IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v308y2024ics036054422402601x.html
   My bibliography  Save this article

Comprehensive survey of artificial intelligence techniques and strategies for climate change mitigation

Author

Listed:
  • Amiri, Zahra
  • Heidari, Arash
  • Navimipour, Nima Jafari

Abstract

With the galloping progress of the changing climates all around the world, Machine Learning (ML) approaches have been prevalently studied in many types of research in this area. ML is a robust tool for acquiring perspectives from data. In this paper, we elaborate on climate change mitigation issues and ML approaches leveraged to solve these issues and aid in the improvement and function of sustainable energy systems. ML has been employed in multiple applications and many scopes of climate subjects such as ecosystems, agriculture, buildings and cities, industry, and transportation. So, a Systematic Literature Review (SLR) is applied to explore and evaluate findings from related research. In this paper, we propose a novel taxonomy of Deep Learning (DL) method applications for climate change mitigation, a comprehensive analysis that has not been conducted before. We evaluated these methods based on critical parameters such as accuracy, scalability, and interpretability and quantitatively compared their results. This analysis provides new insights into the effectiveness and reliability of DL methods in addressing climate change challenges. We classified climate change ML methods into six key customizable groups: ecosystems, industry, buildings and cities, transportation, agriculture, and hybrid applications. Afterward, state-of-the-art research on ML mechanisms and applications for climate change mitigation issues has been highlighted. In addition, many problems and issues related to ML implementation for climate change have been mapped, which are predicted to stimulate more researchers to manage the future disastrous effects of climate change. Based on the findings, most of the papers utilized Python as the most common simulation environment 38.5 % of the time. In addition, most of the methods were analyzed and evaluated in terms of some parameters, namely accuracy, latency, adaptability, and scalability, respectively. Lastly, classification is the most frequent ML task within climate change mitigation, accounting for 40 % of the total. Furthermore, Convolutional Neural Networks (CNNs) are the most widely utilized approach for a variety of applications.

