IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i16p5892-d887837.html
   My bibliography  Save this article

An Auto-Extraction Framework for CEP Rules Based on the Two-Layer LSTM Attention Mechanism: A Case Study on City Air Pollution Forecasting

Author

Listed:
  • Yuan Liu

    (Ministry of Education Key Laboratory for Modern Teaching Technology, Shaanxi Normal University, Xi’an 710119, China
    School of Computer Science, Shaanxi Normal University, Xi’an 710062, China)

  • Wangyang Yu

    (Ministry of Education Key Laboratory for Modern Teaching Technology, Shaanxi Normal University, Xi’an 710119, China
    School of Computer Science, Shaanxi Normal University, Xi’an 710062, China)

  • Cong Gao

    (School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK)

  • Minsi Chen

    (School of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK)

Abstract

Energy is at the center of human society and drives the technologies and overall human well-being. Today, artificial intelligence (AI) technologies are widely used for system modeling, prediction, control, and optimization in the energy sector. The internet of things (IoT) is the core of the third wave of the information industry revolution and AI. In the energy sector, tens of billions of IoT appliances are linked to the Internet, and these appliances generate massive amounts of data every day. Extracting useful information from the massive amount of data will be a very meaningful thing. Complex event processing (CEP) is a stream-based technique that can extract beneficial information from real-time data through pre-establishing pattern rules. The formulation of pattern rules requires strong domain expertise. Therefore, at present, the pattern rules of CEP still need to be manually formulated by domain experts. However, in the face of complex, massive amounts of IoT data, manually setting rules will be a very difficult task. To address the issue, this paper proposes a CEP rule auto-extraction framework by combining deep learning methods with data mining algorithms. The framework can automatically extract pattern rules from unlabeled air pollution data. The deep learning model we presented is a two-layer LSTM (long short-term memory) with an attention mechanism. The framework has two phases: in the first phase, the anomalous data is filtered out and labeled from the IoT data through the deep learning model we proposed, and then the pattern rules are mined from the labeled data through the decision tree data mining algorithm in the second phase. We compare other deep learning models to evaluate the feasibility of the framework. In addition, in the rule extraction stage, we use a decision tree data mining algorithm, which can achieve high accuracy. Experiments have shown that the framework we proposed can effectively extract meaningful and accurate CEP rules. The research work in this paper will help support the advancement of the sector of air pollution prediction, assist in the establishment of air pollution regulatory strategies, and further contribute to the development of a green energy structure.

