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AI-Driven Multi-Technology Renewable Energy Systems: Climate Resilience and Sustainable Management
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AI-Driven Multi-Technology Renewable Energy Systems: Climate Resilience and Sustainable Management

A Special Issue of Sustainability (ISSN 2071-1050) belonging to the section "Energy Sustainability".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 2115

Editor

Dr. Maria Malvoni

E-Mail Website
Guest Editor
Department for Energy Efficiency, ENEA-Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Rome, Italy
Interests: energy efficient buildings; energy efficiency monitoring and evaluation; energy end-use efficiency and energy services; renewable energy sources; RES integration in buildings; photovoltaic performance analysis; PV generation forecasting methods; artificial intelligence predictive methods for photovoltaic systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The global transition to renewable energy requires sophisticated optimization strategies that address performance variability, climate adaptation, and grid integration challenges. This Special Issue will explore the convergence of artificial intelligence, advanced analytics, and sustainability frameworks to enhance renewable energy system efficiency and resilience.

This Special Issue will also examine AI-driven approaches, including machine learning-based forecasting, performance degradation modeling across technologies, hybrid energy systems integration, and climate-adaptive management strategies. We welcome contributions spanning wind, solar thermal, hydroelectric, biomass, geothermal, and emerging renewable technologies, with particular emphasis on their intelligent coordination and optimization.

The primary objective of this Special Issue is to advance scientific understanding through innovative AI applications, performance analysis, and integrated sustainability assessments that support reliable clean energy deployment across diverse environmental conditions. This Special Issue welcomes practical studies that demonstrate measurable improvements in energy efficiency, grid stability, and environmental impact reduction.

Most current research examines renewable energy technologies separately or uses single-optimization methods in isolation. This Special Issue fills this knowledge gap by focusing on comprehensive approaches that combine multiple technologies, adapt to changing climate conditions, and ensure long-term environmental and economic sustainability. We particularly encourage submissions that present practical implementations, validate theoretical models using real-world data, and demonstrate scalable solutions for different geographic regions and energy system configurations.

Proposed topics for submissions include the following:

  1. AI-enhanced energy system optimization:
  • Machine learning algorithms for multi-source energy management;
  • Predictive analytics for renewable energy forecasting;
  • Neural networks for grid stability and load balancing.
  1. Climate-adaptive energy systems:
  • Performance degradation modeling across renewable technologies;
  • Climate resilience strategies for energy infrastructure;
  • Weather pattern analysis for energy planning.
  1. Smart grid integration and management:
  • Distributed energy resource coordination;
  • Energy storage optimization strategies;
  • Electric vehicle integration and demand response.
  1. Hybrid renewable energy systems:
  • Multi-technology integration frameworks;
  • Biomass–solar–wind hybrid configurations;
  • Microgrids and islanding strategies.
  1. Sustainability assessment and policy:
  • Life cycle assessment methodologies for renewable systems;
  • Carbon footprint optimization across energy portfolios;
  • Economic viability and policy impact studies.
  1. Advanced monitoring and real-world implementation:
  • IoT-enabled performance monitoring systems;
  • Fault detection and diagnostic algorithms;
  • Practical case studies and scalable deployment strategies.

This Special Issue will provide a comprehensive platform for researchers to share innovative solutions that address critical challenges in renewable energy system optimization, climate adaptation, and sustainable energy management across diverse technologies and applications.

We look forward to receiving your contributions.

Dr. Maria Malvoni
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability 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 2400 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

  • artificial intelligence
  • renewable energy systems
  • machine learning
  • smart grid integration
  • climate resilience
  • hybrid energy systems
  • sustainability assessment
  • multi-technology integration

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Published Papers (3 papers)

