IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i13p5874-d1687850.html
   My bibliography  Save this article

Smart Street Lighting Powered by Renewable Energy: A Multi-Criteria, Data-Driven Decision Framework

Author

Listed:
  • Jiachen Bian

    (Smart Mobility and Infrastructure Laboratory, College of Engineering, University of Georgia, Athens, GA 30602, USA)

  • Jidong J. Yang

    (Smart Mobility and Infrastructure Laboratory, College of Engineering, University of Georgia, Athens, GA 30602, USA)

Abstract

Renewable energy sources, such as solar and wind power, are gaining increasing global attention. To facilitate their integration into transportation infrastructure, this paper proposes a multi-criteria assessment framework for identifying the most suitable renewable energy sources for street lighting at any given location. The framework evaluates three key metrics: cost–benefit, reliability, and power generation potential, using time-series weather data. To demonstrate its effectiveness, we apply the framework to data from Georgia, USA. The results show that the proposed approach effectively classifies locations into four categories: solar-recommended, wind-recommended, hybrid-recommended, and no recommendation. Specifically, wind energy is primarily recommended in the southeastern region near the coastline, while solar energy is favored in the northwestern region. A hybrid of both sources is mainly recommended along the coast and in transitional areas. In several isolated parts of the northwest, neither energy source is recommended due to unfavorable weather conditions influenced by the local terrain. Since processing long-term time-series data is computationally intensive and challenging during inference, we train machine learning models, including Multilayer Perceptron (MLP) and Extreme Gradient Boosting (XGBoost), using temporally aggregated features for efficient and rapid decision-making. The MLP model achieves an overall accuracy of 92.4%, while XGBoost further improves accuracy to 94.3%. This study provides a practical reference for regional energy infrastructure planning, promoting optimized renewable energy use in street lighting through a robust, data-driven evaluation framework.

