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Industry 4.0 has driven a transformation in industrial environments through the adoption of technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), and real-time Big Data analytics. In this context, efficient decision-making becomes essential to address process complexity and operational variability. This work proposes the development of an AI-based system to support informed and adaptive decision-making in industrial processes. By identifying efficiency indicators and applying advanced analytical techniques, the proposed system monitors industrial production processes and continuously adapts to real operating conditions. Adopting the Adaptive Business Intelligence (ABI) paradigm, this work integrates forecasting methods with energy efficiency metrics, pursuing a more effective and dynamic approach to operational management. This project aims to contribute to the advancement of decision support solutions in the Industry 4.0 landscape, promoting a more intelligent and responsive integration of analytical systems into industrial operations.

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Time Series Modeling for Smart Energy Consumption in Industry 4.0

  • 会议论文

  • 发表日期：

    2026年

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  • Lara Ferreira,
  • João Lopes,
  • Júlio Duarte,
  • Daniela Ferreira,
  • Rogério Pires,
  • Isabel Silva,
  • Manuel Santos

摘要

Industry 4.0 has driven a transformation in industrial environments through the adoption of technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), and real-time Big Data analytics. In this context, efficient decision-making becomes essential to address process complexity and operational variability. This work proposes the development of an AI-based system to support informed and adaptive decision-making in industrial processes. By identifying efficiency indicators and applying advanced analytical techniques, the proposed system monitors industrial production processes and continuously adapts to real operating conditions. Adopting the Adaptive Business Intelligence (ABI) paradigm, this work integrates forecasting methods with energy efficiency metrics, pursuing a more effective and dynamic approach to operational management. This project aims to contribute to the advancement of decision support solutions in the Industry 4.0 landscape, promoting a more intelligent and responsive integration of analytical systems into industrial operations.