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Feature Paper Collection: Energy and Buildings
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Feature Paper Collection: Energy and Buildings

A topical collection in Energies (ISSN 1996-1073). This collection belongs to the section "G: Energy and Buildings".

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Editors

Prof. Dr. Anastasios Dounis

E-Mail Website
Guest Editor
Department of Biomedical Engineering, Egaleo Park Campus, University of West Attica, 12243 Athens, Greece
Interests: computational-artificial intelligence; intelligent control; evolutionary computation; neural networks; systems optimization; distributed artificial intelligence; multi-agent management systems; hybrid intelligent systems and development intelligent systems in biomedicine and energy management in buildings/hospitals
Special Issues, Collections and Topics in MDPI journals
Dr. Paulo Santos

E-Mail Website
Guest Editor
Department of Civil Engineering, University of Coimbra, 3030-788 Coimbra, Portugal
Interests: sustainability; thermal behaviour and energy efficiency of buildings; lightweight steel framed (LSF) construction; construction safety management; boundary elements method
Special Issues, Collections and Topics in MDPI journals

Topical Collection Information

Dear Colleagues,

We are delighted to invite you to contribute a research or review paper for this Collection on reducing the energy needs of buildings, improving building energy efficiency, and improving how energy is managed in buildings. The main topics are presented below:

  • Solar and other renewable energy sources in buildings;
  • Intelligent buildings;
  • Data science (AI, machine learning) for building energy management;
  • Zero-energy, low-energy, and carbon-neutral buildings;
  • Life-cycle energy efficiency of buildings and embodied energy;
  • Life-cycle assessment (LCA) of buildings;
  • Building physics as applied to energy;
  • Energy consumption in buildings;
  • Building energy demand management;
  • District heating and cooling;
  • Heat recovery systems for buildings;
  • Building energy storage;
  • Lighting and other factors related to energy;
  • Smart energy scheduling in hospitals and public buildings.

Prof. Dr. Anastasios Dounis
Dr. Paulo Santos
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the collection website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (4 papers)

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2024

Jump to: 2023

14 pages, 2516 KiB  
Article
Using Urban Building Energy Models for the Development of Sustainable Island Energy Systems
by Jaime Cevallos-Sierra, Afonso Pinto Gonçalves and Carlos Santos Silva
Energies 2024, 17(13), 3135; https://doi.org/10.3390/en17133135 - 26 Jun 2024
Viewed by 801
Abstract
This study evaluates the use of City Energy Analyst, an urban building energy modelling tool, to design zero-carbon energy communities in low-industry isolated island settings. The research aims to test the effectiveness of the software during the development of sustainable energy systems in [...] Read more.
This study evaluates the use of City Energy Analyst, an urban building energy modelling tool, to design zero-carbon energy communities in low-industry isolated island settings. The research aims to test the effectiveness of the software during the development of sustainable energy systems in isolated microgrids and compares it with the widely used tool EnergyPLAN. The goal of the study focused on making a community self-sustainable, considering the rooftop area available in the populated settlements to install photovoltaic systems and distributed storage capacity. With this purpose in mind, the evaluated tool estimated the energy consumption of each building and the respective total annual consumption of Corvo Island, a location that is naturally isolated and dependent on fossil fuels. The results demonstrated that City Energy Analyst is an innovative tool to estimate energy consumption and potential energy generation of photovoltaic systems in a remote location, providing additional features to a traditional model and motivating further development of the associated plug-in. However, it requires initial time-consuming efforts to build a reliable model. As a complement, EnergyPLAN can be used to enhance the design, with the integration of the local existing and potential generation sources and to confirm the stability of the overall energy system. This tool introduced additional wind capacity and centralized storage into the model, testing the balance of the system. Therefore, the study proposes a framework combining the strengths of both tools to measure island energy systems, as they can complement each other, to build a strong analysis model. Full article
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19 pages, 4587 KiB  
Systematic Review
Exploring Residential Energy Behaviour of the Younger Generation for Sustainable Living: A Systematic Review
by Yehui Peng, Jacopo Gaspari and Lia Marchi
Energies 2024, 17(12), 3043; https://doi.org/10.3390/en17123043 - 20 Jun 2024
Viewed by 395
Abstract
The global energy crisis has spurred increased investments in energy efficiency and clean energy initiatives; however, the results have fallen short of expected effectiveness. Concurrently, population growth and urbanisation drive a persistent surge in energy demands, especially within the residential sector, significant to [...] Read more.
The global energy crisis has spurred increased investments in energy efficiency and clean energy initiatives; however, the results have fallen short of expected effectiveness. Concurrently, population growth and urbanisation drive a persistent surge in energy demands, especially within the residential sector, significant to overall building energy consumption. Current research focuses on residents’ responses to one-shot investments for energy efficiency or clean sources. The renovation wave, involving a massive number of existing buildings, calls for the mobilisation of huge investments that can be hard to afford in the short run. Sustainable behavioural change is complementarily rising as a key asset for maximising the overall estimated energy saving potential. Despite significant efforts to analyse household energy use and promote behavioural transformations, the literature remains gaping about future users, particularly the younger generation, as future leaders of sustainable development who exhibit a more responsible approach towards climate-related issues but also a strong dependency on digital-based solutions, which may influence energy use patterns and living habits, also impacting relations among peers and overall societal sustainability and energy efficiency. This article proposes a systematic literature review to analyse the variables affecting young people’s energy behaviour at home. The aim is to investigate the engines and gaps between strategies or tools for behaviour change and the expected effects, then find potential methods to address that barrier to identify a more promising approach, encouraging the younger generation to translate towards more sustainable energy behaviours. Full article
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24 pages, 5222 KiB  
Article
Toward Prediction of Energy Consumption Peaks and Timestamping in Commercial Supermarkets Using Deep Learning
by Mengchen Zhao, Santiago Gomez-Rosero, Hooman Nouraei, Craig Zych, Miriam A. M. Capretz and Ayan Sadhu
Energies 2024, 17(7), 1672; https://doi.org/10.3390/en17071672 - 1 Apr 2024
Viewed by 899
Abstract
Building energy consumption takes up over 30% of global final energy use and 26% of global energy-related emissions. In addition, building operations represent nearly 55% of global electricity consumption. The management of peak demand plays a crucial role in optimizing building electricity usage, [...] Read more.
Building energy consumption takes up over 30% of global final energy use and 26% of global energy-related emissions. In addition, building operations represent nearly 55% of global electricity consumption. The management of peak demand plays a crucial role in optimizing building electricity usage, consequently leading to a reduction in carbon footprint. Accurately forecasting peak demand in commercial buildings provides benefits to both the suppliers and consumers by enhancing efficiency in electricity production and minimizing energy waste. Precise predictions of energy peaks enable the implementation of proactive peak-shaving strategies, the effective scheduling of battery response, and an enhancement of smart grid management. The current research on peak demand for commercial buildings has shown a gap in addressing timestamps for peak consumption incidents. To bridge the gap, an Energy Peaks and Timestamping Prediction (EPTP) framework is proposed to not only identify the energy peaks, but to also accurately predict the timestamps associated with their occurrences. In this EPTP framework, energy consumption prediction is performed with a long short-term memory network followed by the timestamp prediction using a multilayer perceptron network. The proposed framework was validated through experiments utilizing real-world commercial supermarket data. This evaluation was performed in comparison to the commonly used block maxima approach for indexing. The 2-h hit rate saw an improvement from 21% when employing the block maxima approach to 52.6% with the proposed EPTP framework for the hourly resolution. Similarly, the hit rate increased from 65.3% to 86% for the 15-min resolution. In addition, the average minute deviation decreased from 120 min with the block maxima approach to 62 min with the proposed EPTP framework with high-resolution data. The framework demonstrates satisfactory results when applied to high-resolution data obtained from real-world commercial supermarket energy consumption. Full article
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2023

