TL;DR
An IoT task management mechanism based on predictive optimization to minimize energy consumption in smart residential buildings is proposed and achieves near-perfect prediction performance of power consumption.
An IoT-based energy management system for EVs by combining the harbor seal whisker optimization (HSWO) and the improved Elman spike neural network (IESNN) has been proposed.
The findings demonstrate that combining deep representation learning with adaptive optimization improves classification accuracy and stability, offering practical value for sector-aware energy planning, load prioritization, and data-driven decision support in smart city energy management.
The review highlights the significance of machine learning for load forecasting and the prediction of energy usage in buildings, and investigates cutting-edge modelling techniques such as digital twin technology, demonstrating its potential to contribute to energy efficiency.
The rise of renewable energy systems has driven the need for more intelligent techniques to enhance efficiency, reliability, and sustainability in a DC solar microgrid. This paper presents a framework of adaptive predictive energy optimization (APEO) algorithm-driven intelligent energy management and predictive fault d...
This work proposes and justifies a cluster-aware approach partitioning data into K regimes such that D=⋃c=1KCc, and proposes and justifies a GA-XGBoost model, enabling an intermediate granularity between global and meter-level learning.
An overview of emerging ML techniques and practical lessons are given to the researchers and practitioners to develop intelligent, scalable and sustainable solutions for energy optimization in next-generation smart buildings and industrial facilities.
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