Suggested Citation

  • Amiri, Zahra & Heidari, Arash & Navimipour, Nima Jafari, 2024. "Comprehensive survey of artificial intelligence techniques and strategies for climate change mitigation," Energy, Elsevier, vol. 308(C).
    • Handle: RePEc:eee:energy:v:308:y:2024:i:c:s036054422402601x
    DOI: 10.1016/j.energy.2024.132827
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054422402601X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2024.132827?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Pankaj Verma & Nitish Katal & Bhisham Sharma & Subrata Chowdhury & Abolfazl Mehbodniya & Julian L. Webber & Ali Bostani, 2022. "Voltage Rise Mitigation in PV Rich LV Distribution Networks Using DC/DC Converter Level Active Power Curtailment Method," Energies, MDPI, vol. 15(16), pages 1-16, August.
    2. Leal Filho, Walter & Wall, Tony & Rui Mucova, Serafino Afonso & Nagy, Gustavo J. & Balogun, Abdul-Lateef & Luetz, Johannes M. & Ng, Artie W. & Kovaleva, Marina & Safiul Azam, Fardous Mohammad & Alves,, 2022. "Deploying artificial intelligence for climate change adaptation," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    3. Zhan, Choujun & Cao, Weiwen & Fan, Junyu & Tse, C.K., 2018. "Impulse Weibull distribution for daily precipitation and climate change in China during 1961–2011," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 57-67.
    4. Joseph Nyangon & Ruth Akintunde, 2024. "Principal component analysis of day‐ahead electricity price forecasting in CAISO and its implications for highly integrated renewable energy markets," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 13(1), January.
    5. Paul X McCarthy & Xian Gong & Sina Eghbal & Daniel S Falster & Marian-Andrei Rizoiu, 2021. "Evolution of diversity and dominance of companies in online activity," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-19, April.
    6. Kaba, Kazım & Sarıgül, Mehmet & Avcı, Mutlu & Kandırmaz, H. Mustafa, 2018. "Estimation of daily global solar radiation using deep learning model," Energy, Elsevier, vol. 162(C), pages 126-135.
    7. Joseph Nyangon & John Byrne, 2023. "Estimating the impacts of natural gas power generation growth on solar electricity development: PJM's evolving resource mix and ramping capability," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 12(1), January.
    8. Zhang, Liang & Wen, Jin & Li, Yanfei & Chen, Jianli & Ye, Yunyang & Fu, Yangyang & Livingood, William, 2021. "A review of machine learning in building load prediction," Applied Energy, Elsevier, vol. 285(C).
    9. Hamed Tahami & Ehsan Akbari & Adil Hussein Mohammed & Reza Faraji & Sittiporn Channumsin, 2023. "A Transformerless Enhanced-Boost Quasi-Z-Source Inverter with Low Input Current Ripple for Stand-Alone RES-Based Systems," Energies, MDPI, vol. 16(6), pages 1-33, March.
    10. Sebastian Sippel & Nicolai Meinshausen & Erich M. Fischer & Enikő Székely & Reto Knutti, 2020. "Climate change now detectable from any single day of weather at global scale," Nature Climate Change, Nature, vol. 10(1), pages 35-41, January.
    11. Elbeltagi, Ahmed & Deng, Jinsong & Wang, Ke & Malik, Anurag & Maroufpoor, Saman, 2020. "Modeling long-term dynamics of crop evapotranspiration using deep learning in a semi-arid environment," Agricultural Water Management, Elsevier, vol. 241(C).
    12. Michael Keane & Timothy Neal, 2020. "Comparing deep neural network and econometric approaches to predicting the impact of climate change on agricultural yield," The Econometrics Journal, Royal Economic Society, vol. 23(3), pages 59-80.
    13. Salah L. Zubaidi & Sandra Ortega-Martorell & Patryk Kot & Rafid M. Alkhaddar & Mawada Abdellatif & Sadik K. Gharghan & Maytham S. Ahmed & Khalid Hashim, 2020. "A Method for Predicting Long-Term Municipal Water Demands Under Climate Change," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(3), pages 1265-1279, February.
    14. Alireza Dehghani & Mehdi Alidadi & Ayyoob Sharifi, 2022. "Compact Development Policy and Urban Resilience: A Critical Review," Sustainability, MDPI, vol. 14(19), pages 1-19, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Taejoon Kim & Haiyan Wang, 2025. "Global Dense Vector Representations for Words or Items Using Shared Parameter Alternating Tweedie Model," Mathematics, MDPI, vol. 13(4), pages 1-40, February.

