Design optimization of renewable energy systems for NZEBs based on deep residual learning

Ferrara, Maria; Santa, Francesco Della; Bilardo, Matteo; Gregorio, Alessandro De; Mastropietro, Antonio; Fugacci, Ulderico; Vaccarino, Francesco; Fabrizio, Enrico

doi:10.1016/j.renene.2021.05.044

Cited by 40 publications

(11 citation statements)

References 47 publications

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“…The breakdown of the main categories is dependent on the chosen research methodology. In our previous study, 12 we employed a quantitative approach utilising GIS data to evaluate the resilience of net zero communities. Within the Physical Environment category, we identified three subcategories: transportation, education and leisure.…”

Section: Existing Workmentioning

confidence: 99%

RECOMM. Measuring resilient communities: An analytical and predictive tool

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Pintacuda

et al. 2023

International Journal of Architectural Computing

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We present initial findings of our project RECOMM: an analytical tool that evaluates the resilience of urban areas. The tool utilises Deep Neural Networks to identify characteristics of resilience and assigns a resilience score to different urban areas based on the proximity to certain features such as green spaces, buildings, natural elements and infrastructure. The tool also identifies which urban morphological factors have the greatest impact on resilience. The method uses Convolutional Neural Networks with the Keras library on Tensorflow for calculations and the results are displayed in an online demo built with Node.js and React.js. This work contributes to the analysis and design of sustainable cities and communities by offering a tool to assess resilience through urban form.

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Section: Existing Workmentioning

confidence: 99%

RECOMM. Measuring resilient communities: An analytical and predictive tool

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Pintacuda

et al. 2023

International Journal of Architectural Computing

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“…In other words, the proactive optimal ventilation system presented a decrease in operating expenditure of more than 4217 USD each year. Ferrara et al [63] presented a machine-learning technology based on residual neural networks to minimize the primary consumption of non-renewable energy resources. The method showed good prediction accuracy, with a prediction error of 3%, and an energy performance improvement of 47% was reached after identifying the optimized design solutions.…”

Section: Energy Saving and Emission Reductionmentioning

confidence: 99%

Recent Advances in Low-Carbon and Sustainable, Efficient Technology: Strategies and Applications

et al. 2022

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The COVID-19 pandemic has had a significant impact on the supply chains of traditional fossil fuels. According to a report by the International Energy Agency (IEA) from 2020, oil-refining activity fell by more than the IEA had anticipated. It was also assumed that the demand in 2021 would likely be 2.6 million bpd below the 2019 levels. However, renewable markets have shown strong resilience during the crisis. It was determined that renewables are on track to meet 80% of the growth in electricity demand over the next 10 years and that sustainable energy will act as the primary source of electricity production instead of coal. On the other hand, the report also emphasized that measures for reducing environmental pollution and CO2 emissions are still insufficient and that significant current investments should be further expanded. The Sustainable Development of Energy, Water and Environment Systems (SDEWES) conference series is dedicated to the advancement and dissemination of knowledge on methods, policies and technologies for improving the sustainability of development by decoupling growth from the use of natural resources. The 15th SDEWES conference was held online from 1–5 September 2020; more than 300 reports with 7 special sections were organized on the virtual conference platform. This paper presents the major achievements of the recommended papers in the Special Issue of Energies. Additionally, related studies connected to the above papers published in the SDEWES series are also introduced, including the four main research fields of energy saving and emission reduction, renewable energy applications, the development of district heating systems, and the economic assessment of sustainable energy.

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“…The decision-making on energy use in buildings is another problem field where the applications of mathematical modelling and optimization methods are widespread. The study by Ferrara et al (2021) proposed an approach based on deep residual learning to search for optimal design solutions that are more energy efficient. It was applied to the system design optimization of an Italian multi-family building equipped with a solar cooling system.…”

Section: Short Overview Of the Papers From Other Journals' Sismentioning

confidence: 99%

Introductory remarks on the special issue of Optimization and Engineering dedicated to SDEWES 2021 conference

et al. 2022

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The background of this Special Issue (SI) of the Optimization and Engineering (OPTE) journal is the 2021 Sustainable Development of Energy, Water and Environment Systems (SDEWES) Conference. This broad field was discussed by the participants of the 16th SDEWES Conference held in Dubrovnik, Croatia. This SI aims to bring together articles that discuss recent advances in optimization methods and algorithms that integrate various life-supporting systems. A decision-making problem often appears as a parameter estimation problem; it can be formulated as an optimization model and solved using different optimization algorithms and techniques. All papers included in this OPTE journal SI consider aspects of numerical analysis, mathematical modelling, and computational methods involved in investigating, planning and implementing sustainable development. In this context, the guest editors have confidence that the selected papers and addressed issues will substantially contribute to the increase of the knowledge body published in the OPTE journal, and the SI will be a powerful platform for researchers to discuss, share, and disseminate new ideas.

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Design optimization of renewable energy systems for NZEBs based on deep residual learning

Cited by 40 publications

References 47 publications

RECOMM. Measuring resilient communities: An analytical and predictive tool

RECOMM. Measuring resilient communities: An analytical and predictive tool

Recent Advances in Low-Carbon and Sustainable, Efficient Technology: Strategies and Applications

Introductory remarks on the special issue of Optimization and Engineering dedicated to SDEWES 2021 conference

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