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Optimized environmental prediction in smart buildings using Dynamic Greylag Goose algorithm and deep learning.

Sayed Kenawy1, Amel Ali Alhussan2, Doaa Sami Khafaga2

  • 1Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura, 11152, Egypt. sayed.kenawy@deltauniv.edu.eg.

Scientific Reports
|March 28, 2026
PubMed
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This summary is machine-generated.

This study introduces a new AI framework for smart buildings, combining Dynamic Greylag Goose Optimization (DGGO) with Long Short-Term Memory (LSTM) networks for accurate environmental forecasting. The DGGO-LSTM model significantly improves prediction accuracy and computational efficiency.

Area of Science:

  • Environmental Science
  • Computer Science
  • Artificial Intelligence

Background:

  • Internet of Things (IoT) adoption in smart buildings generates vast data, posing challenges for environmental monitoring and control.
  • Existing research often prioritizes anomaly detection over high-accuracy environmental prediction, creating a need for advanced forecasting models.
  • Proactive environmental control in smart buildings requires robust prediction models to manage complex data streams.

Purpose of the Study:

  • To develop and evaluate a novel predictive framework for high-accuracy environmental forecasting in smart buildings.
  • To enhance environmental prediction by integrating feature selection and hyperparameter tuning using optimization algorithms.
  • To address the limitations of existing methods by improving the accuracy and efficiency of predicting key environmental parameters.
Keywords:
Environmental predictionIoT sensor networksLong short-term memorySmart buildings

Related Experiment Videos

Main Methods:

  • Proposed a predictive framework integrating Dynamic Greylag Goose Optimization (DGGO) with Long Short-Term Memory (LSTM) networks.
  • Utilized binary DGGO for sensor feature selection to reduce input dimensionality and optimize LSTM hyperparameters.
  • Applied the DGGO-LSTM model to predict temperature, humidity, air quality, sound, and light using a public IoT dataset.

Main Results:

  • DGGO-LSTM achieved the lowest Mean Squared Error (MSE) of 0.00119 and highest Nash-Sutcliffe Efficiency (NSE) of 0.98247.
  • Demonstrated a 17-37% reduction in MSE compared to GWO-LSTM, GGO-LSTM, and WOA-LSTM.
  • Achieved superior computational efficiency, reducing execution time by approximately 42% compared to WOA-LSTM.

Conclusions:

  • The DGGO-LSTM framework provides robust, efficient, and high-accuracy environmental forecasting for intelligent building systems.
  • The integration of deep learning with nature-inspired optimization offers a scalable approach for sustainable, data-driven control strategies.
  • This research advances smart building technology by enabling more effective proactive environmental management.