Energy and AI最新文献_第9页

Surrogate-driven design optimization with uncertainty constraints in Monte Carlo simulations 蒙特卡罗仿真中不确定性约束下的代理驱动设计优化

IF 9.6

Energy and AI Pub Date : 2025-12-01 Epub Date: 2025-11-28 DOI: 10.1016/j.egyai.2025.100655

Omer F. Erdem , David P. Broughton , Josef Svoboda , Chengkun Huang , Majdi I. Radaideh

{"title":"Surrogate-driven design optimization with uncertainty constraints in Monte Carlo simulations","authors":"Omer F. Erdem , David P. Broughton , Josef Svoboda , Chengkun Huang , Majdi I. Radaideh","doi":"10.1016/j.egyai.2025.100655","DOIUrl":"10.1016/j.egyai.2025.100655","url":null,"abstract":"<div><div>In multi-objective design tasks, the computational cost increases rapidly when high-fidelity simulations are used to evaluate objective functions. Surrogate models help mitigate this cost by approximating the simulation output, simplifying the design process. However, under high uncertainty, surrogate models trained on noisy data can produce inaccurate predictions, as their performance depends heavily on the quality of training data. This study investigates the impact of data uncertainty on two multi-objective design problems modeled using Monte Carlo transport simulations: a neutron moderator and an ion-to-neutron converter. For each, a grid search was performed using five different tally uncertainty levels to generate training data for neural network surrogate models. These models were then optimized using Non-dominated Sorting Genetic Algorithm (NSGA-III). The recovered Pareto-fronts were analyzed across uncertainty levels: in the moderator problem, normalized hypervolume dropped from 0.886 at 1.0% uncertainty to 0.748 at 10% uncertainty, while in the converter problem it remained near 0.50 for all cases. Average simulation times were also compared to evaluate the trade-off between accuracy and computational cost. Results show that the influence of simulation uncertainty is strongly problem-dependent. In the neutron moderator case, higher uncertainties led to exaggerated objective sensitivities and distorted Pareto-fronts, reducing normalized hypervolume. In contrast, the ion-to-neutron converter task was less affected—low-fidelity simulations produced results similar to those from high-fidelity data. These findings suggest that a fixed-fidelity approach is not optimal. Surrogate models can recover the Pareto-front under noisy conditions, and multi-fidelity studies help identify suitable uncertainty levels for each problem to balance efficiency and accuracy.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100655"},"PeriodicalIF":9.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145736581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine learning-guided optimization of high-performance porous composite membranes for alkaline water electrolysis 碱水电解用高性能多孔复合膜的机器学习优化

IF 9.6

Energy and AI Pub Date : 2025-12-01 Epub Date: 2025-11-27 DOI: 10.1016/j.egyai.2025.100657

Xinyang Zhao , Zhen Geng , Sheng Guo , Hao Cai , Qihan Xia , Min Liu , Xuesong Zhang , Liming Jin , Cunman Zhang

{"title":"Machine learning-guided optimization of high-performance porous composite membranes for alkaline water electrolysis","authors":"Xinyang Zhao , Zhen Geng , Sheng Guo , Hao Cai , Qihan Xia , Min Liu , Xuesong Zhang , Liming Jin , Cunman Zhang","doi":"10.1016/j.egyai.2025.100657","DOIUrl":"10.1016/j.egyai.2025.100657","url":null,"abstract":"<div><div>The production of green hydrogen via alkaline water electrolysis necessitates porous composite membranes with high ionic conductivity and high bubble-point pressure. However, the mainstream preparation process of porous composite membranes involves many parameters, rendering this a complex high-dimensional optimization problem. Traditional trial-and-error experimentation is inefficient and often fails to explore the performance boundaries. In this study, an XGBoost -based machine learning model is developed and trained with laboratory-collected datasets, achieving satisfactory predictive performance. The model provides critical insights into the relationships between six manufacturing parameters and the two core performance parameters of the membrane. Subsequently, prediction based on coarse-grained grid and reverse search are performed on the model to identify optimal parameter regions, followed by manual refinement through feature analysis. This integrated approach ultimately identifies three high-performance composite membrane candidates, which are experimentally validated. This work demonstrates a highly efficient and accurate machine learning-driven paradigm for the development of advanced porous composite membrane in alkaline water electrolysis.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100657"},"PeriodicalIF":9.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145680994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A hybrid monthly electricity demand forecasting model combining an Hodrick-Prescott filter, recurrent neural networks, and autoregressive integrated moving average 结合Hodrick-Prescott滤波、递归神经网络和自回归综合移动平均的混合月电力需求预测模型

