23 research outputs found

    Modeling and Communicating Flexibility in Smart Grids Using Artificial Neural Networks as Surrogate Models

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    Increasing shares of renewable energies and the transition towards electric vehicles pose major challenges to the energy system. In order to tackle these in an economically sensible way, the flexibility of distributed energy resources (DERs), such as battery energy storage systems, combined heat and power plants, and heat pumps, needs to be exploited. Modeling and communicating this flexibility is a fundamental step when trying to achieve control over DERs. The literature proposes and makes use of many different approaches, not only for the exploitation itself, but also in terms of models. In the first step, this thesis presents an extensive literature review and a general framework for classifying exploitation approaches and the communicated models. Often, the employed models only apply to specific types of DERs, or the models are so abstract that they neglect constraints and only roughly outline the true flexibility. Surrogate models, which are learned from data, can pose as generic DER models and may potentially be trained in a fully automated process. In this thesis, the idea of encoding the flexibility of DERs into ANNs is systematically investigated. Based on the presented framework, a set of ANN-based surrogate modeling approaches is derived and outlined, of which some are only applicable for specific use cases. In order to establish a baseline for the approximation quality, one of the most versatile identified approaches is evaluated in order to assess how well a set of reference models is approximated. If this versatile model is able to capture the flexibility well, a more specific model can be expected to do so even better. The results show that simple DERs are very closely approximated, and for more complex DERs and combinations of multiple DERs, a high approximation quality can be achieved by introducing buffers. Additionally, the investigated approach has been tested in scheduling tasks for multiple different DERs, showing that it is indeed possible to use ANN-based surrogates for the flexibility of DERs to derive load schedules. Finally, the computational complexity of utilizing the different approaches for controlling DERs is compared

    Data-driven remote fault detection and diagnosis of HVAC terminal units using machine learning techniques

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    The modernising and retrofitting of older buildings has created a drive to install building management systems (BMS) aimed to assist building managers pave the way towards smarter energy use, improve maintenance and increase occupants comfort inside a building. BMS is a computerised control system that controls and monitors a building’s equipment, services such as lighting, ventilation, power systems, fire and security systems, etc. Buildings are becoming more and more complex environments and energy consumption has globally increased to 40% in the past decades. Still, there is no generalised solution or standardisation method available to maintain and handle a building’s energy consumption. Thus this research aims to discover an intelligent solution for the building’s electrical and mechanical units that consume the most power. Indeed, remote control and monitoring of Heating, Ventilation and Air-Conditioning (HVAC) units based on the received information through the thousands of sensors and actuators, is a crucial task in BMS. Thus, it is a foremost task to identify faulty units automatically to optimise running and energy usage. Therefore, a comprehensive analysis on HVAC data and the development of computational intelligent methods for automatic fault detection and diagnosis is been presented here for a period of July 2015 to October 2015 on a real commercial building in London. This study mainly investigated one of the HVAC sub-units namely Fan-coil unit’s terminal unit (TU). It comprises of the three stages: data collection, pre-processing, and machine learning. Further to the aspects of machine learning algorithms for TU behaviour identification by employing unsupervised, supervised, and semi-supervised learning algorithms and their combination was employed to make an automatic intelligent solution for building services. The accuracy of these employed algorithms have been measured in both training and testing phases, results compared with different suitable algorithms, and validated through statistical measures. This research provides an intelligent solution for the real time prediction through the development of an effective automatic fault detection and diagnosis system creating a smarter way to handle the BMS data for energy optimisation

    A Predictive maintenance model for heterogeneous industrial refrigeration systems

