Applications of Artificial Intelligence in Sustainable Energy and Environment

Topic Information

Dear Colleagues,

Artificial intelligence (AI) is accelerating the transition toward cleaner, safer, and more resilient energy and environmental systems. From forecasting renewable generation and orchestrating distributed resources to monitoring air and water quality at scale, AI enables data‑driven decisions that improve efficiency, reliability, and sustainability. At the same time, advances in edge computing, digital twins, physics‑informed learning, and privacy‑preserving analytics are making AI deployable in real‑world infrastructure.

This Topic invites original research articles, reviews, short communications, case studies, and data/resource papers that demonstrate AI‑enabled methods, tools, and applications that advance sustainable energy and environmental stewardship. Submissions that report field validation, open datasets/code, reproducible pipelines, uncertainty quantification, and explainable decision support are especially encouraged.

Topics of interest include, but are not limited to:

• AI for renewable energy forecasting (solar irradiance, wind speed, hydro resources);
• Predictive maintenance, fault detection, and diagnostics for PV plants, wind turbines, inverters, and other assets;
• Optimization and control of smart grids, microgrids, and virtual power plants (DER coordination, demand response, EV/V2G scheduling);
• Energy storage analytics and control (SoC/SoH estimation, degradation modeling, thermal management, hybrid storage);
• Building and campus energy management, HVAC optimization, occupancy‑aware control, and digital twins;
• Power electronics and converter control enhanced by machine learning and reinforcement learning;
• Edge AI, IoT sensing, and federated learning for privacy‑preserving energy analytics;
• Physics‑informed and hybrid AI models that integrate domain knowledge with data‑driven approaches;
• Uncertainty quantification, interpretability, safety, and robustness of AI in critical energy infrastructure;
• AI-based risk early warning and emergency control for power systems with high penetration of renewable energy;
• Energy market analytics and carbon/energy trading (price forecasting, bidding strategies, risk management);
• Planning for decarbonized systems: renewable siting, transmission expansion, and multi‑energy systems design using AI;
• AI for hydrogen and fuel‑cell systems (electrolyzer optimization, diagnostics, system integration);
• Electric mobility: smart charging strategies, fleet optimization, and EV–grid interaction;
• Environmental monitoring and protection using AI (air and water quality, emissions estimation, remote sensing, wildfire/flood risk);
• AI‑assisted life‑cycle assessment, circular‑economy strategies, waste‑to‑energy systems, and carbon footprint analysis;
• Carbon capture, utilization and storage (CCUS) modeling, monitoring, and optimization;
• Water–energy–food nexus modeling and resource allocation with AI;
• Generative AI and large language models for engineering knowledge management, anomaly summarization, and decision support;
• Benchmark datasets, standardized evaluation protocols, and reproducible MLOps for energy and environmental AI;
• Policy, ethics, equity, and societal impacts of AI‑enabled sustainable energy and environmental decision‑making.

Prof. Dr. Junhua Zhao
Prof. Dr. Yanbo Chen
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
AI ai	5.0	6.9	2020	19.2 Days	CHF 1800	Submit
Applied Sciences applsci	2.5	5.5	2011	16 Days	CHF 2400	Submit
Electronics electronics	2.6	6.1	2012	16.4 Days	CHF 2400	Submit
Energies energies	3.2	7.3	2008	16.8 Days	CHF 2600	Submit
Technologies technologies	3.6	8.5	2013	19.1 Days	CHF 1800	Submit

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Published Papers (2 papers)

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23 pages, 2454 KB  

Open AccessArticle

Sustainable Maritime Applications with Lightweight Classifier Using Modified MobileNet

by Gandeva Bayu Satrya, Febrian Kurniawan, Gelar Budiman, Adelia Octora Pristisahida, Bledug Kusuma Prasaja Moesdradjad, I Nyoman Apraz Ramatryana and Salah Eddine Choutri

Technologies 2026, 14(3), 161; https://doi.org/10.3390/technologies14030161 - 5 Mar 2026

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Abstract

The enormously growing demand for seafood has resulted in the over-exploitation of marine resources, pushing certain species to the brink of extinction. Overfishing is one of the main issues in sustainable marine development. To support marine resource protection and sustainable fishing, this study [...] Read more.

The enormously growing demand for seafood has resulted in the over-exploitation of marine resources, pushing certain species to the brink of extinction. Overfishing is one of the main issues in sustainable marine development. To support marine resource protection and sustainable fishing, this study proposes advanced fish classification techniques using state-of-the-art machine learning (ML). Specifically, the proposed method enables the precise identification of protected fish species, among other features. In this paper, we present a system-level optimization of the MobileNet architecture, termed M-MobileNet, designed to operate efficiently on resource-limited hardware environments. Our classifier is constructed by a refined modification of the well-known MobileNet neural network, resulting in a reduction of parameters. Furthermore, we have collected, organized, and compiled an original and comprehensive labeled dataset of 37,462 images of fish native to the Indonesian archipelago. The proposed model is trained on this dataset to classify images of captured fish and accurately identify their respective species. Furthermore, the system provides recommendations regarding the consumability of the catch. Compared to the MobileNet deep neural network structure, our model utilizes only 50% of the top-layer parameters, with approximately 42% GTX 860M utility. This configuration results in achieving up to 97% accuracy of classification. Considering the constrained computing capacity prevalent on many fishing vessels, our proposed model offers a practical solution for on-site fish classification. Moreover, synchronized implementation of the proposed model across multiple vessels can provide valuable insights into the movement and location of various fish species. Full article

(This article belongs to the Topic Applications of Artificial Intelligence in Sustainable Energy and Environment)

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17 pages, 3991 KB  

Open AccessArticle

Carbon Emission Forecasting Using Multi-Scale Temporal Patches

by Yuanhao Xiong and Meiling Wang

Appl. Sci. 2026, 16(4), 2025; https://doi.org/10.3390/app16042025 - 18 Feb 2026

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Abstract

Accurate carbon emission forecasting is essential for China’s dual-carbon targets and mitigation planning. However, many existing models struggle to capture long-range dependencies while remaining sensitive to short-term fluctuations. We evaluate State Space Transformer (SST) on a Rwanda dataset constructed from weekly Sentinel-5P observations. [...] Read more.

Accurate carbon emission forecasting is essential for China’s dual-carbon targets and mitigation planning. However, many existing models struggle to capture long-range dependencies while remaining sensitive to short-term fluctuations. We evaluate State Space Transformer (SST) on a Rwanda dataset constructed from weekly Sentinel-5P observations. The resulting time series are noisy, weakly periodic, and heterogeneous across monitoring sites. SST forms interrelated temporal patches through Multi-Scale Temporal Patches (MSTP). It models low-frequency trends with a Mamba state space backbone and captures high-frequency disturbances using an enhanced Local Window Transformer (LWT). These design choices explicitly disentangle low-frequency trends from high-frequency perturbations in noisy observations, improving robustness to non-stationary remote-sensing sequences. Across forecasting horizons from 6 to 72 weeks, SST achieves an average MSE of 0.0331. It reduces MSE by approximately 3.5% compared with the strongest baseline, PatchTST, and consistently outperforms other baselines. With short input histories, SST remains stable for one-year-ahead forecasting (about 53 weeks), which is critical when historical records are limited in operational monitoring systems. Ablation studies further show that MSTP, Mamba, and LWT each contribute substantially to accuracy. Overall, SST-style multi-scale modeling is well suited to noisy monitoring data and supports sustainable planning and emission-trend analysis. Full article

(This article belongs to the Topic Applications of Artificial Intelligence in Sustainable Energy and Environment)

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