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Decarbonizing Smart Buildings and Energy Systems: Digital Twins, Advanced Models and Optimization Algorithms

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

Deadline for manuscript submissions: 25 December 2025 | Viewed by 3323

Special Issue Editors

Dr. Ferraro Marco

E-Mail Website
Guest Editor
Istituto di Tecnologie Avanzate per l’Energia “Nicola Giordano” (ITAE), Consiglio Nazionale delle Ricerche (CNR), Salita S. Lucia Sopra Contesse, 5, 98126 Messina, Italy
Interests: building design; combined heat and power; ecodesign; life cycle analysis; electrochemistry; batteries and fuel cells; fuel cell technology; hydrogen production; RES integration in buildings; sustainable energy communities
Special Issues, Collections and Topics in MDPI journals
Dr. Giuseppina Ciulla

E-Mail Website
Guest Editor
Department of Engineering, University of Palermo, 90128 Palermo, Italy
Interests: renewable energies sources; energy; energy and building; energy and environment
Dr. Tancredi Testasecca

E-Mail Website
Guest Editor
Department of Engineering, Università degli Studi di Palermo, 90128 Palermo, Italy
Interests: energy engineering; digital twins of buildings and energy systems; district heating and cooling networks; energy optimization of smart energy networks
Dr. Manfredi Maniscalco

E-Mail Website
Guest Editor
Consiglio Nazionale delle Ricerche, Istituto di Tecnologie Avanzate per l’Energia “Nicola Giordano”, Viale delle Scienze Edificio 9, 90128 Palermo, Italy
Interests: waste management; environmental engineering; ecological engineering; architectural engineering

Special Issue Information

Dear Colleagues,

The implementation of digitalization is becoming increasingly widespread, impacting various sectors, including the energy field. In energy-related applications, this digital transformation is enhancing the reliability of systems, reducing operational costs, and increasing efficiency. This shift is particularly evident in the fields of smart buildings and smart energy systems, where research collaborations between academia and industry are expediting the realization of decarbonization.

This "smartness" is enabled by sensors and actuators, and is achieved via the development of innovative technologies such as artificial intelligence, advanced physics-based and hybrid models, and sophisticated optimization algorithms in real-world applications. The aim of these applications varies from enhancing energy efficiency and the maintenance of systems to enabling grid interaction and decarbonization. Digital twins, which are synchronized replicas of real-world entities, are proving that significant and tangible benefits can be realized when scientific research is applied to systems in real time.

This Special Issue aims to highlight recent research that has contributed to the decarbonization of both smart buildings and energy systems. We welcome the submission of both original research and review articles that address the following topics:

  • Innovative data-driven and physics-based models that reduce energy consumption and emissions;
  • BIM-based frameworks and case studies for the integration and optimal management of smart buildings;
  • Optimization of smart energy networks, including smart grids and fourth- and fifth-generation district heating and cooling networks;
  • Novel methodologies for improving building control systems to enhance efficiency and indoor comfort for occupants;
  • Internet of Things applications for enhancing energy efficiency, reducing costs, and lowering emissions;
  • The integration and optimal control of renewable energy sources in smart built environments;
  • Systematic reviews on recent smart energy systems applications;
  • The application of Life Cycle Assessment to systems integrated with smart technologies.

Dr. Ferraro Marco
Dr. Giuseppina Ciulla
Dr. Tancredi Testasecca
Dr. Manfredi Maniscalco
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

  • smart energy systems
  • smart buildings
  • digital twins
  • artificial intelligence
  • machine learning
  • optimization algorithms

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Published Papers (3 papers)