Suggested Citation

  • Jiachen Bian & Jidong J. Yang, 2025. "Smart Street Lighting Powered by Renewable Energy: A Multi-Criteria, Data-Driven Decision Framework," Sustainability, MDPI, vol. 17(13), pages 1-19, June.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:13:p:5874-:d:1687850
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/13/5874/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/13/5874/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ingeborg Graabak & Magnus Korpås, 2016. "Variability Characteristics of European Wind and Solar Power Resources—A Review," Energies, MDPI, vol. 9(6), pages 1-31, June.
    2. Giamalaki, Marina & Tsoutsos, Theocharis, 2019. "Sustainable siting of solar power installations in Mediterranean using a GIS/AHP approach," Renewable Energy, Elsevier, vol. 141(C), pages 64-75.
    3. Meysam Asadi & Kazem Pourhossein & Younes Noorollahi & Mousa Marzband & Gregorio Iglesias, 2023. "A New Decision Framework for Hybrid Solar and Wind Power Plant Site Selection Using Linear Regression Modeling Based on GIS-AHP," Sustainability, MDPI, vol. 15(10), pages 1-24, May.
    4. Ahmad, Tanveer & Zhang, Dongdong & Huang, Chao, 2021. "Methodological framework for short-and medium-term energy, solar and wind power forecasting with stochastic-based machine learning approach to monetary and energy policy applications," Energy, Elsevier, vol. 231(C).
    5. Draxl, Caroline & Clifton, Andrew & Hodge, Bri-Mathias & McCaa, Jim, 2015. "The Wind Integration National Dataset (WIND) Toolkit," Applied Energy, Elsevier, vol. 151(C), pages 355-366.
    6. Yang, Qing & Huang, Tianyue & Wang, Saige & Li, Jiashuo & Dai, Shaoqing & Wright, Sebastian & Wang, Yuxuan & Peng, Huaiwu, 2019. "A GIS-based high spatial resolution assessment of large-scale PV generation potential in China," Applied Energy, Elsevier, vol. 247(C), pages 254-269.
    7. Zhang, Hengxu & Cao, Yongji & Zhang, Yi & Terzija, Vladimir, 2018. "Quantitative synergy assessment of regional wind-solar energy resources based on MERRA reanalysis data," Applied Energy, Elsevier, vol. 216(C), pages 172-182.
    8. Dincer, Ibrahim & Rosen, Marc A., 1999. "Energy, environment and sustainable development," Applied Energy, Elsevier, vol. 64(1-4), pages 427-440, September.
    9. Yu, Ruiguo & Liu, Zhiqiang & Li, Xuewei & Lu, Wenhuan & Ma, Degang & Yu, Mei & Wang, Jianrong & Li, Bin, 2019. "Scene learning: Deep convolutional networks for wind power prediction by embedding turbines into grid space," Applied Energy, Elsevier, vol. 238(C), pages 249-257.
    10. Ruiz de la Hermosa González-Carrato, Raúl, 2018. "Wind farm monitoring using Mahalanobis distance and fuzzy clustering," Renewable Energy, Elsevier, vol. 123(C), pages 526-540.
    11. Wen, Lulu & Zhou, Kaile & Yang, Shanlin & Lu, Xinhui, 2019. "Optimal load dispatch of community microgrid with deep learning based solar power and load forecasting," Energy, Elsevier, vol. 171(C), pages 1053-1065.
    12. Shahid, Farah & Zameer, Aneela & Mehmood, Ammara & Raja, Muhammad Asif Zahoor, 2020. "A novel wavenets long short term memory paradigm for wind power prediction," Applied Energy, Elsevier, vol. 269(C).
    13. Duan, Jikai & Zuo, Hongchao & Bai, Yulong & Duan, Jizheng & Chang, Mingheng & Chen, Bolong, 2021. "Short-term wind speed forecasting using recurrent neural networks with error correction," Energy, Elsevier, vol. 217(C).
    14. Sharadga, Hussein & Hajimirza, Shima & Balog, Robert S., 2020. "Time series forecasting of solar power generation for large-scale photovoltaic plants," Renewable Energy, Elsevier, vol. 150(C), pages 797-807.
    15. Qin, Yong & Li, Kun & Liang, Zhanhao & Lee, Brendan & Zhang, Fuyong & Gu, Yongcheng & Zhang, Lei & Wu, Fengzhi & Rodriguez, Dragan, 2019. "Hybrid forecasting model based on long short term memory network and deep learning neural network for wind signal," Applied Energy, Elsevier, vol. 236(C), pages 262-272.
    16. Thapar, Vinay & Agnihotri, Gayatri & Sethi, Vinod Krishna, 2011. "Critical analysis of methods for mathematical modelling of wind turbines," Renewable Energy, Elsevier, vol. 36(11), pages 3166-3177.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ana Lagos & Joaquín E. Caicedo & Gustavo Coria & Andrés Romero Quete & Maximiliano Martínez & Gastón Suvire & Jesús Riquelme, 2022. "State-of-the-Art Using Bibliometric Analysis of Wind-Speed and -Power Forecasting Methods Applied in Power Systems," Energies, MDPI, vol. 15(18), pages 1-40, September.
    2. Liu, Yongqi & Qin, Hui & Zhang, Zhendong & Pei, Shaoqian & Jiang, Zhiqiang & Feng, Zhongkai & Zhou, Jianzhong, 2020. "Probabilistic spatiotemporal wind speed forecasting based on a variational Bayesian deep learning model," Applied Energy, Elsevier, vol. 260(C).
