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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
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    References listed on IDEAS

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    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.
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