Hybrid Deep Learning and First-Principles Model for Predictive Control of Solar Direct Steam Generation

Physics-Informed Deep Learning and Rule-Based adjustment PIDL-RB for online Building Energy Management System: A Comparative Study

Using Data-Driven Simulation Models for Deep Reinforcement Learning Based HVAC Control

This manuscript summarizes ongoing doctoral research on control of building heating, ventilation, and air conditioning (HVAC) systems through deep reinforcement learning trained on data-driven simulators. The thesis investigates whether multivariate time-series forecasting models can act as reliable surrogates of physi...

Comparative Study of Deep Q-Network-Based Reinforcement Learning and Model Predictive Control for Zone Comfort and Energy Optimization in VAV-Controlled HVAC

HVAC systems account for a major part of building energy consumption, making advanced control strategies essential. This paper compares Deep Q-Network (DQN)-based reinforcement learning and model predictive control (MPC) for thermal comfort and energy optimization in a VAV-controlled HVAC system. Both approaches are ev...

Twin-delayed deep deterministic policy gradient for enhanced power optimization in solar PV-integrated DFIG wind energy systems.

The electrical power systems are facing rising challenges of stability and control with increasing share of intermittent renewable energy power sources. This work presents application of Twin-Delayed Deep Deterministic Policy Gradient (TD3) algorithm in single unified controller for multi-objective control of DFIG-Sola...

Intelligent Control of Parabolic Trough Collectors via Deep Reinforcement Learning

A novel reinforcement learning (RL)-based controller, designed specifically for solar thermal systems, designed to learn directly from operational data, enabling it to adapt its control policy in real time to mitigate external disturbances.

Ultra-Short-Term Photovoltaic Power Forecasting Based on a Hybrid Decomposition Linear Long Short-Term Memory Model

The rapid expansion of photovoltaic (PV) systems poses significant challenges to grid stability. Hybrid Energy Systems (HES) are intended to alleviate this volatility, yet their coordinated dispatch often remains suboptimal due to communication delays and ramp-rate constraints. Accurate ultra-short-term PV power foreca...