1,207 research outputs found

    Proceedings of the 10th International congress on architectural technology (ICAT 2024): architectural technology transformation.

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    The profession of architectural technology is influential in the transformation of the built environment regionally, nationally, and internationally. The congress provides a platform for industry, educators, researchers, and the next generation of built environment students and professionals to showcase where their influence is transforming the built environment through novel ideas, businesses, leadership, innovation, digital transformation, research and development, and sustainable forward-thinking technological and construction assembly design

    The Pragmatic Development of a Carbon Management Framework for UK SMEs

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    The UK's commitment to net-zero emissions by 2050 is challenged by critics citing current government strategies as inadequate, marked by a lack of concrete action and aspirational guidelines. Notably, businesses, including small and medium-sized enterprises (SMEs) which constitute about half of all business emissions, are pivotal to this goal. Yet, existing policies and standards often neglect the significant role of SMEs, who face barriers such as limited knowledge and resources in implementing carbon management practices. This thesis explores the development of a novel carbon management framework specifically designed for medium-sized organisations in the UK to address these problems. The research adopts a practical approach through collaboration with an industry partner, facilitating a case study for real-world application. Adopting a mixed-methods research design grounded in pragmatism, the study commenced with a qualitative study in the form of a focus group. This exploratory phase, critical for understanding SME challenges, yielded rich data revealing key management themes in strategy, energy, and data. The framework design was supported by a materiality assessment and input from key stakeholders on three major iterations. The final framework comprises three phases: establishing a baseline carbon footprint, creating a carbon reduction plan, and strategically implementing this plan. The validation process, conducted at Knowsley Safari, successfully tested the initial two phases but faced constraints in fully assessing the third phase due to time limitations. While the research achieved its primary aim of developing a novel carbon management framework for SMEs, it encountered limitations, notably in time and the generalisability of findings due to reliance on a single case study. Future research could test the framework across diverse SME settings to establish its broader applicability and effectiveness in aiding the UK's net-zero emission goals

    A review of recent control techniques of drooped inverter‐based AC microgrids

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    As the penetration of distributed generation (DG) systems in the grid is increasing, the challenge of combining large numbers of DGs in the power systems has to be carefully clarified and managed. The control strategy and management concept of the interconnected systems should be flexible and reliable to handle the various types of DGs. This can be suitably met by microgrids. This paper introduces the microgrid structure and elements and states the main objectives that should be achieved by the microgrid controllers and each DG controller in both operation modes (grid-connected and island mode). It also presents the challenges of having multiple DG units in a microgrid in terms of accurate power control/sharing, voltage and frequency regulation, power management between DGs, different renewable energy sources integration and deployment, seamless mode transfer, and the modeling issues. The centralized and decentralized control techniques as potential solutions have been discussed and compared by highlighting the advantages and disadvantages of each. Furthermore, the recent control techniques for drooped alternating current microgrids and the main proposed solutions and contributions in the literature have been exposed to finally overcome the droop control limitations and obtain a flexible and smart distributed power system

    A Literature Review of Fault Diagnosis Based on Ensemble Learning

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    The accuracy of fault diagnosis is an important indicator to ensure the reliability of key equipment systems. Ensemble learning integrates different weak learning methods to obtain stronger learning and has achieved remarkable results in the field of fault diagnosis. This paper reviews the recent research on ensemble learning from both technical and field application perspectives. The paper summarizes 87 journals in recent web of science and other academic resources, with a total of 209 papers. It summarizes 78 different ensemble learning based fault diagnosis methods, involving 18 public datasets and more than 20 different equipment systems. In detail, the paper summarizes the accuracy rates, fault classification types, fault datasets, used data signals, learners (traditional machine learning or deep learning-based learners), ensemble learning methods (bagging, boosting, stacking and other ensemble models) of these fault diagnosis models. The paper uses accuracy of fault diagnosis as the main evaluation metrics supplemented by generalization and imbalanced data processing ability to evaluate the performance of those ensemble learning methods. The discussion and evaluation of these methods lead to valuable research references in identifying and developing appropriate intelligent fault diagnosis models for various equipment. This paper also discusses and explores the technical challenges, lessons learned from the review and future development directions in the field of ensemble learning based fault diagnosis and intelligent maintenance

    A Novel MPPT based reptile search algorithm for photovoltaic system under various conditions

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    Solar systems connected to the grid are crucial in addressing the global energy crisis and meeting rising energy demand. The efficiency of these systems is totally impacted by varying weather conditions such as changes in irradiance and temperature throughout the day. Additionally, partial shading (PS) adds to the complexity of the nonlinear characteristics of photovoltaic (PV) systems, leading to significant power loss. To address this issue, maximum power point tracking (MPPT) algorithms have become an essential component in PV systems to ensure optimal power extraction. This paper introduces a new MPPT control technique based on a novel reptile search optimization algorithm (RSA). The effectiveness of the proposed RSA method is evaluated under different weather conditions with varying irradiance and partial shading. The results of the RSA algorithm are compared to other existing bio-inspired algorithms and show superior performance with an average efficiency of 99.91%, faster dynamic response of 50 ms, and less than 20 watts of oscillation. The RSA-MPPT based technique provides higher efficiency, faster settling time, and minimal oscillation around the maximum power point (MPP), making it a reliable solution for effective solar power harvesting

