731 research outputs found

    Machine learning for the sustainable energy transition: a data-driven perspective along the value chain from manufacturing to energy conversion

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    According to the special report Global Warming of 1.5 °C of the IPCC, climate action is not only necessary but more than ever urgent. The world is witnessing rising sea levels, heat waves, events of flooding, droughts, and desertification resulting in the loss of lives and damage to livelihoods, especially in countries of the Global South. To mitigate climate change and commit to the Paris agreement, it is of the uttermost importance to reduce greenhouse gas emissions coming from the most emitting sector, namely the energy sector. To this end, large-scale penetration of renewable energy systems into the energy market is crucial for the energy transition toward a sustainable future by replacing fossil fuels and improving access to energy with socio-economic benefits. With the advent of Industry 4.0, Internet of Things technologies have been increasingly applied to the energy sector introducing the concept of smart grid or, more in general, Internet of Energy. These paradigms are steering the energy sector towards more efficient, reliable, flexible, resilient, safe, and sustainable solutions with huge environmental and social potential benefits. To realize these concepts, new information technologies are required, and among the most promising possibilities are Artificial Intelligence and Machine Learning which in many countries have already revolutionized the energy industry. This thesis presents different Machine Learning algorithms and methods for the implementation of new strategies to make renewable energy systems more efficient and reliable. It presents various learning algorithms, highlighting their advantages and limits, and evaluating their application for different tasks in the energy context. In addition, different techniques are presented for the preprocessing and cleaning of time series, nowadays collected by sensor networks mounted on every renewable energy system. With the possibility to install large numbers of sensors that collect vast amounts of time series, it is vital to detect and remove irrelevant, redundant, or noisy features, and alleviate the curse of dimensionality, thus improving the interpretability of predictive models, speeding up their learning process, and enhancing their generalization properties. Therefore, this thesis discussed the importance of dimensionality reduction in sensor networks mounted on renewable energy systems and, to this end, presents two novel unsupervised algorithms. The first approach maps time series in the network domain through visibility graphs and uses a community detection algorithm to identify clusters of similar time series and select representative parameters. This method can group both homogeneous and heterogeneous physical parameters, even when related to different functional areas of a system. The second approach proposes the Combined Predictive Power Score, a method for feature selection with a multivariate formulation that explores multiple sub-sets of expanding variables and identifies the combination of features with the highest predictive power over specified target variables. This method proposes a selection algorithm for the optimal combination of variables that converges to the smallest set of predictors with the highest predictive power. Once the combination of variables is identified, the most relevant parameters in a sensor network can be selected to perform dimensionality reduction. Data-driven methods open the possibility to support strategic decision-making, resulting in a reduction of Operation & Maintenance costs, machine faults, repair stops, and spare parts inventory size. Therefore, this thesis presents two approaches in the context of predictive maintenance to improve the lifetime and efficiency of the equipment, based on anomaly detection algorithms. The first approach proposes an anomaly detection model based on Principal Component Analysis that is robust to false alarms, can isolate anomalous conditions, and can anticipate equipment failures. The second approach has at its core a neural architecture, namely a Graph Convolutional Autoencoder, which models the sensor network as a dynamical functional graph by simultaneously considering the information content of individual sensor measurements (graph node features) and the nonlinear correlations existing between all pairs of sensors (graph edges). The proposed neural architecture can capture hidden anomalies even when the turbine continues to deliver the power requested by the grid and can anticipate equipment failures. Since the model is unsupervised and completely data-driven, this approach can be applied to any wind turbine equipped with a SCADA system. When it comes to renewable energies, the unschedulable uncertainty due to their intermittent nature represents an obstacle to the reliability and stability of energy grids, especially when dealing with large-scale integration. Nevertheless, these challenges can be alleviated if the natural sources or the power output of renewable energy systems can be forecasted accurately, allowing power system operators to plan optimal power management strategies to balance the dispatch between intermittent power generations and the load demand. To this end, this thesis proposes a multi-modal spatio-temporal neural network for multi-horizon wind power forecasting. In particular, the model combines high-resolution Numerical Weather Prediction forecast maps with turbine-level SCADA data and explores how meteorological variables on different spatial scales together with the turbines' internal operating conditions impact wind power forecasts. The world is undergoing a third energy transition with the main goal to tackle global climate change through decarbonization of the energy supply and consumption patterns. This is not only possible thanks to global cooperation and agreements between parties, power generation systems advancements, and Internet of Things and Artificial Intelligence technologies but also necessary to prevent the severe and irreversible consequences of climate change that are threatening life on the planet as we know it. This thesis is intended as a reference for researchers that want to contribute to the sustainable energy transition and are approaching the field of Artificial Intelligence in the context of renewable energy systems