    3. Wang, Yun & Song, Mengmeng & Yang, Dazhi, 2024. "Local-global feature-based spatio-temporal wind speed forecasting with a sparse and dynamic graph," Energy, Elsevier, vol. 289(C).
    4. Ma, Zhengjing & Mei, Gang, 2022. "A hybrid attention-based deep learning approach for wind power prediction," Applied Energy, Elsevier, vol. 323(C).
    5. Lim, Juin Yau & Safder, Usman & How, Bing Shen & Ifaei, Pouya & Yoo, Chang Kyoo, 2021. "Nationwide sustainable renewable energy and Power-to-X deployment planning in South Korea assisted with forecasting model," Applied Energy, Elsevier, vol. 283(C).
    6. Sward, Jeffrey A. & Nilson, Roberta S. & Katkar, Venktesh V. & Stedman, Richard C. & Kay, David L. & Ifft, Jennifer E. & Zhang, K. Max, 2021. "Integrating social considerations in multicriteria decision analysis for utility-scale solar photovoltaic siting," Applied Energy, Elsevier, vol. 288(C).
    7. Wang, Yun & Zou, Runmin & Liu, Fang & Zhang, Lingjun & Liu, Qianyi, 2021. "A review of wind speed and wind power forecasting with deep neural networks," Applied Energy, Elsevier, vol. 304(C).
    8. Sabarathinam Srinivasan & Suresh Kumarasamy & Zacharias E. Andreadakis & Pedro G. Lind, 2023. "Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey," Energies, MDPI, vol. 16(14), pages 1-56, July.
    9. Tavakol Aghaei, Vahid & Ağababaoğlu, Arda & Bawo, Biram & Naseradinmousavi, Peiman & Yıldırım, Sinan & Yeşilyurt, Serhat & Onat, Ahmet, 2023. "Energy optimization of wind turbines via a neural control policy based on reinforcement learning Markov chain Monte Carlo algorithm," Applied Energy, Elsevier, vol. 341(C).
    10. Jie Du & Shuaizhi Chen & Linlin Pan & Yubao Liu, 2025. "A Wind Speed Prediction Method Based on Signal Decomposition Technology Deep Learning Model," Energies, MDPI, vol. 18(5), pages 1-26, February.
    11. Cheng, Lilin & Zang, Haixiang & Wei, Zhinong & Zhang, Fengchun & Sun, Guoqiang, 2022. "Evaluation of opaque deep-learning solar power forecast models towards power-grid applications," Renewable Energy, Elsevier, vol. 198(C), pages 960-972.
    12. Pillot, Benjamin & Al-Kurdi, Nadeem & Gervet, Carmen & Linguet, Laurent, 2021. "Optimizing operational costs and PV production at utility scale: An optical fiber network analogy for solar park clustering," Applied Energy, Elsevier, vol. 298(C).
    13. Alfeus Sunarso & Kunhali Ibrahim-Bathis & Sakti A. Murti & Irwan Budiarto & Harold S. Ruiz, 2020. "GIS-Based Assessment of the Technical and Economic Feasibility of Utility-Scale Solar PV Plants: Case Study in West Kalimantan Province," Sustainability, MDPI, vol. 12(15), pages 1-12, August.
    14. Hong, Ying-Yi & Satriani, Thursy Rienda Aulia, 2020. "Day-ahead spatiotemporal wind speed forecasting using robust design-based deep learning neural network," Energy, Elsevier, vol. 209(C).
    15. Moradzadeh, Arash & Moayyed, Hamed & Mohammadi-Ivatloo, Behnam & Vale, Zita & Ramos, Carlos & Ghorbani, Reza, 2023. "A novel cyber-Resilient solar power forecasting model based on secure federated deep learning and data visualization," Renewable Energy, Elsevier, vol. 211(C), pages 697-705.
    16. Francisco Bilendo & Angela Meyer & Hamed Badihi & Ningyun Lu & Philippe Cambron & Bin Jiang, 2022. "Applications and Modeling Techniques of Wind Turbine Power Curve for Wind Farms—A Review," Energies, MDPI, vol. 16(1), pages 1-38, December.
    17. Fotsing Metegam, Isabelle Flora & Wolff, Eleonore & Tchinda, René & Huart, Michel & Chara-Dackou, Venant Sorel, 2025. "Evaluation of on-grid and off-grid solar photovoltaic sites in Cameroon using geographic information systems, fuzzy logic, and multi-criteria analysis," Energy, Elsevier, vol. 318(C).
    18. Lv, Furong & Tang, Haiping, 2024. "Sustainable photovoltaic power generation spatial planning through ecosystem service valuation: A case study of the Qinghai-Tibet plateau," Renewable Energy, Elsevier, vol. 222(C).
    19. Paweł Piotrowski & Dariusz Baczyński & Marcin Kopyt & Tomasz Gulczyński, 2022. "Advanced Ensemble Methods Using Machine Learning and Deep Learning for One-Day-Ahead Forecasts of Electric Energy Production in Wind Farms," Energies, MDPI, vol. 15(4), pages 1-30, February.
    20. Arnas Uselis & Mantas Lukoševičius & Lukas Stasytis, 2020. "Localized Convolutional Neural Networks for Geospatial Wind Forecasting," Energies, MDPI, vol. 13(13), pages 1-21, July.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:17:y:2025:i:13:p:5874-:d:1687850. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.