Jump to: 2024

31 pages, 4482 KiB  
Article
In Situ Thermal Transmittance Assessment of the Building Envelope: Practical Advice and Outlooks for Standard and Innovative Procedures
by Iole Nardi and Elena Lucchi
Energies 2023, 16(8), 3319; https://doi.org/10.3390/en16083319 - 7 Apr 2023
Cited by 12 | Viewed by 2796
Abstract
Different standard methods for the assessment of the thermal performance of the building envelope are used: analogy with coeval building, theoretical method, heat flow meter measurement, simple hot box, infrared thermography, and thermometric method. Review papers on these methods, applied in situ and [...] Read more.
Different standard methods for the assessment of the thermal performance of the building envelope are used: analogy with coeval building, theoretical method, heat flow meter measurement, simple hot box, infrared thermography, and thermometric method. Review papers on these methods, applied in situ and in laboratory, have been published, focusing on theory, equipment, metrological performance, test conditions and data acquisition, data analysis, benefits, and limitations. However, steps forward have been done and not been deepened in previous works: in fact, the representative points method and the weighted area method have been proposed, too, whilst artificial intelligence and data-driven methods have begun to prove the reliability also in the U-value prevision using available datasets. Considering this context, this work aims at updating the literature background considering exclusively in situ methods. The work starts from bibliometric and scientometric analysis not previously conducted: this helped to group the methods and to sketch the innovations and the future perspectives. Indeed, from the bibliometric and scientometric literature analysis what emerged was (i) the richness of the background on this topic, especially in the recent years, (ii) two macro-groups (methods with and without measurements), and (iii) the importance of paper keywords (otherwise, interesting papers are eluded by the output of simple database queries). The method study that followed aims at providing (i) a broader view of the thermal transmittance (U-value) assessment procedures, including the utmost recent applications, proposal, and outlooks in this field, (ii) the understanding on the fundamental theories of the techniques, (iii) practical advice for building-envelope assessment, focusing on the advantages and limitations useful for professionals and researchers involved in the energy audit, conservation, or refurbishment of building stock, (iv) the identification of the interconnection between the techniques that often rely on one another, and (v) final remarks and future perspective of the procedures, which embrace the use of artificial intelligence (AI). From the topic analysis, as a result, it emerged that this is an open field for future research, especially with the implementation of AI, which requires good datasets and trials on the models’ architectures, in terms of input layer, number of hidden layer and neurons, and percentage of data to be employed for model training and testing. Full article
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