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Solar Energy Utilization Toward Sustainable Urban Futures
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Solar Energy Utilization Toward Sustainable Urban Futures

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A2: Solar Energy and Photovoltaic Systems".

Deadline for manuscript submissions: 25 February 2026 | Viewed by 1244

Special Issue Editors

Dr. Chuyao Wang

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Guest Editor
School of Energy and Environment, City University of Hong Kong, Kowloon, Hong Kong
Interests: solar energy; building energy saving; indoor environment
Special Issues, Collections and Topics in MDPI journals
Dr. Xin Li

E-Mail Website
Guest Editor
School of Energy and Environment, City University of Hong Kong, Kowloon, Hong Kong
Interests: smart windows; building energy saving; electromagnetic functional materials
Dr. Zhe Song

E-Mail Website
Guest Editor
Department of Building Environment and Energy Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
Interests: photovoltaics; machine learning; climate change

Special Issue Information

Dear Colleagues, 

Buildings contribute significantly to global carbon emissions, with operational energy consumption being a major factor. The integration of solar energy presents a promising solution for reducing this impact and fostering sustainability in urban environments. By effectively harnessing solar power, cities can enhance energy efficiency in buildings and move towards a more sustainable future.

This Special Issue seeks to encourage researchers to explore innovative strategies for the utilization of solar energy in urban settings. We invite submissions that address a wide range of topics, including, but not limited to, the following:

  • Development of advanced solar photovoltaic, thermal, and PV/T technologies;
  • Integration and optimization of solar energy solutions in urban architecture;
  • Innovations in energy storage and management for solar applications;
  • Policy frameworks supporting solar energy adoption;
  • Assessments of solar energy potential at city and regional scales.

Contributions may take the form of original research articles, comprehensive reviews, or case studies that align with the aims of this Special Issue. By sharing insights and advancements, we aim to foster a collaborative dialogue on the role of solar energy in shaping sustainable urban futures.

Dr. Chuyao Wang
Dr. Xin Li
Dr. Zhe Song
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-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

  • photovoltaic
  • solar heat utilization
  • solar energy storage and operation strategies
  • building energy saving
  • resource assessment and planning
  • energy economics and policy

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Published Papers (3 papers)