IF 9.6

Energy and AI Pub Date : 2025-12-01 Epub Date: 2025-08-27 DOI: 10.1016/j.egyai.2025.100600

Zhenyu Su, Juan Zhang, Zhehan Yang, Leihao Ma

{"title":"A hybrid monthly electricity demand forecasting model combining an Hodrick-Prescott filter, recurrent neural networks, and autoregressive integrated moving average","authors":"Zhenyu Su, Juan Zhang, Zhehan Yang, Leihao Ma","doi":"10.1016/j.egyai.2025.100600","DOIUrl":"10.1016/j.egyai.2025.100600","url":null,"abstract":"<div><div>The coexistence of growth trends and seasonal fluctuations in monthly electricity demand presents significant forecasting challenges. Therefore, this study proposes a univariate time series forecasting approach that applies the Hodrick-Prescott (HP) filter to decompose the demand series into trend and seasonal components. Autoregressive integrated moving average (ARIMA) is used to forecast the trend, while recurrent neural networks (RNNs) handle the periodic component. The final prediction is obtained by combining the forecasts of both components. The model’s predictive performance is evaluated using Guangzhou’s total electricity consumption data. Compared to traditional methods such as Holt-Winters, Seasonal ARIMA, and error-trend-seasonal (ETS), the proposed HP_RNN_ARIMA hybrid model reduces mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE) by approximately 9.70 % to 35.66 %, 14.18 % to 35.06 %, and 20.01 % to 41.92 %, respectively. Compared to standalone neural networks such as backpropagation (BP), RNNs, and long short-term memory (LSTM), the proposed model lowers MAPE, RMSE, and MAE by approximately 9.05 % to 44.02 %, 20.88 % to 51.74 %, and 29.53 % to 56.23 %, respectively. Against other hybrid models, it reduces these metrics by 3.60 % to 33.39 %, 4.27 % to 36.67 %, and 4.43 % to 44.87 %. It also achieves the highest Willmott’s index (WI) and Legates and McCabe’s index (LMI) scores, reflecting superior model fit. Moreover, applying the HP filter for decomposition and modeling each component individually significantly improves forecasting accuracy.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100600"},"PeriodicalIF":9.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144919974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predictive maintenance for wind turbines: A physics-driven reinforcement learning strategy with economic-reliability collaborative optimization 风力涡轮机的预测性维护：具有经济可靠性协同优化的物理驱动强化学习策略

IF 9.6

Energy and AI Pub Date : 2025-12-01 Epub Date: 2025-09-13 DOI: 10.1016/j.egyai.2025.100620

Jianghao Zhu , Wei Chen , Le Su , Bin Lan , Tingting Pei , Long Jin

{"title":"Predictive maintenance for wind turbines: A physics-driven reinforcement learning strategy with economic-reliability collaborative optimization","authors":"Jianghao Zhu , Wei Chen , Le Su , Bin Lan , Tingting Pei , Long Jin","doi":"10.1016/j.egyai.2025.100620","DOIUrl":"10.1016/j.egyai.2025.100620","url":null,"abstract":"<div><div>Wind turbine maintenance optimization faces challenges in balancing economic efficiency with operational reliability under environmental uncertainty. Traditional maintenance approaches exhibit limitations in adaptive decision-making, leading to increased operational costs and reliability risks. This study develops a physics-informed reinforcement learning framework that integrates established domain knowledge with adaptive decision algorithms. The approach embeds physical principles—including Weibull wind dynamics and multi-stage degradation models—into a reinforcement learning architecture, while introducing bidirectional temperature-degradation coupling for enhanced failure prediction. A high-fidelity simulation environment enables policy training through Proximal Policy Optimization, capturing complex interactions between environmental variability and equipment deterioration. The framework was validated through case study implementation using northern China wind farm operational data. Results demonstrate zero-failure operation over simulated 19-year lifecycles, with economic performance improvements of 109.3 % and 54.5 % compared to conventional periodic and threshold-based maintenance strategies. By integrating physical constraints with intelligent algorithms, the method achieves adaptive maintenance decisions based on multi-dimensional state information.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100620"},"PeriodicalIF":9.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

