Enhancing Smart Grid Sustainability: Using Advanced Hybrid Machine Learning Techniques While Considering Multiple Influencing Factors for Imputing Missing Electric Load Data

Zhiwen Hou () and Jingrui Liu
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Zhiwen Hou: Chongqing University-University of Cincinnati Joint Co-op Institute, Chongqing University, Chongqing 400044, China
Jingrui Liu: Chongqing University-University of Cincinnati Joint Co-op Institute, Chongqing University, Chongqing 400044, China

Sustainability, 2024, vol. 16, issue 18, 1-17

Abstract: Amidst the accelerating growth of intelligent power systems, the integrity of vast and complex datasets has become essential to promoting sustainable energy management, ensuring energy security, and supporting green living initiatives. This study introduces a novel hybrid machine learning model to address the critical issue of missing power load data—a problem that, if not managed effectively, can compromise the stability and sustainability of power grids. By integrating meteorological and temporal characteristics, the model enhances the precision of data imputation by combining random forest (RF), Spearman weighted k-nearest neighbors (SW-KNN), and Levenberg–Marquardt backpropagation (LM-BP) techniques. Additionally, a variance–covariance weighted method is used to dynamically adjust the model’s parameters to improve predictive accuracy. Tests on five metrics demonstrate that considering various correlated factors reduces errors by approximately 8–38%, and the hybrid modeling approach reduces predictive errors by 12–24% compared to single-model approaches. The proposed model not only ensures the resilience of power grid operations but also contributes to the broader goals of energy efficiency and environmental sustainability.

Keywords: sustainable energy management; smart grid resilience; hybrid machine learning model; power load data imputation; meteorological and temporal characteristics; variance–covariance weighted method (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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