End-to-End Learning-based Operation of Integrated Energy Systems for Buildings and Data Centers

• URL: http://arxiv.org/abs/2604.14184v1
• Date: Tue, 31 Mar 2026 12:57:36 GMT
• Title: End-to-End Learning-based Operation of Integrated Energy Systems for Buildings and Data Centers
• Abstract summary: This paper investigates integrated energy system (IES) for coordinated multi-energy supply of buildings and data centers.<n>An end-to-end learning-based method is proposed for the operational optimization of IES under uncertainty.<n>Case studies show that the proposed methods improves the operational performance of IES by about 7-9% compared to existing predict-then-optimize methods.
• Score: 19.662418818327545
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Buildings and data centers (DCs) are energy-intensive sectors, playing a critical role to achieve the low-carbon and sustainable energy transition targets. To this end, integrated energy system (IES) that incorporates diverse renewables, energy generation, conversion, and storage technologies to enable coordinated multi-energy supply have been widely investigated for both buildings and DCs. However, few works consider the two sectors jointly within IES to exploit their substantial synergistic benefits. Meanwhile, the operational optimization of IES remains challenging due to the difficulty to predict the multi-energy demand and supply accurately. To address these gaps, this paper investigates IES for coordinated multi-energy supply of buildings and DC, where the waste heat from DCs is recovered and reused to enhance energy efficiency. Moreover, an end-to-end learning-based method is proposed for the operational optimization of IES under uncertainty. Unlike conventional predict-then-optimize approaches, the proposed method integrates the training of prediction models for uncertain variables with the constrained optimization of IES into a unified learning framework, guiding the training of prediction models to improve operational performance, rather than prediction accuracy, thereby mitigating the impacts of predictions errors. Case studies based on real-world datasets show that the proposed methods improves the operational performance of IES by about 7-9% compared to existing predict-then-optimize methods. In addition, coordinating buildings and DCs within IES shows substantial economic benefits. In particular, the waste heat recovery from DCs leads to approximately 10% of total energy cost reduction of the IES.