District heating optimization in residential buildings using reinforcement learning with adaptive context-aware predictive environment 基于自适应情景感知预测环境的强化学习的住宅区域供热优化

IF 9.6

Energy and AI Pub Date : 2025-12-01 Epub Date: 2025-09-03 DOI: 10.1016/j.egyai.2025.100603

Sai Sushanth Varma Kalidindi , Hadi Banaee , Hans Karlsson , Amy Loutfi

{"title":"District heating optimization in residential buildings using reinforcement learning with adaptive context-aware predictive environment","authors":"Sai Sushanth Varma Kalidindi , Hadi Banaee , Hans Karlsson , Amy Loutfi","doi":"10.1016/j.egyai.2025.100603","DOIUrl":"10.1016/j.egyai.2025.100603","url":null,"abstract":"<div><div>As district heating networks evolve to meet climate-neutral objectives, optimizing their control under heterogeneous building characteristics and dynamic environmental conditions remains a significant challenge. Traditional control strategies often lack the adaptability necessary to account for building-specific dynamics and to ensure real-time adherence to operational safety constraints. In this work, we present an integrated machine learning-based framework that combines an adaptive context-aware transformer model with deep reinforcement learning to address these limitations. The proposed approach introduces an adaptive context-aware transformer as a predictive environment within a Deep Q-Network (DQN) framework, enabling data-driven, building-specific control of district heating systems. Utilizing real-world data from 148 residential buildings across Sweden and Finland, the model incorporates contextual embeddings and temporal features to predict indoor temperature trajectories with high accuracy, achieving root mean square error values between 0.18–0.24 °C for Swedish buildings and 0.26–0.32 °C for Finnish buildings. The DQN agent leverages these predictions to optimize heating control while ensuring compliance with operational safety limits and preserving occupant comfort. Experimental results demonstrate significant energy savings, with mid-rise buildings achieving up to 14.85% reduction in energy consumption, and peak seasonal savings exceeding 20% during spring months. This integrated approach illustrates the potential for substantial energy optimization and reliable indoor climate management in future district heating networks.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100603"},"PeriodicalIF":9.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive smoothing drift normalization for day-ahead net load forecasting in renewable power system 可再生能源系统日前净负荷预测的自适应平滑漂移归一化

IF 9.6

Energy and AI Pub Date : 2025-12-01 Epub Date: 2025-10-27 DOI: 10.1016/j.egyai.2025.100633

Bizhi Wu , Shanlin Wang

{"title":"Adaptive smoothing drift normalization for day-ahead net load forecasting in renewable power system","authors":"Bizhi Wu , Shanlin Wang","doi":"10.1016/j.egyai.2025.100633","DOIUrl":"10.1016/j.egyai.2025.100633","url":null,"abstract":"<div><div>The increasing integration of renewable energy sources introduces significant variability and non-stationarity into power system, challenging accurate net load forecasting. Although net load forecasting research has devoted considerable efforts to handle non-stationarity – via normalization, incremental learning, or drift detection – existing solutions often suffer from hyperparameter tuning, threshold-based triggers, or reliance on specialized architectures. To overcome these limitations, we propose Adaptive Smoothing Drift Normalization (ASDN), a lightweight normalization layer that continuously adapts to distribution shifts without threshold tuning. ASDN effectively adapts to new data via a mechanism that combines entropy-based adjustments with a dynamic filtering approach. At the same time, it maintains stability with respect to historical patterns, allowing the method to capture both gradual and abrupt shifts in the data distribution. We provide a theoretical guarantee that the estimation error of ASDN remains bounded under piecewise-stationary drift; as incremental drift and noise decrease, this bound tightens and converges to zero. Experiments on nine forecasting models across five public datasets and four prediction horizons show that ASDN consistently outperforms traditional normalization techniques, reducing mean squared error and enhancing robustness. These results confirm ASDN’s effectiveness in handling complex temporal dynamics, making it valuable for improving forecast accuracy in dynamic renewable power systems.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100633"},"PeriodicalIF":9.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A comparative analysis of regression algorithms and a real world application of multivariable energy signatures 回归算法的比较分析和多变量能量特征的实际应用

