Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.5
3.2
Journals
Energies
Special Issues
Advanced Energy Systems in Energy Resilient, Zero/Positive Energy Buildings, Communities...
energies-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Energies Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Advanced Energy Systems in Energy Resilient, Zero/Positive Energy Buildings, Communities and Districts

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "G: Energy and Buildings".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 4172

Special Issue Editors

Dr. Ala Hasan

E-Mail Website
Guest Editor
VTT Technical Research Centre of Finland, FI-02044 VTT Espoo, Finland
Interests: energy in buildings and communities; renwable energy integration; simulation and optimization of buildings’ performance
Special Issues, Collections and Topics in MDPI journals
Dr. Hassam Ur Rehman

E-Mail Website
Guest Editor
VTT Technical Research Centre of Finland, FI-02044 VTT Espoo, Finland
Interests: zero energy building; positive energy buildings; positive energy district; energy transition; energy system modelling; renewable energy systems integration; energy storages; energy flexibility; energy resiliency in buildings and districts; sustainability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Climate action efforts to reduce greenhouse gas emissions in the building sector are important for cities around the world. In general, this sector is responsible for approximately 40% of the EU's energy consumption and 36% of its emissions. Therefore, energy in buildings, communities and districts is at the forefront of mitigating emissions and ensuring a sustainable, self-sufficient and safe future. These goals can be achieved by enhancing energy efficiency and using advanced onsite renewable energy generation, conversion and storage technologies in buildings, communities and districts, which can offset the energy imported from grids. Various concepts for net-zero/positive energy buildings and districts (NZPEBDs) have emerged in recent years to shape cities into carbon-neutral communities in the near future. In addition to this, climate change and energy crises (disruptive events) can reduce a building’s energy performance and impact occupants' well-being and habitability conditions, especially during a power outage. These buildings and districts can also help reach self-sufficiency, engage users and provide energy resiliency during outages.

For this Special Issue, authors are invited to submit high-quality papers on one or more of the following topics related to advanced energy systems in net-zero/positive energy buildings and districts:

  • Concepts, definitions and KPI development of energy-resilient nearly/net/zero and positive energy buildings/communities/districts;
  • The energy efficiency of buildings in communities and districts;
  • Advanced HVAC systems in buildings;
  • Heating/cooling energy and electricity demand;
  • Advanced short/long-term energy storage for heating/cooling/electricity and controls;
  • Renewable-based energy generation and smart control;
  • The energy resiliency of buildings during grid outages under various weather, economical and political conditions;
  • Energy resilience in buildings/districts;
  • Active and passive habitability and survivability conditions in buildings/districts;
  • Energy flexibility offered by buildings, communities and districts to the grid;
  • Energy self-sufficiency of buildings, communities and districts;
  • Advanced simulation and optimization methods;
  • Experience and results from demos and monitoring sites;
  • Economic-, social- and policy-related aspects;
  • User acceptance and engagement in communities and districts.

Dr. Ala Hasan
Dr. Hassam Ur Rehman
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 special issue 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.

Keywords

  • nearly/net/zero and positive energy buildings/communities/districts
  • advanced energy systems
  • energy resiliency
  • energy flexibility
  • experience from demo sites
  • economic, social and policy aspects
  • user's acceptance and engagement

Published Papers (6 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

29 pages, 8051 KiB  
Article
Simulation-Based Evaluation of the Impact of an Electrochromic Glazing on the Energy Use and Indoor Comfort of an Office Room
by Henriqueta Teixeira, A. Moret Rodrigues, Daniel Aelenei and M. Glória Gomes
Energies 2024, 17(9), 2110; https://doi.org/10.3390/en17092110 (registering DOI) - 28 Apr 2024
Viewed by 156
Abstract
Electrochromic glazing alters its optical properties in the absence/presence of an electrical charge, varying from clear to dark to control daylighting and solar heat gains. This study aims to evaluate the impact of an electrochromic glazing, with indoor glare or temperature control, on [...] Read more.
Electrochromic glazing alters its optical properties in the absence/presence of an electrical charge, varying from clear to dark to control daylighting and solar heat gains. This study aims to evaluate the impact of an electrochromic glazing, with indoor glare or temperature control, on the energy performance and thermal and visual comfort of an office room under three European climates, using a calibrated simulation model. The novelty of the paper lies in its combined performance assessment, using different standards and metrics. The results showed reduced climatization energy requirements with temperature control, but significantly increased artificial lighting energy use. Glare control achieved useful illuminance levels during 74–80% of working hours. Concerning temperature control, working hours within thermal comfort increased (21–43%) under a free-float regime. Moreover, the performance of this glazing was compared to that of a clear glazing with/without a reflective film and a thermochromic glazing for different solar orientations. The electrochromic glazing with glare control showed the highest energy savings (14–36%) for a western orientation, and the lowest negative impact on daylighting for a northern orientation. The best glare reduction was achieved with the reflective film. Considering the free-float regime, the electrochromic glazing, with temperature control, showed the highest increase in working hours within thermal comfort (6–9%) for a western orientation. Full article
(This article belongs to the Special Issue Advanced Energy Systems in Energy Resilient, Zero/Positive Energy Buildings, Communities and Districts)
►▼ Show Figures

