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Comprehensive Forecasting of Electrical Quantities in an Educational Building via Artificial Intelligence-Driven

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This study introduces an AI-driven smart infrastructure for educational buildings to improve energy management. The system reliably forecasts electrical parameters, enabling optimized energy use and scalability.

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Area of Science:

  • Sustainable energy systems
  • Artificial Intelligence in power grids
  • Building energy management

Background:

  • Growing environmental concerns necessitate sustainable solutions in all sectors, including power systems.
  • Buildings are increasingly designed as net-zero emitters or retrofitted for energy efficiency.
  • Effective energy management is crucial for optimizing power system performance and reducing environmental impact.

Purpose of the Study:

  • To enhance the energy management capabilities of an educational building through a smart infrastructure.
  • To implement and validate an AI-driven framework for forecasting electrical parameters.
  • To explore the generalization of AI models across different measurement nodes within the infrastructure.

Main Methods:

  • Installation of photovoltaic panels and a distributed measurement system to collect voltage, current, and power data.
  • Development and application of an AI-driven framework for data management and forecasting.
  • Experimental validation of the AI model and correlation analysis of collected data.

Main Results:

  • The implemented system provides reliable forecasts of electrical parameters.
  • The distributed measurement system and data analysis offer valuable insights for energy optimization.
  • The AI model demonstrated generalization capabilities across measurement nodes, even with data gaps.

Conclusions:

  • The smart infrastructure effectively supports informed actions for optimizing energy management and system performance.
  • The study highlights the potential for accurate predictions and highlights ease of deployment for scalability.
  • The findings support the adaptation of this system in similar infrastructures for enhanced energy efficiency.