    SET2022 : 19th International Conference on Sustainable Energy Technologies 16th to 18th August 2022, Turkey : Sustainable Energy Technologies 2022 Conference Proceedings. Volume 4

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    Papers submitted and presented at SET2022 - the 19th International Conference on Sustainable Energy Technologies in Istanbul, Turkey in August 202

    Book of Abstracts:9th International Conference on Smart Energy Systems

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    A strategic turnaround model for distressed properties

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    The importance of commercial real estate is clearly shown by the role it plays, worldwide, in the sustainability of economic activities, with a substantial global impact when measured in monetary terms. This study responds to an important gap in the built environment and turnaround literature relating to the likelihood of a successful distressed commercial property financial recovery. The present research effort addressed the absence of empirical evidence by identifying a number of important factors that influence the likelihood of a successful distressed, commercial property financial recovery. Once the important factors that increase the likelihood of recovery have been determined, the results can be used as a basis for turnaround strategies concerning property investors who invest in distressed opportunities. A theoretical turnaround model concerning properties in distress, would be of interest to ‘opportunistic investing’ yield-hungry investors targeting real estate transactions involving ‘turnaround’ potential. Against this background, the main research problem investigated in the present research effort was as follows: Determine the important factors that would increase the likelihood of a successful distressed commercial property financial recovery. A proposed theoretical model was constructed and empirically tested through a questionnaire distributed physically and electronically to a sample of real estate practitioners from across the globe, and who had all been involved, directly or indirectly, with reviving distressed properties. An explanation was provided to respondents of how the questionnaire was developed and how it would be administered. The demographic information pertaining to the 391 respondents was analysed and summarised. The statistical analysis performed to ensure the validity and reliability of the results, was explained to respondents, together with a detailed description of the covariance structural equation modelling method used to verify the proposed theoretical conceptual model. vi The independent variables of the present research effort comprised; Obsolescence Identification, Capital Improvements Feasibility, Tenant Mix, Triple Net Leases, Concessions, Property Management, Contracts, Business Analysis, Debt Renegotiation, Cost-Cutting, Market Analysis, Strategic Planning and Demography, while the dependent variable was The Perceived Likelihood of a Distressed Commercial Property Financial Recovery. After analysis of the findings, a revised model was then proposed and assessed. Both validity and reliability were assessed and resulted in the following factors that potentially influence the dependent variables; Strategy, Concessions, Tenant Mix, Debt Restructuring, Demography, Analyse Alternatives, Capital Improvements Feasibility, Property Management and Net Leases while, after analysis, the dependent variable was replaced by two dependent variables; The Likelihood of a Distressed Property Turnaround and The Likelihood of a Distressed Property Financial Recovery. The results showed that Strategy (comprising of items from Strategic Planning, Business Analysis, Obsolescence Identification and Property Management) and Concessions (comprising of items from Concessions and Triple Net Leases) had a positive influence on both the dependent variables. Property Management (comprising of items from Business Analysis, Property Management, Capital Improvements Feasibility and Tenant Mix) had a positive influence on Financial Turnaround variable while Capital Improvements Feasibility (comprising of items from Capital Improvements Feasibility, Obsolescence Identification and Property Management) had a negative influence on both. Demography (comprising of items only from Demography) had a negative influence on the Financial Recovery variable. The balance of the relationships were depicted as non-significant. The present research effort presents important actions that can be used to influence the turnaround and recovery of distressed real estate. The literature had indicated reasons to recover distressed properties as having wide-ranging economic consequences for the broader communities and the countries in which they reside. The turnaround of distressed properties will not only present financial rewards for opportunistic investors but will have positive effects on the greater community and economy and, thus, social and economic stability. Vii With the emergence of the COVID-19 pandemic crisis, issues with climate change and sustainability, global demographic shifts, changing user requirements, shifts in technology, the threat of obsolescence, urbanisation, globalisation, geo-political tensions, shifting global order, new trends and different generational expectations, it is becoming more apparent that the threat of distressed, abandoned and derelict properties is here to stay, and which will present future opportunities for turnaround, distressed property owners, as well as future worries for urban authorities and municipalities dealing with urban decay. The study concluded with an examination of the perceived limitations of the study as well as presenting a comprehensive range of suggestions for further research.Thesis (PhD) -- Faculty of Engineering, Built Environment and Information Technology, School of the built Environment, 202