Figure 1

25 pages, 3846 KiB  
Article
Multi-Stakeholder Decision Support Based on Multicriteria Assessment: Application to Industrial Waste Heat Recovery for a District Heating Network in Grenoble, France
by Jaume Fitó and Julien Ramousse
Energies 2024, 17(9), 2009; https://doi.org/10.3390/en17092009 - 24 Apr 2024
Viewed by 166
Abstract
The decarbonization and decentralization of district heating networks lead to the shared use of on-site resources by multiple stakeholders. The optimal design of prospective equipment in such contexts should take into account the preferences and objectives of each stakeholder. This article focuses on [...] Read more.
The decarbonization and decentralization of district heating networks lead to the shared use of on-site resources by multiple stakeholders. The optimal design of prospective equipment in such contexts should take into account the preferences and objectives of each stakeholder. This article focuses on the adaptation of a 4E multicriteria model (the criteria being energy, exergy, economic, and exergoeconomic) to include and compare the stakeholders’ performance criteria around the technical design. In addition, two graphical supports are proposed that represent and cross-analyze the different stakeholders’ preferred optima. A preliminary implementation of the methodology is illustrated through a study case in France, which features waste heat recovery for district heating utilization. After presenting the results, a discussion is offered on how to complete the methodology with an iterative negotiation procedure to determine the most suitable design. It was concluded, among other considerations, that the relaxation of the stakeholders’ optimality requirements can greatly enable the project’s feasibility. Such a relaxation could be implemented in the form of a joint consortium. In addition, the results showed that stakeholder relaxations of requirements can lead to new solutions that may outperform the best solutions pre-relaxation. Lastly, perspectives are suggested toward verifying whether relaxed requirements from upstream stakeholders might be more impactful than those of downstream stakeholders. Full article
(This article belongs to the Special Issue Advanced Energy Systems in Energy Resilient, Zero/Positive Energy Buildings, Communities and Districts)
►▼ Show Figures

Figure 1

15 pages, 5389 KiB  
Article
Applicability of Face Masks as Recyclable Raw Materials for Self-Made Insulation Panels
by Eugenia Rossi di Schio, Vincenzo Ballerini, Jan Kašpar, Manuela Neri, Mariagrazia Pilotelli, Edoardo Alessio Piana and Paolo Valdiserri
Energies 2024, 17(7), 1648; https://doi.org/10.3390/en17071648 - 29 Mar 2024
Viewed by 460
Abstract
The circular economy model is based on the 4R framework—reduce, reuse, recycle, and recover. While recycling was the primary focus in the past, the shortage of raw materials and the desire to reduce carbon footprints have led to a change in focus: end-of-life [...] Read more.
The circular economy model is based on the 4R framework—reduce, reuse, recycle, and recover. While recycling was the primary focus in the past, the shortage of raw materials and the desire to reduce carbon footprints have led to a change in focus: end-of-life materials are now considered resources rather than waste. When discharged, end-of-life materials still possess properties that can be exploited. For this reason, a comprehensive characterization of reusable materials is mandatory to reduce waste and increase material availability. The reuse of waste materials, such as surgical masks, is of particular interest in giving people in disadvantaged contexts the opportunity to self-produce and self-install panels within their homes, with the dual result of improving indoor comfort and increasing human capital. This paper focuses on the identification of a possible second application for surgical face masks through experimental characterization. Panels made of masks were tested for water vapor permeability, thermal conductivity, and fire resistance and their use as insulating material in the building sector was discussed. Based on the results, surgical face masks are suitable as thermal insulating materials, do not pose safety concerns, and can reduce energy consumption and improve thermal comfort when installed indoors. Full article
(This article belongs to the Special Issue Advanced Energy Systems in Energy Resilient, Zero/Positive Energy Buildings, Communities and Districts)
►▼ Show Figures

