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

A Review of Classification Problems and Algorithms in Renewable Energy Applications

Classification problems and their corresponding solving approaches constitute one of the fields of machine learning. The application of classification schemes in Renewable Energy (RE) has gained significant attention in the last few years, contributing to the deployment, management and optimization of RE systems. The main objective of this paper is to review the most important classification algorithms applied to RE problems, including both classical and novel algorithms. The paper also provides a comprehensive literature review and discussion on different classification techniques in specific RE problems, including wind speed/power prediction, fault diagnosis in RE systems, power quality disturbance classification and other applications in alternative RE systems. In this way, the paper describes classification techniques and metrics applied to RE problems, thus being useful both for researchers dealing with this kind of problem and for practitioners of the field

PV System Information Enhancement and Better Control of Power Systems.

abstract: Due to the rapid penetration of solar power systems in residential areas, there has been a dramatic increase in bidirectional power flow. Such a phenomenon of bidirectional power flow creates a need to know where Photovoltaic (PV) systems are located, what their quantity is, and how much they generate. However, significant challenges exist for accurate solar panel detection, capacity quantification, and generation estimation by employing existing methods, because of the limited labeled ground truth and relatively poor performance for direct supervised learning. To mitigate these issue, this thesis revolutionizes key learning concepts to (1) largely increase the volume of training data set and expand the labelled data set by creating highly realistic solar panel images, (2) boost detection and quantification learning through physical knowledge and (3) greatly enhance the generation estimation capability by utilizing effective features and neighboring generation patterns. These techniques not only reshape the machine learning methods in the GIS domain but also provides a highly accurate solution to gain a better understanding of distribution networks with high PV penetration. The numerical validation and performance evaluation establishes the high accuracy and scalability of the proposed methodologies on the existing solar power systems in the Southwest region of the United States of America. The distribution and transmission networks both have primitive control methodologies, but now is the high time to work out intelligent control schemes based on reinforcement learning and show that they can not only perform well but also have the ability to adapt to the changing environments. This thesis proposes a sequence task-based learning method to create an agent that can learn to come up with the best action set that can overcome the issues of transient over-voltage.Dissertation/ThesisMasters Thesis Electrical Engineering 201

CPS Data Streams Analytics based on Machine Learning for Cloud and Fog Computing: A Survey

Cloud and Fog computing has emerged as a promising paradigm for the Internet of things (IoT) and cyber-physical systems (CPS). One characteristic of CPS is the reciprocal feedback loops between physical processes and cyber elements (computation, software and networking), which implies that data stream analytics is one of the core components of CPS. The reasons for this are: (i) it extracts the insights and the knowledge from the data streams generated by various sensors and other monitoring components embedded in the physical systems; (ii) it supports informed decision making; (iii) it enables feedback from the physical processes to the cyber counterparts; (iv) it eventually facilitates the integration of cyber and physical systems. There have been many successful applications of data streams analytics, powered by machine learning techniques, to CPS systems. Thus, it is necessary to have a survey on the particularities of the application of machine learning techniques to the CPS domain. In particular, we explore how machine learning methods should be deployed and integrated in cloud and fog architectures for better fulfilment of the requirements, e.g. mission criticality and time criticality, arising in CPS domains. To the best of our knowledge, this paper is the first to systematically study machine learning techniques for CPS data stream analytics from various perspectives, especially from a perspective that leads to the discussion and guidance of how the CPS machine learning methods should be deployed in a cloud and fog architecture

Management And Security Of Multi-Cloud Applications

Single cloud management platform technology has reached maturity and is quite successful in information technology applications. Enterprises and application service providers are increasingly adopting a multi-cloud strategy to reduce the risk of cloud service provider lock-in and cloud blackouts and, at the same time, get the benefits like competitive pricing, the flexibility of resource provisioning and better points of presence. Another class of applications that are getting cloud service providers increasingly interested in is the carriers\u27 virtualized network services. However, virtualized carrier services require high levels of availability and performance and impose stringent requirements on cloud services. They necessitate the use of multi-cloud management and innovative techniques for placement and performance management. We consider two classes of distributed applications – the virtual network services and the next generation of healthcare – that would benefit immensely from deployment over multiple clouds. This thesis deals with the design and development of new processes and algorithms to enable these classes of applications. We have evolved a method for optimization of multi-cloud platforms that will pave the way for obtaining optimized placement for both classes of services. The approach that we have followed for placement itself is predictive cost optimized latency controlled virtual resource placement for both types of applications. To improve the availability of virtual network services, we have made innovative use of the machine and deep learning for developing a framework for fault detection and localization. Finally, to secure patient data flowing through the wide expanse of sensors, cloud hierarchy, virtualized network, and visualization domain, we have evolved hierarchical autoencoder models for data in motion between the IoT domain and the multi-cloud domain and within the multi-cloud hierarchy

