A Physics-Informed GAN Algorithm for Energy-Saving Optimization of Parallel Chiller Plants

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

Safe Deep Reinforcement Learning for Energy-Efficient HVAC Control in Multi-Zone Residential Buildings

HVAC systems represent a major share of building energy consumption. Traditional control strategies are limited in coordinating energy-comfort tradeoffs across multiple zones simultaneously. Reinforcement learning (RL) offers adaptive, data-driven control that optimizes performance over time. However, deploying learned neural network controllers in safety-critical building systems remains challenging due to lack of formal safety guarantees. We propose a safety-certified deep RL framework for multi-zone residential HVAC control. Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) agents are trained in an EnergyPlus/Sinergym simulation to minimize energy consumption while maintaining thermal comfort. Post-training safety certification is performed on the PPO policy using Lipschitz-based forward invariance analysis, building on existing tools for the computation of Lipschitz constants for neural networks, to guarantee constraint satisfaction. Both agents are evaluated over an annual simulation cycle in an eight-zone variable refrigerant flow (VRF) testbed. The PPO agent achieves 67\% comfort violation reduction compared to rule-based control, while the SAC agent achieves 27.6\% energy savings. The PPO policy satisfies formal safety certification with a margin of $2.003^\circ$C. These results demonstrate the feasibility of combining reinforcement learning with post-training safety verification for multi-zone building control.

A deep surrogate modeling approach for distributed control of aggregated air-conditioning loads

A three-layer “Aggregator-Edge-End user” architecture leveraging a data-driven workflow and a hybrid deep learning model combining a Temporal Convolutional Network (TCN) and a Bidirectional Long Short-Term Memory (BiLSTM) network is introduced.

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

Rising global energy demand and increasing decarbonization requirements have intensified the need for intelligent building energy management capable of handling nonlinear dynamics and multi-objective operational trade-offs. Conventional discrete-time and simulation-dependent control strategies often struggle to maintain temporal continuity, adaptive responsiveness, and consistent performance across heterogeneous building environments. Addressing these limitations, NODE-RL-BEM (Neural Ordinary Differential Equation Reinforcement Learning for Building Energy Management) introduces a unified continuous-time optimization paradigm that jointly models system dynamics and learns adaptive control policies. The approach integrates heterogeneous operational data, temporal state embeddings, neural differential equation modeling, and multi-objective reinforcement learning within a cohesive architecture designed for predictive and responsive energy optimization. Performance evaluation conducted on the ASHRAE Great Energy Predictor III dataset and the Intelligent Indoor Environment Dataset demonstrates the effectiveness of the proposed framework, achieving 42-48% energy savings, maintaining comfort violations below 0.5%, and improving indoor air quality by 28-35%. The framework further achieves a generalization score of 0.91 across diverse building operational scenarios, confirming strong transferability and stability. Continuous-time dynamics learning improves predictive fidelity and ensures smooth state evolution, while adaptive reinforcement learning enables robust decision-making under dynamic environmental and occupancy variations. Scalable applicability to multi-zone building environments highlights practical deployment feasibility. 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.

AI-augmented optimization of electric machine parameters via permutation importance and dynamic sampling: a motor study

The framework is the first, to the best of the authors’ knowledge, to integrate explainable permutation importance with dynamically reweighted TPE sampling for constrained electric-machine optimization, simultaneously enhancing feasibility, performance and parameter diversity.

Thermal management optimization of Battery Electric Vehicles via hierarchical NMPC with CMO-trained Neural Models

This paper addresses the problem of energy-efficient thermal management in Battery Electric Vehicles (BEVs), with the goal of extending driving range while ensuring passenger comfort and battery thermal safety. A hierarchical Nonlinear Model Predictive Control (NMPC) framework is proposed, in which the supervisory layer explicitly balances energy consumption and cabin temperature reference tracking over a long horizon and translates this trade-off into optimal actuator command sequences for the integrated heating, ventilation, and air conditioning (HVAC) system and the battery thermal management (BTM) system. These command sequences are then enforced by a lower-layer NMPC, which introduces local corrections to compensate disturbances and modeling uncertainty while tracking an externally specified cabin temperature reference trajectory. To enable predictive control of the highly nonlinear and strongly coupled thermal dynamics, data-driven neural network models are employed as prediction models within both control layers. The proposed approach is developed and validated using a high-fidelity BEV thermal simulator developed at Politecnico di Torino in collaboration with Stellantis N.V., and benchmarked against an industrial rule-based (RB) control strategy over the WLTC driving cycle. Simulation results demonstrate a significant reduction in energy consumption compared to the baseline strategy, while satisfying comfort and battery temperature constraints.