Suggested Citation

  • Yuan Liu & Wangyang Yu & Cong Gao & Minsi Chen, 2022. "An Auto-Extraction Framework for CEP Rules Based on the Two-Layer LSTM Attention Mechanism: A Case Study on City Air Pollution Forecasting," Energies, MDPI, vol. 15(16), pages 1-16, August.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:16:p:5892-:d:887837
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/16/5892/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/16/5892/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Robert Cichowicz & Maciej Dobrzański, 2022. "Analysis of Air Pollution around a CHP Plant: Real Measurements vs. Computer Simulations," Energies, MDPI, vol. 15(2), pages 1-18, January.
    2. Venelin Todorov & Ivan Dimov, 2022. "Innovative Digital Stochastic Methods for Multidimensional Sensitivity Analysis in Air Pollution Modelling," Mathematics, MDPI, vol. 10(12), pages 1-14, June.
    3. Naser Hossein Motlagh & Mahsa Mohammadrezaei & Julian Hunt & Behnam Zakeri, 2020. "Internet of Things (IoT) and the Energy Sector," Energies, MDPI, vol. 13(2), pages 1-27, January.
    4. Kaplan, Andreas & Haenlein, Michael, 2020. "Rulers of the world, unite! The challenges and opportunities of artificial intelligence," Business Horizons, Elsevier, vol. 63(1), pages 37-50.
    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. Nallapaneni Manoj Kumar & Aneesh A. Chand & Maria Malvoni & Kushal A. Prasad & Kabir A. Mamun & F.R. Islam & Shauhrat S. Chopra, 2020. "Distributed Energy Resources and the Application of AI, IoT, and Blockchain in Smart Grids," Energies, MDPI, vol. 13(21), pages 1-42, November.
    2. Matteo Vaccargiu & Andrea Pinna & Roberto Tonelli & Luisanna Cocco, 2023. "Blockchain in the Energy Sector for SDG Achievement," Sustainability, MDPI, vol. 15(20), pages 1-23, October.
    3. Athanasios Tsipis & Asterios Papamichail & Ioannis Angelis & George Koufoudakis & Georgios Tsoumanis & Konstantinos Oikonomou, 2020. "An Alertness-Adjustable Cloud/Fog IoT Solution for Timely Environmental Monitoring Based on Wildfire Risk Forecasting," Energies, MDPI, vol. 13(14), pages 1-35, July.
    4. Shrestha, Yash Raj & Krishna, Vaibhav & von Krogh, Georg, 2021. "Augmenting organizational decision-making with deep learning algorithms: Principles, promises, and challenges," Journal of Business Research, Elsevier, vol. 123(C), pages 588-603.
    5. Abdul Hasib Siddique & Mehedi Hasan & Sharnali Islam & Khalid Rashid, 2021. "Prospective Smart Distribution Substation in Bangladesh: Modeling and Analysis," Sustainability, MDPI, vol. 13(19), pages 1-20, September.
    6. Hosseini Dehshiri, Seyyed Jalaladdin & Amiri, Maghsoud, 2023. "Evaluating the risks of the internet of things in renewable energy systems using a hybrid fuzzy decision approach," Energy, Elsevier, vol. 285(C).
    7. Leslier Valenzuela-Fernández & Manuel Escobar-Farfán, 2022. "Zero-Waste Management and Sustainable Consumption: A Comprehensive Bibliometric Mapping Analysis," Sustainability, MDPI, vol. 14(23), pages 1-24, December.
    8. Germán Arana-Landín & Naiara Uriarte-Gallastegi & Beñat Landeta-Manzano & Iker Laskurain-Iturbe, 2023. "The Contribution of Lean Management—Industry 4.0 Technologies to Improving Energy Efficiency," Energies, MDPI, vol. 16(5), pages 1-19, February.
    9. Krzysztof Bartczak & Stanisław Łobejko, 2022. "The Implementation Environment for a Digital Technology Platform of Renewable Energy Sources," Energies, MDPI, vol. 15(16), pages 1-16, August.
    10. Ying Chen & Qi Da & Weizhang Liang & Peng Xiao & Bing Dai & Guoyan Zhao, 2022. "Bagged Ensemble of Gaussian Process Classifiers for Assessing Rockburst Damage Potential with an Imbalanced Dataset," Mathematics, MDPI, vol. 10(18), pages 1-22, September.
    11. Shabana Urooj & Fadwa Alrowais & Yuvaraja Teekaraman & Hariprasath Manoharan & Ramya Kuppusamy, 2021. "IoT Based Electric Vehicle Application Using Boosting Algorithm for Smart Cities," Energies, MDPI, vol. 14(4), pages 1-16, February.
    12. M. Usman Saleem & Mustafa Shakir & M. Rehan Usman & M. Hamza Tahir Bajwa & Noman Shabbir & Payam Shams Ghahfarokhi & Kamran Daniel, 2023. "Integrating Smart Energy Management System with Internet of Things and Cloud Computing for Efficient Demand Side Management in Smart Grids," Energies, MDPI, vol. 16(12), pages 1-21, June.
    13. Singh, Pratibha & Sharma, Mahak & Daim, Tugrul, 2024. "Envisaging AR travel revolution for visiting heritage sites: A mixed-method approach," Technology in Society, Elsevier, vol. 76(C).
    14. Shivam Gupta & Jazmin Campos Zeballos & Gema del Río Castro & Ana Tomičić & Sergio Andrés Morales & Maya Mahfouz & Isimemen Osemwegie & Vicky Phemia Comlan Sessi & Marina Schmitz & Nady Mahmoud & Mnen, 2023. "Operationalizing Digitainability: Encouraging Mindfulness to Harness the Power of Digitalization for Sustainable Development," Sustainability, MDPI, vol. 15(8), pages 1-37, April.
    15. Wen-Cheng Wang & Ngakan Ketut Acwin Dwijendra & Biju Theruvil Sayed & José Ricardo Nuñez Alvarez & Mohammed Al-Bahrani & Aníbal Alviz-Meza & Yulineth Cárdenas-Escrocia, 2023. "Internet of Things Energy Consumption Optimization in Buildings: A Step toward Sustainability," Sustainability, MDPI, vol. 15(8), pages 1-15, April.
    16. Prikshat, Verma & Islam, Mohammad & Patel, Parth & Malik, Ashish & Budhwar, Pawan & Gupta, Suraksha, 2023. "AI-Augmented HRM: Literature review and a proposed multilevel framework for future research," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    17. Joaquim Amândio Azevedo & Filipe Edgar Santos, 2021. "A More Efficient Technique to Power Home Monitoring Systems Using Controlled Battery Charging," Energies, MDPI, vol. 14(13), pages 1-16, June.
    18. Akhil Joseph & Patil Balachandra, 2020. "Energy Internet, the Future Electricity System: Overview, Concept, Model Structure, and Mechanism," Energies, MDPI, vol. 13(16), pages 1-26, August.
    19. Waymond Rodgers & Tam Nguyen, 2022. "Advertising Benefits from Ethical Artificial Intelligence Algorithmic Purchase Decision Pathways," Journal of Business Ethics, Springer, vol. 178(4), pages 1043-1061, July.
    20. Sheeraz Kirmani & Abdul Mazid & Irfan Ahmad Khan & Manaullah Abid, 2022. "A Survey on IoT-Enabled Smart Grids: Technologies, Architectures, Applications, and Challenges," Sustainability, MDPI, vol. 15(1), pages 1-26, December.

    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:jeners:v:15:y:2022:i:16:p:5892-:d:887837. 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.