NILM techniques for intelligent home energy management and ambient assisted living: a review

The ongoing deployment of smart meters and different commercial devices has made electricity disaggregation feasible in buildings and households, based on a single measure of the current and, sometimes, of the voltage. Energy disaggregation is intended to separate the total power consumption into specific appliance loads, which can be achieved by applying Non-Intrusive Load Monitoring (NILM) techniques with a minimum invasion of privacy. NILM techniques are becoming more and more widespread in recent years, as a consequence of the interest companies and consumers have in efficient energy consumption and management. This work presents a detailed review of NILM methods, focusing particularly on recent proposals and their applications, particularly in the areas of Home Energy Management Systems (HEMS) and Ambient Assisted Living (AAL), where the ability to determine the on/off status of certain devices can provide key information for making further decisions. As well as complementing previous reviews on the NILM field and providing a discussion of the applications of NILM in HEMS and AAL, this paper provides guidelines for future research in these topics.Agência financiadora: Programa Operacional Portugal 2020 and Programa Operacional Regional do Algarve 01/SAICT/2018/39578 Fundação para a Ciência e Tecnologia through IDMEC, under LAETA: SFRH/BSAB/142998/2018 SFRH/BSAB/142997/2018 UID/EMS/50022/2019 Junta de Comunidades de Castilla-La-Mancha, Spain: SBPLY/17/180501/000392 Spanish Ministry of Economy, Industry and Competitiveness (SOC-PLC project): TEC2015-64835-C3-2-R MINECO/FEDERinfo:eu-repo/semantics/publishedVersio

Machine Learning Based Detection of False Data Injection Attacks in Wide Area Monitoring Systems

The Smart Grid (SG) is an upgraded, intelligent, and a more reliable version of the traditional Power Grid due to the integration of information and communication technologies. The operation of the SG requires a dense communication network to link all its components. But such a network renders it prone to cyber attacks jeopardizing the integrity and security of the communicated data between the physical electric grid and the control centers. One of the most prominent components of the SG are Wide Area Monitoring Systems (WAMS). WAMS are a modern platform for grid-wide information, communication, and coordination that play a major role in maintaining the stability of the grid against major disturbances. In this thesis, an anomaly detection framework is proposed to identify False Data Injection (FDI) attacks in WAMS using different Machine Learning (ML) and Deep Learning (DL) techniques, i.e., Deep Autoencoders (DAE), Long-Short Term Memory (LSTM), and One-Class Support Vector Machine (OC-SVM). These algorithms leverage diverse, complex, and high-volume power measurements coming from communications between different components of the grid to detect intelligent FDI attacks. The injected false data is assumed to target several major WAMS monitoring applications, such as Voltage Stability Monitoring (VSM), and Phase Angle Monitoring (PAM). The attack vector is considered to be smartly crafted based on the power system data, so that it can pass the conventional bad data detection schemes and remain stealthy. Due to the lack of realistic attack data, machine learning-based anomaly detection techniques are used to detect FDI attacks. To demonstrate the impact of attacks on the realistic WAMS traffic and to show the effectiveness of the proposed detection framework, a Hardware-In-the-Loop (HIL) co-simulation testbed is developed. The performance of the implemented techniques is compared on the testbed data using different metrics: Accuracy, F1 score, and False Positive Rate (FPR) and False Negative Rate (FNR). The IEEE 9-bus and IEEE 39-bus systems are used as benchmarks to investigate the framework scalability. The experimental results prove the effectiveness of the proposed models in detecting FDI attacks in WAMS