IF 9.6

Energy and AI Pub Date : 2025-12-01 Epub Date: 2025-10-29 DOI: 10.1016/j.egyai.2025.100641

Simone Eiraudo , Daniele Salvatore Schiera , Luca Barbierato , Alena Trifirò , Lorenzo Bottaccioli , Andrea Lanzini

{"title":"A comparative analysis of regression algorithms and a real world application of multivariable energy signatures","authors":"Simone Eiraudo , Daniele Salvatore Schiera , Luca Barbierato , Alena Trifirò , Lorenzo Bottaccioli , Andrea Lanzini","doi":"10.1016/j.egyai.2025.100641","DOIUrl":"10.1016/j.egyai.2025.100641","url":null,"abstract":"<div><div>An ecosystem of energy models of buildings is needed to boost the retrofitting process to improve energy efficiency and meet sustainability goals. Such models should enhance the understanding of the energy behavior of a building, the impact of the external variables, and the causes of inefficiencies. Energy Signatures can fill this role, with particular regard to the consumption due to air conditioning. Univariate models, neglecting the impact of solar radiation, have been widely adopted for Energy Signature analysis. This paper presents Multivariable Energy Signatures considering outdoor temperature and solar radiation. The application on a real-world dataset of multivariable non-parametric approaches stands out from previous works in the ES sector. This led to a mean improvement of 0.768 to 0.804 of the coefficients of determination calculated over 103 real-world case studies. Moreover, Neural Networks outperformed several literature algorithms regarding accuracy, robustness, and scalability. The paper also discusses issues regarding the time resolution of input data and introduces appropriate visualization tools to employ Multivariable Energy Signatures as diagnostic tools.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100641"},"PeriodicalIF":9.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluating activation strategies and their stability on PEM water electrolyzers using machine learning 利用机器学习评估PEM水电解槽的激活策略及其稳定性

IF 9.6

Energy and AI Pub Date : 2025-12-01 Epub Date: 2025-10-04 DOI: 10.1016/j.egyai.2025.100623

K. Ashoke Raman , Niklas L. Wolf , Ali Javed , Violeta Karyofylli , Hans Kungl , André Karl , Eva Jodat , Rüdiger-A. Eichel

{"title":"Evaluating activation strategies and their stability on PEM water electrolyzers using machine learning","authors":"K. Ashoke Raman , Niklas L. Wolf , Ali Javed , Violeta Karyofylli , Hans Kungl , André Karl , Eva Jodat , Rüdiger-A. Eichel","doi":"10.1016/j.egyai.2025.100623","DOIUrl":"10.1016/j.egyai.2025.100623","url":null,"abstract":"<div><div>Pre-treatment of the proton exchange membrane water electrolyzers is a crucial procedure performed prior to its regular operation. These procedures help in catalyst activation and membrane saturation, thereby, ensuring its optimal performance. In this study, we use machine learning to investigate the impact of three distinct activation procedures on the cell performance and stability. The data set necessary to develop the surrogate models was obtained from a lab scale PEM electrolyzer cell. After evaluating the performance of the three tested models and validating them with experimental data, extreme gradient boosting is selected as the to perform parametric analysis. The modeling predictions reveal that the activation procedures mainly impact the ohmic resistance at the beginning of the cell life. These observations were further corroborated using through sensitivity analysis performed through an explainable artificial intelligence technique. Furthermore, data-driven time-series forecasting analysis to predict cell stability for different activation procedures showed a good comparison between experimental data and model predictions.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100623"},"PeriodicalIF":9.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Stress-informed transfer learning: Accelerated battery life evaluation model across diverse operating conditions and cell mechanisms 应力信息迁移学习：跨不同操作条件和电池机制的加速电池寿命评估模型

IF 9.6

Energy and AI Pub Date : 2025-12-01 Epub Date: 2025-10-05 DOI: 10.1016/j.egyai.2025.100629

