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Conference Jul 2026

Reinforcement Learning-Based Building Energy Optimization for Flexibility Markets

The rapid growth of energy markets and demand-side response programs has created a significant need for intelligent building-level control strategies to capture high volatility energy consumption in response to price signals and grid conditions. This paper presents a reinforcement learning (RL)-based building energy ma...

Abdulaziz Almalaq · 0 citations
Aug 2026

Collaborative optimization of car-following control and energy management for PHEBs based on deep reinforcement learning

Simulation results demonstrate that, compared to the traditional hierarchical optimization framework, the proposed strategy achieves significant improvements in terms of mean absolute jerk, root-mean-square (RMS) value of acceleration, power demand, battery SOH degradation, battery temperature violation, and comprehens...

Chengrui Zhang, Fei Ju, Sichen Gao et al. · 0 citations
Open access Jul 2026

AI-driven optimization: revolutionizing energy efficiency in modern buildings.

This work establishes a novel continuous-time dynamic-policy learning paradigm that integrates predictive modeling with real-time adaptive control, advancing data-driven intelligent building operation toward sustainable and autonomous energy management.

Hamoud H. Alshammari · 0 citations
Open access Aug 2026

AI-Based Digital Twins for Renewable Energy Grid Optimization: A Framework for Real-Time Forecasting, State Estimation and Adaptive Dispatch

An AI-driven digital twin (DT) framework that pairs a real-time physical-data grid model with a layered AI optimisation engine made up of an LSTM/Transformer forecaster, a graph neural network (GNN) state estimator, and a deep reinforcement learning (DRL) dispatch controller is proposed.

Dr. G. Sripriya, V. S. Guhan, A. S. Nandha Kisore · 0 citations
Jul 2026

A deep reinforcement learning framework for hybrid electric vehicle energy management: Integrating real-world data augmentation and multi-scale perception

A fresh enhanced training set covering all-round driving cycles based on real driving data is constructed, thus breaking through the training restrictions brought by standard driving cycles, and improves fuel economy by 5.2% on average across various driving cycles compared to adaptive ECMS (AECMS).

Yushan Li, Lianbo Zhao, Fanyu Meng et al. · 0 citations
Open access Aug 2026

Deep Learning-Supported Hybrid Renewable Energy System Optimization

Deep learning–based surrogate models can generate sizing decisions comparable to those obtained using PSO with only a fraction of the computational effort, and provide a fast and practical alternative to conventional iterative optimization methods for component sizing in smart grid and sustainable energy planning appli...

Yasemin Alakoç Bozkurt, Cemil Altın, Talip Çay · 1 citation

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