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

A deep learning based predictive control method for enhancing microgrid resilience

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

An INRBO-SSA-LSTM Hybrid Framework for Short-Term Power Load Forecasting in Smart Microgrids

Accurate power load forecasting is critical for the efficient operation of industrial microgrids. However, raw meteorological and consumption data typically exhibit non-stationary characteristics, complicating the hyperparameter tuning of deep learning models, and subsequently degrading the prediction accuracy of these frameworks. To address the aforementioned challenges, a new hierarchical forecasting structure denoted as INRBO-SSA-LSTM is proposed in this paper. First, Pearson correlation analysis is employed for feature reduction, identifying the four main factors to mitigate the dimensionality curse. Building upon this foundation, a refined Newton-Raphson-Based Optimizer (INRBO) is introduced, integrating a cosine adaptive t-distribution perturbation, a boundary-aware non-uniform steering scheme, and a fitness-aware hybrid perturbation mechanism. Evaluated against the CEC2022 benchmark suite, comprehensive evaluations reveal that the INRBO demonstrates superior global exploration and local refinement capabilities compared to baseline algorithms when assessed on the CEC2022 benchmark suite for foundational optimization performance. Furthermore, rigorous testing on the CEC2017 suite across 10, 30, and 50 dimensions successfully validates its exceptional robustness and search capabilities in high-dimensional spaces. INRBO functions as a dual-stage optimizer within the proposed framework; in the initial phase, it dynamically calibrates the parameters of Singular Spectrum Analysis (SSA) to extract deterministic load patterns, achieving a maximum signal-to-noise ratio of 15.87 dB; in the second phase, it optimizes the global hyperparameters of the Long Short-Term Memory (LSTM) network. Validated using actual industrial microgrid data in Jiangsu Province, China, the proposed method significantly outperforms traditional baseline models across all indicators; specifically, the prediction error (RMSE = 10.9764, MAPE = 3.7866%) is substantially minimized, and the coefficient of determination (R2 = 0.9741) is highly optimal. This adaptable framework effectively accommodates temporal demand variations, offering a robust foundation for the advancement of intelligent power management technology.

Grid-Interactive Hyperscale Data Centers: Deep Reinforcement Learning for Joint Workload–Cooling Scheduling to Enable Demand Response and Renewable Integration

Driven by artificial intelligence and cloud computing, hyperscale data centers are becoming one of the fastest-growing electrical loads worldwide and are increasingly recognized as a new class of flexible loads capable of supporting demand response (DR) and the integration of variable renewable energy (VRE). However, their two principal control levers—IT workload scheduling and cooling system operation—have traditionally been managed in a decoupled manner, leaving both energy efficiency and demand-side flexibility under-exploited. This paper proposes a deep reinforcement learning (DRL) framework that jointly co-schedules computing and thermal resources so that a hyperscale data center can operate as a grid-interactive flexible load. We formulate the joint problem as a constrained Markov Decision Process and develop an actor-critic algorithm combining Deep Deterministic Policy Gradient with a safety shield mechanism to guarantee thermal constraint satisfaction during both training and deployment. A high-fidelity digital twin simulation environment enables safe Sim-to-Real training. Extensive experiments demonstrate that the proposed approach reduces total electricity consumption by 18-25% compared to baseline controllers, cuts thermal violations by over 90%, and maintains service level agreement compliance, while broadening the controllable power envelope of the facility to provide a technical basis for participating in DR programs and aligning data-center power profiles with renewable generation. The framework bridges IT-side and facility-side control and supports the evolution of hyperscale data centers from passive electricity consumers toward active, grid-interactive participants in renewable-penetrated power systems.

Intelligent Energy Forecasting Framework Using Deep Neural Networks for Renewable Power Systems

Sound prediction of existence of future generation based on stochastic sources is a critical issue because of high nonlinearities, high transitions in the environment, and the nature of inherent uncertainty in observational information. The paper proposes an integrated architecture of deep learning, which is the Hierarchical Regime-Adaptive Probabilistic Network (HRAPN) that aims to overcome these constraints using an end-to-end learning framework. The solution selection boasts of hierarchical representation induction with a latent regime adaptation mechanism that modulates dynamically the internal model behavior in a non-stationary environment. Besides that, attention guided dependency synthesis module performs informative temporal context aggregation selectively to allow an efficient long horizon modeling without impaired performance. In contrast to the more traditional deterministic approaches, HRAPN uses probabilistic model of output in order to explicitly model predictive uncertainty, which enhances robustness and reliability of the estimated decisions. The system takes directly heterogeneous and multivariate data, without explicit features or domain pre-treatment. It is experimentally assessed that the presented method achieves higher results as compared to existing baselines in accuracy, stability, and uncertainty calibration in various forecast periods. The findings validate the performance of regime perceiving, hierarchical abstraction and probabilistic inference in the same learning process. The proposed HRAPN model offers a scaffoldable and adaptable evaluation of the dynamic generation modeling under both variable operating conditions with data. The suggested framework attains an overall accuracy of 96.6%, illustrating its robust prediction reliability and exceptional performance relative to current methodologies.

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

To solve the generalization bottleneck and environmental perception limitation of deep reinforcement learning (DRL) in the charge-sustaining (CS) stage of hybrid electric vehicles, this paper proposes a novel adaptive hierarchical energy management strategy, which combines real-world data enhancement and multi-scale perception. Aiming at the problems of over-fitting and short-sighted decision-making commonly existing in traditional strategies, this study constructed a fresh enhanced training set covering all-round driving cycles based on real driving data, thus breaking through the training restrictions brought by standard driving cycles. In addition, the historical average speed window is introduced as the enhanced state, which enables the strategy to capture the macro traffic flow trend. In terms of control architecture, a bi-level coupled mechanism based on soft actor-critic (SAC) and equivalent consumption minimization strategy (ECMS) is designed. Experimental results indicate that across multi-type driving cycle tests, the final SOC deviation of the agent based on the augmented training set is maintained within ±2%. Under identical operating conditions, the augmented agent incorporating a 30 s average velocity observation achieves a 43.55% reduction in the standard deviation of SOC fluctuations compared to its counterpart without such observation. While ensuring battery SOC robustness, the proposed strategy improves fuel economy by 5.2% on average across various driving cycles compared to adaptive ECMS (AECMS).