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    The automatic assessment of the degradation state of industrial refrigeration systems is becoming increasingly important and constitutes a key-role within predictive maintenance approaches. Lately, data-driven methods especially became the focus of research in this respect. As they only rely on historical data in the development phase, they offer great advantages in terms of flexibility and generalisability by circumventing the need for specific domain knowledge. While most scientific contributions employ methods emerging from the field of machine learning (ML), only very few consider their applicability amongst different heterogeneous systems. In fact, the majority of existing contributions in this field solely apply supervised ML models, which assume the availability of labelled fault data for each system respectively. However, this places restrictions on the overall applicability, as data labelling is mostly conducted by humans and therefore constitutes a non-negligible cost and time factor. Moreover, such methods assume that all considered fault types occurred in the past, a condition that may not always be guaranteed to be satisfied. Therefore, this dissertation proposes a predictive maintenance model for industrial refrigeration systems by especially addressing its transferability onto different but related heterogeneous systems. In particular, it aims at solving a sub-problem known as condition-based maintenance (CBM) to automatically assess the system’s state of degradation. To this end, the model does not only estimate how far a possible malfunction has progressed, but also determines the fault type being present. As will be described in greater detail throughout this dissertation, the proposed model also utilises techniques from the field of ML but rather bypasses the strict assumptions accompanying supervised ML. Accordingly, it assumes the data of the target system to be primarily unlabelled while a few labelled samples are expected to be retrievable from the fault-free operational state, which can be obtained at low cost. Yet, to enable the model’s intended functionality, it additionally employs data from only one fully labelled source dataset and, thus, allows the benefits of data-driven approaches towards predictive maintenance to be further exploited. After the introduction, the dissertation at hand introduces the related concepts as well as the terms and definitions and delimits this work from other fields of research. Furthermore, the scope of application is further introduced and the latest scientific work is presented. This is then followed by the explanation of the open research gap, from which the research questions are derived. The third chapter deals with the main principles of the model, including the mathematical notations and the individual concepts. It furthermore delivers an overview about the variety of problems arising in this context and presents the associated solutions from a theoretical point of view. Subsequently, the data acquisition phase is described, addressing both the data collection procedure and the outcome of the test cases. In addition, the considered fault characteristics are presented and compared with the ones obtained from the related publicly available dataset. In essence, both datasets form the basis for the model validation, as discussed in the following chapter. This chapter then further comprises the results obtained from the model, which are compared with the ones retrieved from several baseline models derived from the literature. This work then closes with a summary and the conclusions drawn from the model results. Lastly, an outlook of the presented dissertation is provide

    Convergence of Intelligent Data Acquisition and Advanced Computing Systems

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    This book is a collection of published articles from the Sensors Special Issue on "Convergence of Intelligent Data Acquisition and Advanced Computing Systems". It includes extended versions of the conference contributions from the 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS’2019), Metz, France, as well as external contributions

    Characterisation of Dynamic Process Systems by Use of Recurrence Texture Analysis

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    This thesis proposes a method to analyse the dynamic behaviour of process systems using sets of textural features extracted from distance matrices obtained from time series data. Algorithms based on the use of grey level co-occurrence matrices, wavelet transforms, local binary patterns, textons, and the pretrained convolutional neural networks (AlexNet and VGG16) were used to extract features. The method was demonstrated to effectively capture the dynamics of mineral process systems and could outperform competing approaches

    Machine learning for human-centered and value-sensitive building energy efficiency

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    Enhancing building energy efficiency is one of the best strategies to reduce energy consumption and associated CO2 emissions. Recent studies emphasized the importance of occupant behavior as a key means of enhancing building energy efficiency. However, it is also critical that while we strive to enhance the energy efficiency of buildings through improving occupant behavior, we still pay enough attention to occupant comfort and satisfaction. Towards this goal, this research proposes a data-driven machine-learning-based approach to behavioral building energy efficiency, which could help better understand and predict the impact of occupant behavior on building energy consumption and occupant comfort; and help optimize occupant behavior for both energy saving and occupant comfort. Three types of models were developed and tested – simulation-data-driven, real-data-driven, and hybrid. Accordingly, the research included five primary research tasks. First, the importance levels of energy-related human values (e.g., thermal comfort) to building occupants and their current satisfaction levels with these values were identified, in order to better understand the factors that are associated with higher/lower importance and/or satisfaction levels and identify the potential factors that could help predict occupant comfort. Second, a data sensing and occupant feedback collection plan was developed, in order to capture and monitor the indoor environmental conditions, energy consumption, energy-related occupant behavior, and occupant comfort in real buildings. Third, a set of buildings were simulated, in order to model the energy consumption of different buildings in different contexts – in terms of occupant behavior, building sizes, weather conditions, etc.; and a simulation-data-driven occupant-behavior-sensitive machine learning-based model, which learns from simulation data, was developed for predicting hourly cooling energy consumption. Fourth, a set of real-data-driven occupant-behavior-sensitive machine learning-based models, which learn from real data (data collected from real buildings and real occupants), were developed for predicting hourly cooling and lighting energy consumption and thermal and visual occupant comfort; and a genetic algorithm-based optimization model for determining the optimal occupant behavior that can simultaneously reduce energy consumption and improve occupant comfort was developed. Compared to the simulation-data-driven approach, the real-data-driven approach aims to better capture and model the real-life behavior and comfort of occupants and the real-life energy-consumption patterns of buildings. Although successful in this regard, the resulting models may not generalize well outside of their training range. Fifth, a hybrid, occupant-behavior-sensitive machine learning-based model, which learns from both simulation data and real data, was developed for predicting hourly cooling and lighting energy consumption. The hybrid approach aims to overcome the limitations of both simulation-data-driven and real-data-driven approaches – especially the limited ability to capture occupant behavior and real-life consumption patterns in simulation-data-driven approaches and the limited generalizability of real-data-driven approaches to different cases – by learning from both types of data simultaneously. The experimental results show the potential of the proposed approach. The energy consumption prediction models achieved high prediction performance, and the thermal and visual comfort models were able to accurately represent the individual and group comfort levels. The optimization results showed potential behavioral energy savings in the range of 11% and 22%, with significant improvement in occupant comfort