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Research

19 pages, 3901 KB  
Article
Application of Long Short-Term Memory Networks and SHAP Evaluation in the Solar Radiation Forecast
by Ming-Tang Tsai and I-Cheng Lo
Energies 2025, 18(23), 6099; https://doi.org/10.3390/en18236099 - 21 Nov 2025
Viewed by 194
Abstract
This paper proposes a hybrid forecasting framework that combines Long Short-Term Memory (LSTM) networks with Shapley Additive Explanations (SHAPs) to quickly and accurately predict solar radiation. Historical meteorological data from the Central Weather Administration (CWA) in Taiwan, spanning 2018–2023, are processed to construct [...] Read more.
This paper proposes a hybrid forecasting framework that combines Long Short-Term Memory (LSTM) networks with Shapley Additive Explanations (SHAPs) to quickly and accurately predict solar radiation. Historical meteorological data from the Central Weather Administration (CWA) in Taiwan, spanning 2018–2023, are processed to construct multivariate input features, including temperature, humidity, pressure, wind conditions, global radiation, and temporal encodings. The LSTM network is employed to capture nonlinear dependencies and temporal dynamics in the multivariate meteorological data. SHAP-guided feature selection reduces the number of input variables, thereby lowering computational cost and accelerating convergence without sacrificing accuracy. A case study in the Penghu region—characterized by abundant solar irradiance and active photovoltaic deployment—was conducted to evaluate the model under three scenarios. Results demonstrated that if the number of features decreases from fifteen to five, the number of model parameters is reduced from 53,569 to 51,521 and the computation time is reduced from 6 ms to 4 ms. The MSE and MAE remain within the range of 0.07~0.11 and 0.13~0.18, with almost no change. The LSTM–SHAP framework not only achieves high forecasting precision but also provides transparent explanations of key meteorological drivers, with the temperature, humidity, and temporal variables identified as the most influential factors. Overall, this research contributes a scalable and interpretable methodology for solar radiation prediction, offering practical implications for photovoltaic power dispatch, grid stability, and renewable energy planning. Full article
(This article belongs to the Special Issue Solar Energy Utilization Toward Sustainable Urban Futures)
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26 pages, 8885 KB  
Article
Wind-Induced Stability Identification and Safety Grade Catastrophe Evaluation of a Dish Concentrating Solar Thermal Power System
by Hongyan Zuo, Yuhao Su, Jingwei Liang, Guohai Jia, Mang Chen, Duzhong Nie and Jiaqiang E
Energies 2025, 18(23), 6088; https://doi.org/10.3390/en18236088 - 21 Nov 2025
Viewed by 186
Abstract
To avoid the problem of wind-induced resonance damage in a dish concentrating solar thermal power system (DCSTPS), a fluid dynamics model and a finite element analysis model of the DCSTPS were established separately. The wind load was mapped onto the surface of the [...] Read more.
To avoid the problem of wind-induced resonance damage in a dish concentrating solar thermal power system (DCSTPS), a fluid dynamics model and a finite element analysis model of the DCSTPS were established separately. The wind load was mapped onto the surface of the concentrator of the DCSTPS using the sequential coupling method, and the static analysis and modal analysis of the DCSTPS were established based on the fluid–structure coupling (FSC) method and the validity of the established model was verified. Based on the results, it can be concluded that the upper edge of the dish solar concentrator (DSC) of the DCSTPS and the three cantilever beams near the Stirling generator are the most vulnerable to being damaged, the DCSTPS will not experience strong resonance phenomena, and effects of the FSC will decrease the natural frequencies of each order. The results of the safety grade catastrophe evaluation of the DCSTPS showed that the safety grade of the DCSTPS was 0.2586 and 0.2819 under case 1 (α = 30°, β = 90°) and case 2 (α = 60°, β = 90°), where it was found that the membership value of the moment load was low, resulting in the stress on the connection seat of the altitude angle and the steering device of the base approaching the allowable stress of the material. Full article
(This article belongs to the Special Issue Solar Energy Utilization Toward Sustainable Urban Futures)
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30 pages, 2090 KB  
Article
From Trends to Insights: A Text Mining Analysis of Solar Energy Forecasting (2017–2023)
by Mohammed Asloune, Gilles Notton and Cyril Voyant
Energies 2025, 18(19), 5231; https://doi.org/10.3390/en18195231 - 1 Oct 2025
Viewed by 731
Abstract
This study aims to highlight key figures and organizations in solar energy forecasting research, including the most prominent authors, journals, and countries. It also clarifies commonly used abbreviations in the field, with a focus on forecasting methods and techniques, the form and type [...] Read more.
This study aims to highlight key figures and organizations in solar energy forecasting research, including the most prominent authors, journals, and countries. It also clarifies commonly used abbreviations in the field, with a focus on forecasting methods and techniques, the form and type of solar energy forecasting outputs, and the associated error metrics. Building on previous research that analyzed data up to 2017, the study updates findings to include information through 2023, incorporating metadata from 500 articles to identify key figures and organizations, along with 276 full-text articles analyzed for abbreviations. The application of text mining offers a concise yet comprehensive overview of the latest trends and insights in solar energy forecasting. The key findings of this study are threefold: First, China, followed by the United States of America and India, is the leading country in solar energy forecasting research, with shifts observed compared to the pre-2017 period. Second, numerous new abbreviations related to machine learning, particularly deep learning, have emerged in solar energy forecasting since before 2017, with Long Short-Term Memory, Convolutional Neural Networks, and Recurrent Neural Networks the most prominent. Finally, deterministic error metrics are mentioned nearly 11 times more frequently than probabilistic ones. Furthermore, perspectives on the practices and approaches of solar energy forecasting companies are also examined. Full article
(This article belongs to the Special Issue Solar Energy Utilization Toward Sustainable Urban Futures)
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