    Occupant-Centric Simulation-Aided Building Design Theory, Application, and Case Studies

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    This book promotes occupants as a focal point for the design process

    Towards 2050 net zero carbon infrastructure:a critical review of key decarbonization challenges in the domestic heating sector in the UK

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    One of the most challenging sectors to meet “Net Zero emissions” target by 2050 in the UK is the domestic heating sector. This paper provides a comprehensive literature review of the main challenges of heating systems transition to low carbon technologies in which three distinct categories of challenges are discussed. The first challenge is of decarbonizing heat at the supply side, considering specifically the difficulties in integrating hydrogen as a low-carbon heating substitute to the dominant natural gas. The next challenge is of decarbonizing heat at the demand side, and research into the difficulties of retrofitting the existing UK housing stock, of digitalizing heating energy systems, as well as ensuring both retrofits and digitalization do not disproportionately affect vulnerable groups in society. The need for demonstrating innovative solutions to these challenges leads to the final focus, which is the challenge of modeling and demonstrating future energy systems heating scenarios. This work concludes with recommendations for the energy research community and policy makers to tackle urgent challenges facing the decarbonization of the UK heating sector.</p

    Low carbon multi-vector energy systems: a case study of the University of Edinburgh's 2040 'Net Zero' target

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    The ultimate goal of this research was to develop a methodology to support decision-making by large (public sector) organisations regarding future energy technology choices to reduce carbon emissions. This culminated in the development of a multi-vector campus energy systems modelling tool that was applied to the University of Edinburgh as a case study. To deliver this a series of objectives were addressed. Machine learning models were applied to model building heat and electrical energy use for extrapolation to campus level. This was applied to explore the scope to reduce campus level emissions through operational changes; this demonstrated that it is difficult to further reduce the carbon emissions without technological changes given the University’s heavy reliance on natural gas-fired combined heat and power and boilers. As part of the analysis of alternative energy sources, the scope for off-campus wind farms was considered; specifically this focussed on estimation of wind farm generation at the planning stage and employed a model transfer strategy to facilitate use of metered data from wind farms. One of the key issues in making decisions about future energy sources on campus is the simultaneous changes in the wider energy system and specifically the decarbonisation of electricity; to facilitate better choices about onsite production and imports from the grid, a fundamental electricity model was developed to translate the National Grid Future Energy Scenarios into plausible patterns of electricity prices. The learning from these activities were incorporated into a model able to develop possible configurations for campus-level multi-vector energy systems given a variety of future pathways and uncertainties. The optimal planning model is formulated as a mixed-integer linear programming model with the objective to minimize the overall cost including carbon emissions. A numerical case study for the planning of three real-world campuses is presented to demonstrate the effectiveness of the proposed method. The conclusion highlights the importance of energy storage and a remote wind farm in these energy systems. Also, it is noted that there is no single solution that works in all cases where there are differences in factors such as device cost and performance, the gap between gas and electricity prices, weather conditions and the use (or otherwise) of cross-campus local energy balancing

    Integrated long-term city energy planning. Methodology for the modelling and prospective assessment ofurban energy systems

