Model for Real-Time Decision Intelligence Supporting Energy Efficiency and Environmental Sustainability

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@article{1715281,
      author = {Bisola Akeju, Olumide Kumuyi, Esther Uzoka, David Excel Ozowara},
      title = {Model for Real-Time Decision Intelligence Supporting Energy Efficiency and Environmental Sustainability},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {8},
      number = {2},
      pages = {1300-1316},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1715281.pdf},
      abstract = {The escalating demand for energy, coupled with increasing environmental concerns, necessitates advanced frameworks for real-time decision intelligence to optimize energy consumption and promote sustainability. Traditional energy management systems often rely on static control strategies and delayed reporting, which limit responsiveness to dynamic operational conditions and hinder effective integration of renewable energy sources. This presents a conceptual model for real-time decision intelligence, designed to support energy efficiency and environmental sustainability by leveraging data from Internet of Things (IoT) sensor networks, advanced analytics, and machine learning algorithms.The proposed model integrates heterogeneous data streams from smart meters, occupancy sensors, weather stations, and renewable energy generators to provide a comprehensive view of energy usage patterns. Machine learning techniques, including predictive analytics, anomaly detection, and optimization algorithms, enable proactive decision-making, such as adjusting heating, ventilation, and air conditioning (HVAC) settings, optimizing lighting schedules, and managing distributed energy resources in real time. By continuously analyzing energy consumption trends and environmental factors, the system can recommend or autonomously execute energy-saving interventions while minimizing operational disruption.Key components of the model include a data aggregation and processing layer for cleansing, normalizing, and contextualizing raw sensor data, a predictive analytics engine for forecasting demand and identifying inefficiencies, and a decision support interface for real-time visualization, alerts, and automated controls. The framework emphasizes scalability, interoperability, and adaptability, allowing it to operate across commercial buildings, industrial facilities, and smart grid networks.Simulation studies and case scenarios demonstrate that the model can significantly reduce energy consumption, lower carbon emissions, and support compliance with environmental sustainability standards. By integrating real-time intelligence, machine learning, and IoT infrastructure, this approach enables organizations to achieve energy optimization proactively, enhance operational efficiency, and contribute to global sustainability goals. The proposed model represents a forward-looking paradigm for intelligent, responsive, and environmentally responsible energy management.},
      keywords = {Time Decision Intelligence, Energy Efficiency, Environmental Sustainability, Predictive Analytics, IoT Sensors, Smart Grids, Renewable Energy Integration, Adaptive Control Systems, Machine Learning},
      month = {August},
      doi = {https://doi.org/10.64388/IREV8I2-1715281}
  }