Enhancing grid stability with machine learning: A smart predictive approach to residential energy management

This research focuses on enhancing energy efficiency and grid stability in residential buildings by developing and evaluating advanced demand response (DR) strategies, explicitly comparing a Rule-Based model with a Predictive model leveraging machine learning. The Predictive Model utilised a neural...

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Bibliographic Details
Published in:Energy and buildings Vol. 338; p. 115729
Main Authors: Olawumi, Mattew A., Oladapo, B.I.
Format: Journal Article
Language:English
Published: Elsevier B.V 01.07.2025
Subjects:
Demand response
Energy efficiency
Grid stability
Machine learning
Predictive algorithms
Demand response
Energy efficiency
Grid stability
Predictive algorithms
Machine learning
ISSN:0378-7788
Online Access:Get full text
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Summary:This research focuses on enhancing energy efficiency and grid stability in residential buildings by developing and evaluating advanced demand response (DR) strategies, explicitly comparing a Rule-Based model with a Predictive model leveraging machine learning. The Predictive Model utilised a neural network with ReLU activation functions, optimised using grid search and cross-validation, and incorporated real-time data from smart meters and environmental sensors. Evaluation metrics demonstrated that the Predictive Model outperformed the Rule-Based Model, achieving a 15% reduction in electricity costs, a 20% improvement in energy efficiency, and a 15% reduction in peak load demands while maintaining a high predictive accuracy of 0.95%. However, these benefits came with increased computational complexity and resource requirements. The Rule-Based Model, while more straightforward and less resource-intensive, was less effective in dynamic environments. This study underscores the potential of integrating machine learning with real-time data for optimising residential energy management, offering significant cost savings and contributing to sustainable energy practices. The findings suggest that, despite higher computational demands, the Predictive Model provides superior adaptability and accuracy, making it a valuable tool for future smart grid applications.
ISSN:0378-7788
DOI:10.1016/j.enbuild.2025.115729