Guangyuan Zeng , Yuhang Du , Yuchen Song , Datong Liu , Yu Peng

{"title":"Stress-informed transfer learning: Accelerated battery life evaluation model across diverse operating conditions and cell mechanisms","authors":"Guangyuan Zeng , Yuhang Du , Yuchen Song , Datong Liu , Yu Peng","doi":"10.1016/j.egyai.2025.100629","DOIUrl":"10.1016/j.egyai.2025.100629","url":null,"abstract":"<div><div>Life evaluation for newly developed lithium-ion batteries is often constrained by the time-intensive and costly nature of battery testing. This is particularly true in the aerospace industry, where limited comprehensive data availability significantly hampers life evaluations. Data collected from batteries under diverse operating conditions and cell mechanisms provides valuable insights for constructing degradation models. Nevertheless, the nonlinearity in battery degradation across operating conditions, combined with data distribution discrepancies among different cell mechanisms, presents significant challenges in developing degradation models for newly designed batteries. In this study, a stress-informed transfer learning methodology is proposed to accelerate the life evaluation process. Firstly, a stochastic model is employed to capture the nonlinear dynamics inherent in battery degradation under diverse operating conditions. Model migration is implemented to adapt stochastic models to unique degradation trends, ensuring precision under varying stresses. Secondly, a Transformer-based model is developed to accommodate variations in data distributions across different cell mechanisms. Domain-adaptive fine-tuning with specified loss function is then incorporated to address the challenge of limited target degradation features. Finally, a hybrid model is devised by integrating these foundational components, realizing accelerated life evaluation through the utilization of multi-modal data. Experimental results demonstrate that the proposed methodology achieves improvements of 63.40 % in MAE and 58.55 % in RMSE with 30 % training data length compared to mainstream benchmark methods. This highlights the method’s potential as an early-stage screening and assessment tool for newly developed space lithium-ion batteries, complementing conventional cycle life evaluation protocols with accelerated evaluations from limited degradation data.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100629"},"PeriodicalIF":9.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intelligent coalbed methane drainage optimization: A deep reinforcement learning-driven life-cycle strategy 智能煤层气抽放优化：深度强化学习驱动的生命周期策略

IF 9.6

Energy and AI Pub Date : 2025-12-01 Epub Date: 2025-08-27 DOI: 10.1016/j.egyai.2025.100598

Chen Liu , Bin Gong , HaoQiang Wu , Hu Huang , Heng Zhao

{"title":"Intelligent coalbed methane drainage optimization: A deep reinforcement learning-driven life-cycle strategy","authors":"Chen Liu , Bin Gong , HaoQiang Wu , Hu Huang , Heng Zhao","doi":"10.1016/j.egyai.2025.100598","DOIUrl":"10.1016/j.egyai.2025.100598","url":null,"abstract":"<div><div>Coalbed methane (CBM) production, as a significant portion of unconventional natural gas development, plays a crucial role in enhancing output and economic benefits through the optimization of its life-cycle drainage strategy. Traditional drainage strategies rely on experience and trial-and-error methods, making it difficult to adapt to complex and dynamic production environments. This study proposes a life-cycle production and drainage optimization strategy for CBM based on Deep Reinforcement Learning (DRL). Utilizing the Deep Q-Network (DQN) algorithm, this work learns and optimizes the drainage strategy during the production process, achieving intelligent decision-making for drainage operations. An auto-regressive surrogate model is introduced to build a reinforcement learning environment for gas production and drainage, based on a deep learning model (CNN-LSTM). This model substitutes the full-physics simulation model that requires high computational cost, significantly accelerating the interactive learning process between the agent and the environment in DRL. Whether to set the gas production or Net Present Value (NPV) as reward, two strategies for reinforcement learning were considered accordingly. The results concluded that the DRL drainage strategy with NPV as the reward increased the net gain by 5.83 % compared to historical data. Compared with traditional methods, this approach significantly improves the NPV and optimizes the drainage strategy. The findings demonstrate that the life-cycle drainage optimization method for CBM based on DRL is not only efficient and feasible but also provides an intelligent solution for the development of unconventional natural gas resources. The results highlight the method's strong adaptability and potential for addressing complex optimization problems in dynamic production environments.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"22 ","pages":"Article 100598"},"PeriodicalIF":9.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144919975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0