    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. Makade, Rahul G. & Chakrabarti, Siddharth & Jamil, Basharat & Sakhale, C.N., 2020. "Estimation of global solar radiation for the tropical wet climatic region of India: A theory of experimentation approach," Renewable Energy, Elsevier, vol. 146(C), pages 2044-2059.
    2. Chen, Zhelun & O’Neill, Zheng & Wen, Jin & Pradhan, Ojas & Yang, Tao & Lu, Xing & Lin, Guanjing & Miyata, Shohei & Lee, Seungjae & Shen, Chou & Chiosa, Roberto & Piscitelli, Marco Savino & Capozzoli, , 2023. "A review of data-driven fault detection and diagnostics for building HVAC systems," Applied Energy, Elsevier, vol. 339(C).
    3. Gao, Yuan & Miyata, Shohei & Akashi, Yasunori, 2022. "Interpretable deep learning models for hourly solar radiation prediction based on graph neural network and attention," Applied Energy, Elsevier, vol. 321(C).
    4. Joseph Nyangon & Ruth Akintunde, 2024. "Anomaly Detection in California Electricity Price Forecasting: Enhancing Accuracy and Reliability Using Principal Component Analysis," Papers 2412.07787, arXiv.org.
    5. Lu, Yunbo & Wang, Lunche & Zhu, Canming & Zou, Ling & Zhang, Ming & Feng, Lan & Cao, Qian, 2023. "Predicting surface solar radiation using a hybrid radiative Transfer–Machine learning model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    6. Joanna Piotrowska-Woroniak & Tomasz Szul, 2022. "Application of a Model Based on Rough Set Theory (RST) to Estimate the Energy Efficiency of Public Buildings," Energies, MDPI, vol. 15(23), pages 1-13, November.
    7. Ling, Jihong & Zhang, Bingyang & Dai, Na & Xing, Jincheng, 2023. "Coupling input feature construction methods and machine learning algorithms for hourly secondary supply temperature prediction," Energy, Elsevier, vol. 278(C).
    8. Keyan Zheng & Fagang Hu & Yaliu Yang, 2023. "Data-Driven Evaluation and Recommendations for Regional Synergy Innovation Capability," Sustainability, MDPI, vol. 15(14), pages 1-21, July.
    9. Zhu, Jizhong & Dong, Hanjiang & Zheng, Weiye & Li, Shenglin & Huang, Yanting & Xi, Lei, 2022. "Review and prospect of data-driven techniques for load forecasting in integrated energy systems," Applied Energy, Elsevier, vol. 321(C).
    10. Abdul Ghani Olabi & Nabila Shehata & Hussein M. Maghrabie & Lobna A. Heikal & Mohammad Ali Abdelkareem & Shek Mohammod Atiqure Rahman & Sheikh Khaleduzzaman Shah & Enas Taha Sayed, 2022. "Progress in Solar Thermal Systems and Their Role in Achieving the Sustainable Development Goals," Energies, MDPI, vol. 15(24), pages 1-31, December.
    11. Wu, Xianguo & Feng, Zongbao & Chen, Hongyu & Qin, Yawei & Zheng, Shiyi & Wang, Lei & Liu, Yang & Skibniewski, Miroslaw J., 2022. "Intelligent optimization framework of near zero energy consumption building performance based on a hybrid machine learning algorithm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    12. Xian Gong & Paul X. McCarthy & Marian-Andrei Rizoiu & Paolo Boldi, 2024. "Harmony in the Australian Domain Space," Papers 2404.10006, arXiv.org.
    13. Zhou, Xinlei & Lin, Wenye & Kumar, Ritunesh & Cui, Ping & Ma, Zhenjun, 2022. "A data-driven strategy using long short term memory models and reinforcement learning to predict building electricity consumption," Applied Energy, Elsevier, vol. 306(PB).
    14. Taís Maria Nunes Carvalho & Francisco Souza Filho, 2021. "Variational Mode Decomposition Hybridized With Gradient Boost Regression for Seasonal Forecast of Residential Water Demand," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(10), pages 3431-3445, August.
    15. Liu, Che & Li, Fan & Zhang, Chenghui & Sun, Bo & Zhang, Guanguan, 2023. "A day-ahead prediction method for high-resolution electricity consumption in residential units," Energy, Elsevier, vol. 265(C).
    16. Ghimire, Sujan & Deo, Ravinesh C. & Raj, Nawin & Mi, Jianchun, 2019. "Deep solar radiation forecasting with convolutional neural network and long short-term memory network algorithms," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    17. Xiao, Tianqi & You, Fengqi, 2023. "Building thermal modeling and model predictive control with physically consistent deep learning for decarbonization and energy optimization," Applied Energy, Elsevier, vol. 342(C).
    18. Nyangon, Joseph & Darekar, Ayesha, 2024. "Advancements in hydrogen energy systems: A review of levelized costs, financial incentives and technological innovations," Innovation and Green Development, Elsevier, vol. 3(3).
    19. Marine Lanet & Laurent Li & Hervé Le Treut, 2024. "A framework to assess climate change effects on surface air temperature and soil moisture and application to Southwestern France," Climatic Change, Springer, vol. 177(12), pages 1-17, December.
    20. Joseph Nyangon & Ruth Akintunde, 2024. "Principal component analysis of day‐ahead electricity price forecasting in CAISO and its implications for highly integrated renewable energy markets," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 13(1), January.

    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:eee:energy:v:308:y:2024:i:c:s036054422402601x. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.