering of Pontificia Universidad Catolica de Chile, and recently
installed at the ESO 1m telescope of La Silla. The instrument provides spectral
resolution R = 43,000 in the visible spectral range 420-800 nm, reaching a
limiting magnitude of 11 in V band. Precision in the measurement of radial
velocity is guaranteed by light feeding with an octagonal optical fibre,
suitable mechanical isolation, thermal stabilisation, and simultaneous
wavelength calibration. Currently the instrument reaches radial velocity
stability of = 8 m/s over several consecutive nights of observation
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
Advances in Batteries, Battery Modeling, Battery Management System, Battery Thermal Management, SOC, SOH, and Charge/Discharge Characteristics in EV Applications
The second-generation hybrid and Electric Vehicles are currently leading the paradigm shift in the automobile industry, replacing conventional diesel and gasoline-powered vehicles. The Battery Management System is crucial in these electric vehicles and also essential for renewable energy storage systems. This review paper focuses on batteries and addresses concerns, difficulties, and solutions associated with them. It explores key technologies of Battery Management System, including battery modeling, state estimation, and battery charging. A thorough analysis of numerous battery models, including electric, thermal, and electro-thermal models, is provided in the article. Additionally, it surveys battery state estimations for a charge and health. Furthermore, the different battery charging approaches and optimization methods are discussed. The Battery Management System performs a wide range of tasks, including as monitoring voltage and current, estimating charge and discharge, equalizing and protecting the battery, managing temperature conditions, and managing battery data. It also looks at various cell balancing circuit types, current and voltage stressors, control reliability, power loss, efficiency, as well as their advantages and disadvantages. The paper also discusses research gaps in battery management systems.publishedVersio
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