    Efficient Learning Machines

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    Computer scienc

    Inferring Complex Activities for Context-aware Systems within Smart Environments

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    The rising ageing population worldwide and the prevalence of age-related conditions such as physical fragility, mental impairments and chronic diseases have significantly impacted the quality of life and caused a shortage of health and care services. Over-stretched healthcare providers are leading to a paradigm shift in public healthcare provisioning. Thus, Ambient Assisted Living (AAL) using Smart Homes (SH) technologies has been rigorously investigated to help address the aforementioned problems. Human Activity Recognition (HAR) is a critical component in AAL systems which enables applications such as just-in-time assistance, behaviour analysis, anomalies detection and emergency notifications. This thesis is aimed at investigating challenges faced in accurately recognising Activities of Daily Living (ADLs) performed by single or multiple inhabitants within smart environments. Specifically, this thesis explores five complementary research challenges in HAR. The first study contributes to knowledge by developing a semantic-enabled data segmentation approach with user-preferences. The second study takes the segmented set of sensor data to investigate and recognise human ADLs at multi-granular action level; coarse- and fine-grained action level. At the coarse-grained actions level, semantic relationships between the sensor, object and ADLs are deduced, whereas, at fine-grained action level, object usage at the satisfactory threshold with the evidence fused from multimodal sensor data is leveraged to verify the intended actions. Moreover, due to imprecise/vague interpretations of multimodal sensors and data fusion challenges, fuzzy set theory and fuzzy web ontology language (fuzzy-OWL) are leveraged. The third study focuses on incorporating uncertainties caused in HAR due to factors such as technological failure, object malfunction, and human errors. Hence, existing studies uncertainty theories and approaches are analysed and based on the findings, probabilistic ontology (PR-OWL) based HAR approach is proposed. The fourth study extends the first three studies to distinguish activities conducted by more than one inhabitant in a shared smart environment with the use of discriminative sensor-based techniques and time-series pattern analysis. The final study investigates in a suitable system architecture with a real-time smart environment tailored to AAL system and proposes microservices architecture with sensor-based off-the-shelf and bespoke sensing methods. The initial semantic-enabled data segmentation study was evaluated with 100% and 97.8% accuracy to segment sensor events under single and mixed activities scenarios. However, the average classification time taken to segment each sensor events have suffered from 3971ms and 62183ms for single and mixed activities scenarios, respectively. The second study to detect fine-grained-level user actions was evaluated with 30 and 153 fuzzy rules to detect two fine-grained movements with a pre-collected dataset from the real-time smart environment. The result of the second study indicate good average accuracy of 83.33% and 100% but with the high average duration of 24648ms and 105318ms, and posing further challenges for the scalability of fusion rule creations. The third study was evaluated by incorporating PR-OWL ontology with ADL ontologies and Semantic-Sensor-Network (SSN) ontology to define four types of uncertainties presented in the kitchen-based activity. The fourth study illustrated a case study to extended single-user AR to multi-user AR by combining RFID tags and fingerprint sensors discriminative sensors to identify and associate user actions with the aid of time-series analysis. The last study responds to the computations and performance requirements for the four studies by analysing and proposing microservices-based system architecture for AAL system. A future research investigation towards adopting fog/edge computing paradigms from cloud computing is discussed for higher availability, reduced network traffic/energy, cost, and creating a decentralised system. As a result of the five studies, this thesis develops a knowledge-driven framework to estimate and recognise multi-user activities at fine-grained level user actions. This framework integrates three complementary ontologies to conceptualise factual, fuzzy and uncertainties in the environment/ADLs, time-series analysis and discriminative sensing environment. Moreover, a distributed software architecture, multimodal sensor-based hardware prototypes, and other supportive utility tools such as simulator and synthetic ADL data generator for the experimentation were developed to support the evaluation of the proposed approaches. The distributed system is platform-independent and currently supported by an Android mobile application and web-browser based client interfaces for retrieving information such as live sensor events and HAR results

    15th SC@RUG 2018 proceedings 2017-2018

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