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Research

24 pages, 2699 KB  
Article
Digital Twin Framework for Energy Transition in Gas Networks Based on Open-Source Tools: Methodology and Case Study in Southern Italy
by Filippo Luca Alberto Munafò, Ben Alex Baby, Tancredi Testasecca, Marco Ferraro and Marco Beccali
Energies 2025, 18(20), 5434; https://doi.org/10.3390/en18205434 - 15 Oct 2025
Viewed by 100
Abstract
The ongoing digitalization of energy infrastructure is a crucial enabler for improving efficiency, reliability, and sustainability in gas distribution networks, especially in the context of decarbonization and the integration of alternative energy carriers (e.g., renewable gases including biogas, green hydrogen). This study presents [...] Read more.
The ongoing digitalization of energy infrastructure is a crucial enabler for improving efficiency, reliability, and sustainability in gas distribution networks, especially in the context of decarbonization and the integration of alternative energy carriers (e.g., renewable gases including biogas, green hydrogen). This study presents the development and application of a Digital Twin framework for a real-world gas distribution network developed using open-source tools. The proposed methodology covers the entire digital lifecycle: from data acquisition through smart meters and GIS mapping, to 3D modelling and simulation using tools such as QGIS, FreeCAD, and GasNetSim. Consumption data are collected, processed, and harmonized via Python-based workflows, hourly simulations of network operation, including pressure, flow rate, and gas quality indicators like the Wobbe Index. Results demonstrate the effectiveness of the Digital Twin in accurately replicating real network behavior and supporting scenario analyses for the introduction of greener energy vectors such as hydrogen or biomethane. The case study highlights the flexibility and transparency of the workflow, as well as the critical importance of data quality and availability. The framework provides a robust basis for advanced network management, optimization, and planning, offering practical tools to support the energy transition in the gas sector. Full article
(This article belongs to the Special Issue Decarbonizing Smart Buildings and Energy Systems: Digital Twins, Advanced Models and Optimization Algorithms)
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26 pages, 2268 KB  
Article
Assessing the Technical and Economic Viability of Onshore and Offshore Wind Energy in Pakistan Through a Data-Driven Machine Learning and Deep Learning Approach
by Angela Valeria Miceli, Fabio Cardona, Valerio Lo Brano and Fabrizio Micari
Energies 2025, 18(19), 5080; https://doi.org/10.3390/en18195080 - 24 Sep 2025
Viewed by 602
Abstract
An accurate estimation of wind energy productivity is crucial for the success of energy transition strategies in developing countries such as Pakistan, for which the deployment of renewables is essential. This study investigates the use of machine learning and deep learning techniques to [...] Read more.
An accurate estimation of wind energy productivity is crucial for the success of energy transition strategies in developing countries such as Pakistan, for which the deployment of renewables is essential. This study investigates the use of machine learning and deep learning techniques to improve wind farm producibility assessments, tailored to the Pakistani context. SCADA data from a wind turbine in Türkiye were used to train and validate five predictive models. Among these, Random Forest proved most reliable, attaining a coefficient of determination of 0.97 on the testing dataset. The trained model was then employed to simulate the annual production of a 5 × 5 wind farm at two representative sites in Pakistan—one onshore and one offshore—that had been selected using ERA5 reanalysis data. In comparison with conventional estimates based on the theoretical power curve, the machine learning-based approach resulted in net energy predictions up to 20% lower. This is attributable to real-world effects such as wake and grid losses. The onshore site yielded an LCOE of 0.059 USD/kWh, closely aligning with the IRENA’s 2024 national average of approximately 0.06 USD/kWh, thereby confirming the reliability of the estimates. In contrast, the offshore site exhibited an LCOE of 0.120 USD/kWh, thus underscoring the need for incentives to support offshore development in Pakistan’s renewable energy strategy. Full article
(This article belongs to the Special Issue Decarbonizing Smart Buildings and Energy Systems: Digital Twins, Advanced Models and Optimization Algorithms)
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27 pages, 9780 KB  
Article
Hydrogen and Ammonia Production and Transportation from Offshore Wind Farms: A Techno-Economic Analysis
by Farhan Haider Joyo, Andrea Falasco, Daniele Groppi, Adriana Scarlet Sferra and Davide Astiaso Garcia
Energies 2025, 18(9), 2292; https://doi.org/10.3390/en18092292 - 30 Apr 2025
Cited by 1 | Viewed by 2220
Abstract
Offshore wind energy is increasingly considered a vital resource to contribute to the renewable energy future. This renewable energy can be converted to clean energy alternatives such as hydrogen and ammonia via power-to-x technologies, enabling storage, energy security, and decarbonization of hard-to-abate sectors. [...] Read more.
Offshore wind energy is increasingly considered a vital resource to contribute to the renewable energy future. This renewable energy can be converted to clean energy alternatives such as hydrogen and ammonia via power-to-x technologies, enabling storage, energy security, and decarbonization of hard-to-abate sectors. This study assesses the techno-economic feasibility of integrating offshore wind energy with hydrogen and ammonia production as sustainable energy carriers and their transportation via pipelines or shipping. The methodology incorporates Proton Exchange Membrane (PEM) electrolysis for hydrogen production, seawater desalination, and the Haber–Bosch process for ammonia production. Offshore transport scenarios are compared to evaluate their cost-effectiveness based on distance and electrolyzer capacity. Results show the levelized cost of hydrogen (LCOH2) ranges from EUR 6.7 to 9.8/kg (EUR 0.20–0.29/kWh), and the levelized cost of ammonia (LCOA) ranges from EUR 1.9 to 2.8/kg (EUR 0.37–0.55/kWh). Transportation costs vary significantly with distance and electrolyzer capacity, with levelized cost of transport (LCOT) between EUR 0.2 and 15/kg for pipelines and EUR 0.3 and 10.2/kg for shipping. Also, for distances up to 500 km, pipeline transport is the most cost-effective option for both hydrogen and ammonia. Despite high production costs, economies of scale and technological improvements can make offshore hydrogen and ammonia a promising means for a sustainable energy future. Full article
(This article belongs to the Special Issue Decarbonizing Smart Buildings and Energy Systems: Digital Twins, Advanced Models and Optimization Algorithms)
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