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Research

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37 pages, 6338 KB  
Article
Valorizing Residue Biomass into Bioenergy: An Explainable Hybrid Machine Learning Model for Predicting Higher Heating Value (HHV) from Elemental Composition
by Yıldırım Özüpak, Emrah Aslan, Mehmet Burukanli and Davut Ari
Sustainability 2026, 18(16), 8412; https://doi.org/10.3390/su18168412 - 17 Aug 2026
Viewed by 264
Abstract
Transforming waste and agricultural-residue biomass into bioenergy is central to the circular bioeconomy, yet routing such heterogeneous residues to the right thermochemical pathway depends on the higher heating value (HHV), which is conventionally measured by slow, resource-intensive bomb calorimetry. Here, we present an [...] Read more.
Transforming waste and agricultural-residue biomass into bioenergy is central to the circular bioeconomy, yet routing such heterogeneous residues to the right thermochemical pathway depends on the higher heating value (HHV), which is conventionally measured by slow, resource-intensive bomb calorimetry. Here, we present an explainable alternative that predicts HHV from inexpensive elemental inputs. We used a publicly archived compilation of 344 literature-reported biomass samples retrieved from an open data repository rather than assembled by the authors, including carbon (C), hydrogen (H), oxygen (O), nitrogen (N) and sulfur (S). Measured HHV was the target. The samples spanned woody, herbaceous and agricultural-residue biomass, and they were standardized through duplicate removal, consistency verification and outlier assessment. On these features, we developed a stacked hybrid model combining Random Forest, eXtreme Gradient Boosting and Artificial Neural Networks, which estimated the HHV with R2 = 0.99, RMSE = 0.45 MJ/kg and MAE = 0.30 MJ/kg. SHAP and LIME analyses showed that carbon exerts the strongest positive influence on HHV, whereas oxygen contributes negatively, which is consistent with established thermochemical principles. Within the compositional range covered by the training data, and subject to the absence of external validation, the framework offers a fast and interpretable complement to bomb calorimetry for screening residue biomass. Full article
(This article belongs to the Special Issue AI-Driven Multi-Technology Renewable Energy Systems: Climate Resilience and Sustainable Management)
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15 pages, 2498 KB  
Article
A Time Series Forecasting Methodology for Climatic Drivers of Urban Drought in Sustainable Smart City Planning
by Ninoslava Tihi, Srđan Popov, Stefan Popović, Sonja Đukić Popović, Niko Samec and Filip Kokalj
Sustainability 2026, 18(8), 3945; https://doi.org/10.3390/su18083945 - 16 Apr 2026
Viewed by 560
Abstract
Urban drought is a climate-related challenge that threatens environmental sustainability, public health, and socio-economic stability in urban areas. With increasing climate variability, sustainable smart city planning requires reliable forecasting methodologies to facilitate adaptive water resource management and long-term climate resilience plans. This study [...] Read more.
Urban drought is a climate-related challenge that threatens environmental sustainability, public health, and socio-economic stability in urban areas. With increasing climate variability, sustainable smart city planning requires reliable forecasting methodologies to facilitate adaptive water resource management and long-term climate resilience plans. This study proposes and evaluates a time series forecasting methodology for the climatic drivers of urban drought, using standard statistical approaches—Seasonal Autoregressive Integrated Moving Average ((S)ARIMA) and Holt–Winters exponential smoothing. The methodology includes systematic preprocessing of meteorological data, univariate time series modeling, and performance evaluation using recognized accuracy metrics (RMSE, MAE, and MAPE). Air temperature, precipitation, soil moisture, and wind speed are analyzed as key climatic variables affecting urban drought dynamics. The results indicate that forecast performance varies based on the statistical characteristics of each variable: (S)ARIMA models provide superior predictive accuracy for series with significant seasonality or stochastic fluctuations, whereas the Holt–Winters method is more appropriate for variables displaying sustained downward trends, particularly soil moisture. The forecasts provide a methodological foundation for calculating drought indices and classifying severity, enhancing early warning capabilities and supporting sustainable smart city planning under increasing climate uncertainty. Full article
(This article belongs to the Special Issue AI-Driven Multi-Technology Renewable Energy Systems: Climate Resilience and Sustainable Management)
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Review

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22 pages, 1192 KB  
Review
The Double Readiness Gap in Machine Learning for Building Energy Management: A Scoping Review of Deployment Maturity, Trustworthy AI, and EU AI Act Alignment
by Maria Malvoni
Sustainability 2026, 18(12), 6107; https://doi.org/10.3390/su18126107 - 14 Jun 2026
Cited by 1 | Viewed by 637
Abstract
Reducing building energy consumption is central to EU climate-neutrality targets and to sustainable development goals: buildings account for around 40% of EU final energy consumption, placing Building Energy Management Systems (BEMS) at the intersection of the European Green Deal and the EU Artificial [...] Read more.
Reducing building energy consumption is central to EU climate-neutrality targets and to sustainable development goals: buildings account for around 40% of EU final energy consumption, placing Building Energy Management Systems (BEMS) at the intersection of the European Green Deal and the EU Artificial Intelligence Act. A scoping review following PRISMA-ScR guidelines charted 61 Machine Learning (ML) for BEMS papers (2020–2026) across three sub-domains (load forecasting and energy monitoring, HVAC control, and demand response), using a nine-point Technology Readiness Level (TRL) rubric and three Trustworthy AI (TAI) dimensions (Privacy & Data Governance, Robustness, and Transparency). The review finds that 90.2% of papers remain at the development stage (TRL 4–6), with no multi-site production deployment documented. TAI coverage is heterogeneous at publication level: transparency is addressed in only 3 of 61 papers (4.9%), and privacy provisions (the best-covered ALTAI dimension) are concentrated in demand-response papers (9 of 17, 52.9%), largely via Federated Learning (6 of 9 privacy-tagged papers). A three-level EU AI Act risk classification identifies 23 borderline-candidacy papers (37.7%), predominantly Reinforcement Learning-based HVAC control systems, whose high-risk proximity cannot be resolved at abstract level; explicit compliance engagement is absent from all 61 mapped sources, including the 22 papers published after the Act entered into force in August 2024. The findings document adouble readiness gap: a TRL ceiling co-located with limited documented engagement with TAI obligations and EU AI Act compliance at publication level. Closing this gap is necessary before AI-driven building energy management can be deployed at scale under EU governance requirements. Full article
(This article belongs to the Special Issue AI-Driven Multi-Technology Renewable Energy Systems: Climate Resilience and Sustainable Management)
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