Self-adaptive evolutionary neural networks for high-precision short-term electric load forecasting

Self-adaptive evolutionary neural networks for high-precision short-term electric load forecasting

Abstract Reliable short-term electric load forecasting (STLF) is essential for enhancing grid stability, optimizing energy distribution, and minimizing operational costs in modern power systems. However, existing forecasting models, including statistical approaches and deep learning architectures su...

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Bibliographic Details
Main Authors: Muhammad Abbas, Yanbo Che, Sarmad Maqsood, Muhammad Zain Yousaf, Mustafa Abdullah, Wajid Khan, Saqib Khalid, Mohit Bajaj, Mohammad Shabaz
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Electric load forecasting
Kolmogorov–Arnold networks
Machine learning
Optimization
Power systems
Self-adaptive differential evolution
Online Access:https://doi.org/10.1038/s41598-025-05918-w
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https://doi.org/10.1038/s41598-025-05918-w

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