4,508 research outputs found
The Application of Nature-inspired Metaheuristic Methods for Optimising Renewable Energy Problems and the Design of Water Distribution Networks
This work explores the technical challenges that emerge when applying bio-inspired optimisation methods to real-world engineering problems. A number of new heuristic algorithms were proposed and tested to deal with these challenges. The work is divided into three main dimensions: i) One of the most significant industrial optimisation problems is optimising renewable energy systems. Ocean wave energy is a promising technology for helping to meet future growth in global energy demand. However, the current technologies of wave energy converters (WECs) are not fully developed because of technical engineering and design challenges. This work proposes new hybrid heuristics consisting of cooperative coevolutionary frameworks and neuro-surrogate optimisation methods for optimising WECs problem in three domains, including position, control parameters, and geometric parameters. Our problem-specific algorithms perform better than existing approaches in terms of higher quality results and the speed of convergence. ii) The second part applies search methods to the optimization of energy output in wind farms. Wind energy has key advantages in terms of technological maturity, cost, and life-cycle greenhouse gas emissions. However, designing an accurate local wind speed and power prediction is challenging. We propose two models for wind speed and power forecasting for two wind farms located in Sweden and the Baltic Sea by a combination of recurrent neural networks and evolutionary search algorithms. The proposed models are superior to other applied machine learning methods. iii) Finally, we investigate the design of water distribution systems (WDS) as another challenging real-world optimisation problem. WDS optimisation is demanding because it has a high-dimensional discrete search space and complex constraints. A hybrid evolutionary algorithm is suggested for minimising the cost of various water distribution networks and for speeding up the convergence rate of search.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 202
Open Data and Models for Energy and Environment
This Special Issue aims at providing recent advancements on open data and models. Energy and environment are the fields of application.For all the aforementioned reasons, we encourage researchers and professionals to share their original works. Topics of primary interest include, but are not limited to:Open data and models for energy sustainability;Open data science and environment applications;Open science and open governance for Sustainable Development Goals;Key performance indicators of data-aware energy modelling, planning and policy;Energy, water and sustainability database for building, district and regional systems; andBest practices and case studies
Quantum ESPRESSO: a modular and open-source software project for quantum simulations of materials
Quantum ESPRESSO is an integrated suite of computer codes for
electronic-structure calculations and materials modeling, based on
density-functional theory, plane waves, and pseudopotentials (norm-conserving,
ultrasoft, and projector-augmented wave). Quantum ESPRESSO stands for "opEn
Source Package for Research in Electronic Structure, Simulation, and
Optimization". It is freely available to researchers around the world under the
terms of the GNU General Public License. Quantum ESPRESSO builds upon
newly-restructured electronic-structure codes that have been developed and
tested by some of the original authors of novel electronic-structure algorithms
and applied in the last twenty years by some of the leading materials modeling
groups worldwide. Innovation and efficiency are still its main focus, with
special attention paid to massively-parallel architectures, and a great effort
being devoted to user friendliness. Quantum ESPRESSO is evolving towards a
distribution of independent and inter-operable codes in the spirit of an
open-source project, where researchers active in the field of
electronic-structure calculations are encouraged to participate in the project
by contributing their own codes or by implementing their own ideas into
existing codes.Comment: 36 pages, 5 figures, resubmitted to J.Phys.: Condens. Matte
Soft Computing approaches in ocean wave height prediction for marine energy applications
El objetivo de esta tesis consiste en investigar el uso de tĂ©cnicas de Soft Computing (SC) aplicadas a la energĂa producida por las olas o energĂa undimotriz. Ésta es, entre todas las energĂas marinas disponibles, la que exhibe el mayor potencial futuro porque, además de ser eficiente desde el punto de vista tĂ©cnico, no causa problemas ambientales significativos. Su importancia práctica radica en dos hechos: 1) es aproximadamente 1000 veces más densa que la energĂa eĂłlica, y 2) hay muchas regiones oceánicas con abundantes recursos de olas que están cerca de zonas pobladas que demandan energĂa elĂ©ctrica. La contrapartida negativa se encuentra en que las olas son más difĂciles de caracterizar que las mareas debido a su naturaleza estocástica. Las tĂ©cnicas SC exhiben resultados similares e incluso superiores a los de otros mĂ©todos estadĂsticos en las estimaciones a corto plazo (hasta 24 h), y tienen la ventaja adicional de requerir un esfuerzo computacional mucho menor que los mĂ©todos numĂ©rico-fĂsicos. Esta es una de las razones por la que hemos decidido explorar el uso de tĂ©cnicas de SC en la energĂa producida por el oleaje. La otra se encuentra en el hecho de que su intermitencia puede afectar a la forma en la que se integra la electricidad que genera con la red elĂ©ctrica. Estas dos son las razones que nos han impulsado a explorar la viabilidad de nuevos enfoques de SC en dos lĂneas de investigaciĂłn novedosas.