Related papers

• Direct Advantage Estimation for Scalable and Sample-efficient Deep Reinforcement Learning [54.916555668983726]
  We introduce discrete latent dynamics models that efficiently approximate transition probabilities.<n>We find that DAE scales effectively with function approximator capacity while retaining high sample efficiency.
  arXiv  Detail & Related papers  (2026-06-18T15:58:48Z)
• On Electric Vehicle Energy Demand Forecasting and the Effect of Federated Learning [1.2599533416395765]
  Energy Demand Forecasting of Electric Vehicle Supply Equipments (EVSEs) is one of the most critical operations for ensuring efficient energy management and sustainability.<n>It enables utility providers to anticipate energy/power demand, optimize resource allocation, and implement proactive measures to improve grid reliability.<n>As concerns and restrictions about privacy and sustainability have grown, training data has become increasingly fragmented.
  arXiv  Detail & Related papers  (2026-02-24T11:21:45Z)
• Joint Resource Management for Energy-efficient UAV-assisted SWIPT-MEC: A Deep Reinforcement Learning Approach [50.52139512096988]
  6G Internet of Things (IoT) networks face challenges in remote areas and disaster scenarios where ground infrastructure is unavailable.<n>This paper proposes a novel aerial unmanned vehicle (UAV)-assisted computing (MEC) system enhanced by directional antennas to provide both computational and energy support for ground edge terminals.
  arXiv  Detail & Related papers  (2025-05-06T06:46:19Z)
• Explainable AI for building energy retrofitting under data scarcity [40.14307808809578]
  This study presents an Artificial Intelligence (AI) and Machine Learning (ML)-based framework to recommend energy efficiency measures for residential buildings.<n>Using Latvia as a case study, the methodology addresses challenges associated with limited datasets, class imbalance and data scarcity.<n>The evaluation of the approach shows that it notably overcomes data limitations, achieving improvements up to 54% in precision, recall and F1 score.
  arXiv  Detail & Related papers  (2025-04-08T14:00:08Z)
• Global-Decision-Focused Neural ODEs for Proactive Grid Resilience Management [50.34345101758248]
  We propose predict-all-then-optimize-globally (PATOG), a framework that integrates outage prediction with globally optimized interventions.<n>Our approach ensures spatially and temporally coherent decision-making, improving both predictive accuracy and operational efficiency.<n>Experiments on synthetic and real-world datasets demonstrate significant improvements in outage prediction consistency and grid resilience.
  arXiv  Detail & Related papers  (2025-02-25T16:15:35Z)
• Synergistic Development of Perovskite Memristors and Algorithms for Robust Analog Computing [53.77822620185878]
  We propose a synergistic methodology to concurrently optimize perovskite memristor fabrication and develop robust analog DNNs.<n>We develop "BayesMulti", a training strategy utilizing BO-guided noise injection to improve the resistance of analog DNNs to memristor imperfections.<n>Our integrated approach enables use of analog computing in much deeper and wider networks, achieving up to 100-fold improvements.
  arXiv  Detail & Related papers  (2024-12-03T19:20:08Z)
• TranDRL: A Transformer-Driven Deep Reinforcement Learning Enabled Prescriptive Maintenance Framework [58.474610046294856]
  Industrial systems demand reliable predictive maintenance strategies to enhance operational efficiency and reduce downtime. This paper introduces an integrated framework that leverages the capabilities of the Transformer model-based neural networks and deep reinforcement learning (DRL) algorithms to optimize system maintenance actions.
  arXiv  Detail & Related papers  (2023-09-29T02:27:54Z)
• Sustainable AIGC Workload Scheduling of Geo-Distributed Data Centers: A Multi-Agent Reinforcement Learning Approach [48.18355658448509]
  Recent breakthroughs in generative artificial intelligence have triggered a surge in demand for machine learning training, which poses significant cost burdens and environmental challenges due to its substantial energy consumption. Scheduling training jobs among geographically distributed cloud data centers unveils the opportunity to optimize the usage of computing capacity powered by inexpensive and low-carbon energy. We propose an algorithm based on multi-agent reinforcement learning and actor-critic methods to learn the optimal collaborative scheduling strategy through interacting with a cloud system built with real-life workload patterns, energy prices, and carbon intensities.
  arXiv  Detail & Related papers  (2023-04-17T02:12:30Z)
• Predict+Optimize Problem in Renewable Energy Scheduling [31.032838966665828]
  This paper benchmarks solutions from the IEEE-CIS Technical Challenge on Predict+ for Renewable Energy Scheduling.<n>The top-ranked method employed optimization using LightGBM ensembles achieved at least a 2% reduction in energy costs.<n>The novelty of this work lies in its comprehensive evaluation of Predict+ methodologies applied to a real-world renewable energy scheduling problem.
  arXiv  Detail & Related papers  (2022-12-21T02:34:12Z)
• Optimal Planning of Hybrid Energy Storage Systems using Curtailed Renewable Energy through Deep Reinforcement Learning [0.0]
  We propose a sophisticated deep reinforcement learning (DRL) methodology with a policy-based algorithm to plan energy storage systems (ESS) A quantitative performance comparison proved that the DRL agent outperforms the scenario-based optimization (SO) algorithm. The corresponding results confirmed that the DRL agent learns the way like what a human expert would do, suggesting reliable application of the proposed methodology.
  arXiv  Detail & Related papers  (2022-12-12T02:24:50Z)
• Digital Twins based Day-ahead Integrated Energy System Scheduling under Load and Renewable Energy Uncertainties [14.946548030861866]
  Digital twins (DT) of an integrated energy system (IES) can improve coordinations among various energy converters. Case studies show that the proposed DT-based method is able to reduce the operating cost of IES by 63.5%.
  arXiv  Detail & Related papers  (2021-09-29T13:58:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.