Figure 1

26 pages, 968 KiB  
Article
Optimizing Building Short-Term Load Forecasting: A Comparative Analysis of Machine Learning Models
by Paraskevas Koukaras, Akeem Mustapha, Aristeidis Mystakidis and Christos Tjortjis
Energies 2024, 17(6), 1450; https://doi.org/10.3390/en17061450 - 18 Mar 2024
Viewed by 727
Abstract
The building sector, known for its high energy consumption, needs to reduce its energy use due to rising greenhouse gas emissions. To attain this goal, a projection for domestic energy usage is needed. This work optimizes short-term load forecasting (STLF) in the building [...] Read more.
The building sector, known for its high energy consumption, needs to reduce its energy use due to rising greenhouse gas emissions. To attain this goal, a projection for domestic energy usage is needed. This work optimizes short-term load forecasting (STLF) in the building sector while considering several variables (energy consumption/generation, weather information, etc.) that impact energy use. It performs a comparative analysis of various machine learning (ML) models based on different data resolutions and time steps ahead (15 min, 30 min, and 1 h with 4-step-, 2-step-, and 1-step-ahead, respectively) to identify the most accurate prediction method. Performance assessment showed that models like histogram gradient-boosting regression (HGBR), light gradient-boosting machine regression (LGBMR), extra trees regression (ETR), ridge regression (RR), Bayesian ridge regression (BRR), and categorical boosting regression (CBR) outperformed others, each for a specific resolution. Model performance was reported using R2, root mean square error (RMSE), coefficient of variation of RMSE (CVRMSE), normalized RMSE (NRMSE), mean absolute error (MAE), and execution time. The best overall model performance indicated that the resampled 1 h 1-step-ahead prediction was more accurate than the 15 min 4-step-ahead and the 30 min 2-step-ahead predictions. Findings reveal that data preparation is vital for the accuracy of prediction models and should be model-adjusted. Full article
(This article belongs to the Special Issue Advanced Energy Systems in Energy Resilient, Zero/Positive Energy Buildings, Communities and Districts)
►▼ Show Figures

Figure 1

20 pages, 5900 KiB  
Article
Machine Learning-Based Forecasting of Temperature and Solar Irradiance for Photovoltaic Systems
by Wassila Tercha, Sid Ahmed Tadjer, Fathia Chekired and Laurent Canale
Energies 2024, 17(5), 1124; https://doi.org/10.3390/en17051124 - 27 Feb 2024
Viewed by 871
Abstract
The integration of photovoltaic (PV) systems into the global energy landscape has been boosted in recent years, driven by environmental concerns and research into renewable energy sources. The accurate prediction of temperature and solar irradiance is essential for optimizing the performance and grid [...] Read more.
The integration of photovoltaic (PV) systems into the global energy landscape has been boosted in recent years, driven by environmental concerns and research into renewable energy sources. The accurate prediction of temperature and solar irradiance is essential for optimizing the performance and grid integration of PV systems. Machine learning (ML) has become an effective tool for improving the accuracy of these predictions. This comprehensive review explores the pioneer techniques and methodologies employed in the field of ML-based forecasting of temperature and solar irradiance for PV systems. This article presents a comparative study between various algorithms and techniques commonly used for temperature and solar radiation forecasting. These include regression models such as decision trees, random forest, XGBoost, and support vector machines (SVM). The beginning of this article highlights the importance of accurate weather forecasts for the operation of PV systems and the challenges associated with traditional meteorological models. Next, fundamental concepts of machine learning are explored, highlighting the benefits of improved accuracy in estimating the PV power generation for grid integration. Full article
(This article belongs to the Special Issue Advanced Energy Systems in Energy Resilient, Zero/Positive Energy Buildings, Communities and Districts)
►▼ Show Figures

Figure 1

13 pages, 2998 KiB  
Article
Dynamic Modelling of Data Center Waste Heat Potential Integration in District Heating in Latvia
by Ieva Pakere, Kirils Goncarovs, Armands Grāvelsiņš and Marita Agate Zirne
Energies 2024, 17(2), 445; https://doi.org/10.3390/en17020445 - 16 Jan 2024
Viewed by 652
Abstract
As demand for data centers (DC) has increased rapidly, so has their electricity consumption. Cooling DCs is essential to maintain optimal temperatures for the operation of servers and equipment. The consequence of the cooling process is that most of the electricity consumed in [...] Read more.
As demand for data centers (DC) has increased rapidly, so has their electricity consumption. Cooling DCs is essential to maintain optimal temperatures for the operation of servers and equipment. The consequence of the cooling process is that most of the electricity consumed in DCs, including cooling, is eventually dissipated as heat that is released into the atmosphere without any useful application. Recovering and reusing waste heat offers a sustainable solution to reduce primary energy consumption and minimize the environmental impact. Using waste heat from DCs to heat buildings can significantly improve the energy efficiency and environmental sustainability of DCs. Therefore, this research analyzes the existing potential of waste heat recovery from data centers in Latvia and proposes a system dynamic modelling approach for evaluation of the future impact of waste heat on the national heat supply. The overall waste heat generated by DCs in 2022 was 51.37 GWh at a temperature of 65 °C. By 2050, the total heat energy production potential from DCs will increase to 257 GWh, with 201 GWh being utilized. Full article
(This article belongs to the Special Issue Advanced Energy Systems in Energy Resilient, Zero/Positive Energy Buildings, Communities and Districts)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-6
Back to TopTop