La primera de ellas es un nuevo enfoque que combina un algoritmo genĂ©tico (GA: Genetic Algorithm) con una Extreme Learning Machine (ELM) aplicado a un problema de reconstrucciĂłn de la altura de ola significativa (en un boya donde los datos se han perdido, por ejemplo, por una tormenta) utilizando datos de otras boyas cercanas. Nuestro algoritmo GA-ELM es capaz de seleccionar un conjunto reducido de parámetros del oleaje que maximizan la reconstrucciĂłn de la altura de ola significativa en la boya cuyos datos se han perdido utilizando datos de boyas vecinas. El mĂ©todo y los resultados de esta investigaciĂłn han sido publicados en: Alexandre, E., Cuadra, L., Nieto-Borge, J. C., Candil-GarcĂa, G., Del Pino, M., & Salcedo-Sanz, S. (2015). A hybrid genetic algorithm—extreme learning machine approach for accurate significant wave height reconstruction. Ocean Modelling, 92, 115-123.
La segunda contribuciĂłn combina conceptos de SC, Smart Grids (SG) y redes complejas (CNs: Complex Networks). Está motivada por dos aspectos importantes, mutuamente interrelacionados: 1) la forma en la que los conversores WECs (wave energy converters) se interconectan elĂ©ctricamente para formar un parque, y 2) cĂłmo conectar Ă©ste con la red elĂ©ctrica en la costa. Ambos están relacionados con el carácter aleatorio e intermitente de la energĂa elĂ©ctrica producida por las olas. Para poder integrarla mejor sin afectar a la estabilidad de la red se deberĂa recurrir al concepto Smart Wave Farm (SWF). Al igual que una SG, una SWF utiliza sensores y algoritmos para predecir el olaje y controlar la producciĂłn y/o almacenamiento de la electricidad producida y cĂłmo se inyecta Ă©sta en la red. En nuestro enfoque, una SWF y su conexiĂłn con la red elĂ©ctrica se puede ver como una SG que, a su vez, se puede modelar como una red compleja. Con este planteamiento, que se puede generalizar a cualquier red formada por generadores renovables y nodos que consumen y/o almacenan energĂa, hemos propuesto un algoritmo evolutivo que optimiza la robustez de dicha SG modelada como una red compleja ante fallos aleatorios o condiciones anormales de funcionamiento. El modelo y los resultados han sido publicados en: Cuadra, L., Pino, M. D., Nieto-Borge, J. C., & Salcedo-Sanz, S. (2017). Optimizing the Structure of Distribution Smart Grids with Renewable Generation against Abnormal Conditions: A Complex Networks Approach with Evolutionary Algorithms. Energies, 10(8), 1097
Ono: an open platform for social robotics
In recent times, the focal point of research in robotics has shifted from industrial ro- bots toward robots that interact with humans in an intuitive and safe manner. This evolution has resulted in the subfield of social robotics, which pertains to robots that function in a human environment and that can communicate with humans in an int- uitive way, e.g. with facial expressions. Social robots have the potential to impact many different aspects of our lives, but one particularly promising application is the use of robots in therapy, such as the treatment of children with autism. Unfortunately, many of the existing social robots are neither suited for practical use in therapy nor for large scale studies, mainly because they are expensive, one-of-a-kind robots that are hard to modify to suit a specific need. We created Ono, a social robotics platform, to tackle these issues. Ono is composed entirely from off-the-shelf components and cheap materials, and can be built at a local FabLab at the fraction of the cost of other robots. Ono is also entirely open source and the modular design further encourages modification and reuse of parts of the platform
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