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Advanced Energy Systems in Energy Resilient and Flexible 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: 30 June 2025 | Viewed by 530

Special Issue Editor


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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 resilience in buildings and districts; sustainability
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Special Issue Information

Dear Colleagues,

The Building sector is important for cities around the world in its Climate Action efforts to reduce greenhouse gas emissions. In general, they are responsible for approximately 40% of the EU’s energy consumption and 36% of the emissions. Therefore, energy in the buildings, communities and districts is one of the main fields for the mitigation of emissions and ensuring a sustainable, self-sufficient and safe future. This can be done by enhancing the energy efficiency and using advanced energy systems components of onsite renewable energy generation, conversion, and storage technologies in buildings, communities and districts, which can offset the imported energy from the grids. Various raising concepts for net-zero/positive energy buildings and districts (NZPEBDs) have emerged in recent years to shape cities in to carbon-neutral communities in the near future. In addition to this, the climate change and energy crises (disruptive events) can cause reduction in the building’s energy performance and impact the occupant well-being and habitability conditions especially during the power outage. These buildings and districts can also support in reaching self-sufficiency, flexibility, engage users and provide energy resilience during outages.

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

  • Concepts, definitions and KPIs development of energy resilience nearly/net/zero and positive energy buildings/communities/districts;
  • 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 generations and smart controls;
  • Energy resiliency of the buildings during grid outages under various weather, economical, political conditions;
  • Energy resilience in buildings/districts;
  • Active and passive habitability and survivability conditions in the buildings/districts
  • Energy flexibility offered by buildings, communities and districts to the grid;
  • Energy self-sufficiency of the buildings, communities and districts;
  • Advanced simulation and optimization methods;
  • Experience and results from demos and monitoring sites;
  • Economic-, social- and policy-related aspects;
  • User’s acceptance and engagement in communities and districts.

Dr. Hassam Ur Rehman
Guest Editor

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

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Published Papers (1 paper)

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Research

24 pages, 3994 KiB  
Article
Smart Charging Recommendation Framework for Electric Vehicles: A Machine-Learning-Based Approach for Residential Buildings
by Nikolaos Tsalikidis, Paraskevas Koukaras, Dimosthenis Ioannidis and Dimitrios Tzovaras
Energies 2025, 18(6), 1528; https://doi.org/10.3390/en18061528 - 19 Mar 2025
Viewed by 311
Abstract
The transition to a decarbonized energy sector, driven by the integration of Renewable Energy Sources (RESs), smart building technology, and the rise of Electric Vehicles (EVs), has highlighted the need for optimized energy system planning. Increasing EV adoption creates additional challenges for charging [...] Read more.
The transition to a decarbonized energy sector, driven by the integration of Renewable Energy Sources (RESs), smart building technology, and the rise of Electric Vehicles (EVs), has highlighted the need for optimized energy system planning. Increasing EV adoption creates additional challenges for charging infrastructure and grid demand, while proactive and informed decisions by residential EV users can help mitigate such challenges. Our work develops a smart residential charging framework that assists residents in making informed decisions about optimal EV charging. The framework integrates a machine-learning-based forecasting engine that consists of two components: a stacking and voting meta-ensemble regressor for predicting EV charging load and a bidirectional LSTM for forecasting national net energy exchange using real-world data from local road traffic, residential charging sessions, and grid net energy exchange flow. The combined forecasting outputs are passed through a data-driven weighting mechanism to generate probabilistic recommendations that identify optimal charging periods, aiming to alleviate grid stress and ensure efficient operation of local charging infrastructure. The framework’s modular design ensures adaptability to local charging infrastructure within or nearby building complexes, making it a versatile tool for enhancing energy efficiency in residential settings. Full article
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