    Resilient cooling of buildings: state of the art review

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    Name of the research project : IEA Annex 80 – Resilient Cooling of Buildings Publisher: Institute of Building Research & Innovation ZT GmbH, AustriaThis report summarizes an assessment of current State-of-the Art resilient cooling strategies and technologies. It is a result of a collaborative work conducted by participants members of IEA EBC Annex 80. This report consists of four chapters. In the first chapter are included relevant technologies and strategies that contribute to reducing heat loads to people and indoor environments. These technologies/strategies include Advanced window/glazing and shading technologies, Cool envelope materials, Evaporative Envelope Surfaces, Ventilated Envelope Surfaces and Heat Storage and Release. In the second chapter are assessed cooling strategies and technologies that are responsible for removing sensible heat in indoor environments: Ventilative cooling, Evaporative Cooling, Compression refrigeration, Desiccant cooling system, Ground source cooling, Night sky radiative cooling and High-temperature cooling systems. In the third chapter various typologies of cooling strategies and technologies are assessed inside the framework of enhancing personal comfort apart from space cooling. This group of strategies/technologies comprise of: Vertical-axis ceiling fans and horizontal-axis wall fans (such fixed fans differ from pure PCS in that they may be operated under imposed central control or under group or individual control), Small desktop-scale fans or stand fans, Furnitureintegrated fan jets, Devices combining fans with misting/evaporative cooling, Cooled chairs, with convective/conductive cooled heat absorbing surfaces, Cooled desktop surfaces, Workstation micro-air-conditioning units, some including phase change material storage, Radiantly cooled panels (these are currently less for PCS than for room heat load extraction), Conductive wearables, Fan-ventilated clothing ensembles, Variable clothing insulation: flexible dress codes and variable porosity fabrics. In the fourth chapter technologies and strategies pertinent to removing latent heat from indoor environments are assessed. This group includes Desiccant dehumidification, Refrigeration dehumidification, Ventilation dehumidification, and Thermos-electric dehumidification.Preprin

    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

    Evaluation and optimisation of traction system for hybrid railway vehicles

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    Over the past decade, energy and environmental sustainability in urban rail transport have become increasingly important. Hybrid transportation systems present a multifaceted challenge, encompassing aspects such as hydrogen production, refuelling station infrastructure, propulsion system topology, power source sizing, and control. The evaluation and optimisation of these aspects are critical for the adaptation and commercialisation of hybrid railway vehicles. While there has been significant progress in the development of hybrid railway vehicles, further improvements in propulsion system design are necessary. This thesis explores strategies to achieve this ambitious goal by substituting diesel trains with hybrid trains. However, limited research has assessed the operational performance of replacing diesel trains with hybrid trains on the same tracks. This thesis develops various optimisation techniques for evaluating and refining the hybrid traction system to address this gap. In this research's first phase, the author developed a novel Hybrid Train Simulator designed to analyse driving performance and energy flow among multiple power sources, such as internal combustion engines, electrification, fuel cells, and batteries. The simulator incorporates a novel Automatic Smart Switching Control technique, which scales power among multiple power sources based on the route gradient for hybrid trains. This smart switching approach enhances battery and fuel cell life and reduces maintenance costs by employing it as needed, thereby eliminating the forced charging and discharging of excessively high currents. Simulation results demonstrate a 6% reduction in energy consumption for hybrid trains equipped with smart switching compared to those without it. In the second phase of this research, the author presents a novel technique to solve the optimisation problem of hybrid railway vehicle traction systems by utilising evolutionary and numerical optimisation techniques. The optimisation method employs a nonlinear programming solver, interpreting the problem via a non-convex function combined with an efficient "Mayfly algorithm." The developed hybrid optimisation algorithm minimises traction energy while using limited power to prevent unnecessary load on power sources, ensuring their prolonged life. The algorithm takes into account linear and non-linear variables, such as velocity, acceleration, traction forces, distance, time, power, and energy, to address the hybrid railway vehicle optimisation problem, focusing on the energy-time trade-off. The optimised trajectories exhibit an average reduction of 16.85% in total energy consumption, illustrating the algorithm's effectiveness across diverse routes and conditions, with an average increase in journey times of only 0.40% and a 15.18% reduction in traction power. The algorithm achieves a well-balanced energy-time trade-off, prioritising energy efficiency without significantly impacting journey duration, a critical aspect of sustainable transportation systems. In the third phase of this thesis, the author introduced artificial neural network models to solve the optimisation problem for hybrid railway vehicles. Based on time and power-based architecture, two ANN models are presented, capable of predicting optimal hybrid train trajectories. These models tackle the challenge of analysing large datasets of hybrid railway vehicles. Both models demonstrate the potential for efficiently predicting hybrid train target parameters. The results indicate that both ANN models effectively predict a hybrid train's critical parameters and trajectory, with mean errors ranging from 0.19% to 0.21%. However, the cascade-forward neural network topology in the time-based architecture outperforms the feed-forward neural network topology in terms of mean squared error and maximum error in the power-based architecture. Specifically, the cascade-forward neural network topology within the time-based structure exhibits a slightly lower MSE and maximum error than its power-based counterpart. Moreover, the study reveals the average percentage difference between the benchmark and FFNN/CNFN trajectories, highlighting that the time-based architecture exhibits lower differences (0.18% and 0.85%) compared to the power-based architecture (0.46% and 0.92%)
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