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    277 p.El objetivo de esta Tesis es desarrollar un marco integrado para la planificación energética urbana a largo plazo basada en la modelización y evaluación prospectiva de los sistemas energéticos urbanos. Este trabajo busca aportar soluciones a los diversos retos a los que se enfrenta el modelado energético en entornos urbanos, así como proporcionar herramientas a los responsables y planificadores urbanos de cara a la consecución de los objetivos energéticos y climáticos. Más concretamente, esta Tesis pretende aclarar los problemas y lagunas específicas afrontadas durante el análisis de los sistemas energéticos urbanos como pueden ser la falta de enfoques claros para el desarrollo de modelos energéticos urbanos holísticos y la integración de sus resultados en los planes energéticos urbanos, la complejidad e incertidumbre a la hora de modelar el uso futuro de la energía en las ciudades, la escasez de datos energéticos a nivel urbano y la necesidad de armonizar los planes energéticos y climáticos locales y nacionales.Abordando la planificación y modelización energética urbana a través de diferentes perspectivas, esta Tesis propone tres enfoques innovadores para: la coordinación de los niveles de planificación energética nacional y urbano, la modelización energética del sector edificios, y la modelización energética integrada y evaluación de impacto de escenarios energéticos urbanos. Todo ello reduciendo la necesidad de datos y la complejidad en la modelización. Asimismo, y con el objetivo de reducir las diferencias entre valores reales y modelados, se propone la combinación de enfoques top-down (de arriba abajo) y bottom-up (de abajo arriba

    The universe without us: a history of the science and ethics of human extinction

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    This dissertation consists of two parts. Part I is an intellectual history of thinking about human extinction (mostly) within the Western tradition. When did our forebears first imagine humanity ceasing to exist? Have people always believed that human extinction is a real possibility, or were some convinced that this could never happen? How has our thinking about extinction evolved over time? Why do so many notable figures today believe that the probability of extinction this century is higher than ever before in our 300,000-year history on Earth? Exploring these questions takes readers from the ancient Greeks, Persians, and Egyptians, through the 18th-century Enlightenment, past scientific breakthroughs of the 19th century like thermodynamics and evolutionary theory, up to the Atomic Age, the rise of modern environmentalism in the 1970s, and contemporary fears about climate change, global pandemics, and artificial general intelligence (AGI). Part II is a history of Western thinking about the ethical and evaluative implications of human extinction. Would causing or allowing our extinction be morally right or wrong? Would our extinction be good or bad, better or worse compared to continuing to exist? For what reasons? Under which conditions? Do we have a moral obligation to create future people? Would past “progress” be rendered meaningless if humanity were to die out? Does the fact that we might be unique in the universe—the only “rational” and “moral” creatures—give us extra reason to ensure our survival? I place these questions under the umbrella of Existential Ethics, tracing the development of this field from the early 1700s through Mary Shelley’s 1826 novel The Last Man, the gloomy German pessimists of the latter 19th century, and post-World War II reflections on nuclear “omnicide,” up to current-day thinkers associated with “longtermism” and “antinatalism.” In the dissertation, I call the first history “History #1” and the second “History #2.” A main thesis of Part I is that Western thinking about human extinction can be segmented into five distinction periods, each of which corresponds to a unique “existential mood.” An existential mood arises from a particular set of answers to fundamental questions about the possibility, probability, etiology, and so on, of human extinction. I claim that the idea of human extinction first appeared among the ancient Greeks, but was eclipsed for roughly 1,500 years with the rise of Christianity. A central contention of Part II is that philosophers have thus far conflated six distinct types of “human extinction,” each of which has its own unique ethical and evaluative implications. I further contend that it is crucial to distinguish between the process or event of Going Extinct and the state or condition of Being Extinct, which one should see as orthogonal to the six types of extinction that I delineate. My aim with the second part of the book is to not only trace the history of Western thinking about the ethics of annihilation, but lay the theoretical groundwork for future research on the topic. I then outline my own views within “Existential Ethics,” which combine ideas and positions to yield a novel account of the conditions under which our extinction would be bad, and why there is a sense in which Being Extinct might be better than Being Extant, or continuing to exist
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