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Atmosphere
Special Issues
Carbon Neutrality, Renewable Energy and Climate Change Impacts
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Carbon Neutrality, Renewable Energy and Climate Change Impacts

A Special Issue of Atmosphere (ISSN 2073-4433) belonging to the section "Climatology".

Deadline for manuscript submissions: 10 December 2026 | Viewed by 518

Editors

Dr. Junhong Guo

E-Mail Website
Guest Editor
College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China
Interests: high-resolution climate projection; climate change impacts assessment; extreme climate and weather events
Dr. Xander Wang

E-Mail Website
Guest Editor
School of Climate Change and Adaptation, University of Prince Edward Island, Charlottetown, PE C1A 4P3, Canada
Interests: regional climate modeling; climate downscaling; hydrological modeling and flooding risk analysis; energy systems modeling under climate change; climate change impact assessment and adaptation studies; GIS; spatial modeling and analysis; big data analysis and visualization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the interdisciplinary nexus of carbon neutrality, renewable energy development, and climate change impacts, addressing the urgent global challenge of balancing decarbonization goals with atmospheric environmental stability. It welcomes original research and critical reviews exploring the synergies and trade-offs between renewable energy deployment (solar, wind, hydro, etc.) and climate change dynamics, including regional climate simulations, statistical or dynamical downscaling techniques for climate models, climate-driven variations in renewable energy potential, atmospheric feedback effects of low-carbon energy transitions. Topics cover quantitative assessments of greenhouse gas mitigation via renewable energy, climate change impacts on energy system, carbon-neutral pathways for atmospheric pollution co-control, and regional/global policy frameworks for integrated energy-climate governance. We aim to advance evidence-based insights for optimizing renewable energy strategies under climate change, bridging atmospheric science, energy engineering, and environmental policy to support actionable solutions for global carbon neutrality and atmospheric sustainability.

Dr. Junhong Guo
Dr. Xander Wang
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 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. Atmosphere is an international peer-reviewed open access monthly 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

  • climate
  • meteorology
  • energy
  • coupling
  • optimization

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Published Papers (1 paper)

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Research

38 pages, 5009 KB  
Article
A Similarity-Enhanced Transformer-LSTM Framework with IPOA for Short-Term Photovoltaic Power Forecasting
by Xiaoxiao Wei, Tao Wang, Xu Wang, Ye Xu and Wei Li
Atmosphere 2026, 17(9), 842; https://doi.org/10.3390/atmos17090842 - 28 Aug 2026
Viewed by 245
Abstract
Accurate prediction of PV output is critical for optimizing its absorption potential and ensuring the safe, stable, and cost-effective operation of the power grid. Yet, due to the intermittent and stochastic nature of photovoltaic power generation, establishing a highly precise prediction model presents [...] Read more.
Accurate prediction of PV output is critical for optimizing its absorption potential and ensuring the safe, stable, and cost-effective operation of the power grid. Yet, due to the intermittent and stochastic nature of photovoltaic power generation, establishing a highly precise prediction model presents significant difficulties. In this study, a hybrid forecasting framework integrating WCSD, CEEMDAN-FE, IPOA, and Transformer-LSTM is developed to improve PV power forecasting accuracy. Firstly, a new training data sample generation method based on WCSD is developed for determining the historical days having similar meteorological conditions to the predicted day. Secondly, CEEMDAN is employed to decompose original output sequence into an ensemble of components with different amplitudes and frequencies, where they were recombined as new set including a handful of components with low-frequency variation characteristics based on FE index. Thirdly, the IPOA is proposed for the first time, which couples Gaussian mutation and enhanced circle chaotic mapping. Next, the prediction model for each component is formulated by aid of Transformer-LSTM algorithm, the optimal hyperparameter combination of which is determined by IPOA method. Finally, the predicted results are obtained as the sum of individual component predictions. The prediction performance of the designed model is tested and verified via experimental analysis located in Yunnan Province, China and the publicly available Australian DKASC dataset. The empirical findings demonstrate that, in contrast to alternative benchmark models, our developed hybrid prediction model consistently attains superior prediction accuracy. Full article
(This article belongs to the Special Issue Carbon Neutrality, Renewable Energy and Climate Change Impacts)
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