Cleaner Energy Systems最新文献_第2页

Cleaner grid or smarter cooling? Environmental impact trade-offs of a data center using the life cycle assessment method 更清洁的电网还是更智能的冷却系统？使用生命周期评估方法的数据中心环境影响权衡

Cleaner Energy Systems

Pub Date : 2025-12-01 DOI: 10.1016/j.cles.2025.100223

Mengying Zhang , Michael Carbajales-Dale , Xiaolong Ma , Linke Guo , Chao Fan

The environmental impacts of data centers have been widely recognized and assessed through life cycle approaches. Although numerous mitigation strategies have been proposed to minimize these environmental impacts, the consequences and trade-offs introduced by implementing these strategies have not been systematically evaluated. We conduct a comprehensive life cycle assessment (LCA) to examine the environmental impacts of a representative hyperscale data center located in Virginia, United States. Using detailed inventory data from the OpenLCA software and the Ecoinvent database, we quantify greenhouse gas emissions and nine additional environmental impact categories. Our study shows that over 99 % of the data center’s total environmental impacts result from electricity consumption. Hence, an effective mitigation strategy to this challenge should focus on the efficiency of electricity use and optimizing the environmental impacts of the strategy. Unlike prior studies focusing on global warming potential (GWP) alone, this study evaluates three specific mitigation strategies that target the environmental impacts of data centers: (1) transitioning to a cleaner electricity grid in the United States, (2) enhancing power usage effectiveness (PUE), an indicator widely adopted by the industry to reflect cooling system efficiency, and (3) the combination of these two strategies. Our results reveal that supplying data centers with a cleaner electricity grid significantly reduces GWP by 28.2 %, exceeding the 20.8 % reduction achieved by improving PUE to 1.1. However, a cleaner grid simultaneously increases respiratory effects and eutrophication potential. Enhancing PUE does not bring negative impacts on environmental categories. An integrated approach, combining cleaner grid usage and enhanced PUE, has been proven to be a solution that effectively balances the highest possible reduction with 43.1 % in greenhouse gas emissions, with mitigation of respiratory effects and eutrophication potential inherent in transitioning to cleaner grids. Our study highlights the necessity of evaluating the environmental impacts of mitigation strategies and considering the complementary effects of different strategies to substantially enhance data center sustainability.

数据中心的环境影响已经通过生命周期方法得到了广泛的认识和评估。虽然提出了许多缓解战略以尽量减少这些环境影响，但实施这些战略所带来的后果和权衡尚未得到系统评价。我们进行了全面的生命周期评估（LCA），以检查位于美国弗吉尼亚州的一个具有代表性的超大规模数据中心的环境影响。利用OpenLCA软件和Ecoinvent数据库的详细清单数据，我们量化了温室气体排放和9个额外的环境影响类别。我们的研究表明，超过99%的数据中心的环境影响是由电力消耗造成的。因此，应对这一挑战的有效缓解战略应侧重于电力使用效率和优化该战略的环境影响。与以往的研究只关注全球变暖潜势（GWP）不同，本研究评估了针对数据中心环境影响的三种具体缓解策略：(1)美国向更清洁的电网过渡，(2)提高电力使用效率（PUE），这是业界广泛采用的反映冷却系统效率的指标，以及(3)这两种策略的结合。我们的研究结果显示，为数据中心提供更清洁的电网，显著降低了28.2%的GWP，超过了将PUE提高到1.1所实现的20.8%的降低。然而，更清洁的电网同时会增加呼吸效应和富营养化的可能性。提高PUE不会对环境类别产生负面影响。将使用更清洁的电网和提高PUE相结合的综合方法已被证明是一种解决方案，可以有效地平衡温室气体排放量减少43.1%的最高可能减量，同时缓解向更清洁电网过渡所固有的呼吸效应和富营养化潜力。我们的研究强调有必要评估缓解战略的环境影响，并考虑不同战略的互补效应，以大幅提高数据中心的可持续性。

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引用次数: 0

Proposal of an interleaved boost converter controlled by a nonlinear MPPT ADRC as the boost stage of a solar inverter connected to the hospital electrical network 提出了一种由非线性MPPT自抗扰控制器控制的交错升压变换器作为医院电网太阳能逆变器升压级

Cleaner Energy Systems

Pub Date : 2025-12-01 DOI: 10.1016/j.cles.2025.100222

Badr RERHRHAYE, Fathallah RERHRHAYE, Ilyas LAHLOUH, Ahmed El AKKARY, Nacer SEFIANI, Najoua MRABET, Driss KHOUILI, Yassine ENNACIRI, Chaymae EL MARTAOUI

This paper addresses the integration of renewable energy into hospital electrical networks, focusing on enhancing system reliability, efficiency, and compliance with stringent standards such as NFC15–211. Hospitals are critical infrastructures with high energy demands and require uninterrupted power for vital equipment. To address these challenges, we propose an innovative solution combining an interleaved DC-DC boost converter with a nonlinear MPPT-ADRC control strategy. The interleaved architecture reduces current and voltage ripple, improving energy conversion efficiency, while the robust ADRC control compensates for system disturbances and enhances stability under fluctuating solar irradiation. Simulation results demonstrate significant improvements in DC output quality and AC performance compared to conventional PID-GA-controlled systems, particularly under extreme operational conditions. Furthermore, the system design includes redundancy features to ensure continuous operation in fault scenarios, crucial for hospital environments. Future work will extend this control strategy to the inverter stage and explore transitioning to a microgrid architecture, aiming to enhance decentralized energy management and optimize renewable energy integration for sustainable and resilient hospital networks.

本文讨论了将可再生能源整合到医院电网中的问题，重点是提高系统的可靠性、效率，并符合NFC15-211等严格的标准。医院是能源需求高的关键基础设施，需要为重要设备提供不间断的电力。为了应对这些挑战，我们提出了一种结合交错DC-DC升压变换器和非线性MPPT-ADRC控制策略的创新解决方案。交错结构减少了电流和电压纹波，提高了能量转换效率，同时鲁棒自抗扰控制补偿了系统干扰，增强了系统在波动太阳辐照下的稳定性。仿真结果表明，与传统的pid - ga控制系统相比，直流输出质量和交流性能有了显著改善，特别是在极端操作条件下。此外，系统设计包括冗余功能，以确保在故障场景下持续运行，这对医院环境至关重要。未来的工作将把这种控制策略扩展到逆变器阶段，并探索向微电网架构的过渡，旨在加强分散的能源管理，优化可再生能源整合，以实现可持续和有弹性的医院网络。

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引用次数: 0

Sustainable hybrid thermo-hygroelectric nanogenerator from ceramic waste: A circular, low-cost solution for decentralized renewable energy 陶瓷废料的可持续混合热-水电纳米发电机：一种循环、低成本的分散式可再生能源解决方案

Cleaner Energy Systems

Pub Date : 2025-12-01 DOI: 10.1016/j.cles.2025.100225

Maria Cecília Caldeira Vieira , Ingridi dos Santos Kremer , Matheus Amancio Correa Neres , Patrícia Regina Ebani , Marcus Paulo de Oliveira , Luisa Dias Lopes , Larissa Friedrich , Arthur Batista Bromirsky , Glauber Rodrigues De Quadros , Lucas Alves Lamberti , Josué Neroti Rigue , Jocenir Boita

The transition to a low-carbon future demands sustainable, scalable, and cost-effective energy technologies that minimize environmental impact while valorizing industrial residues. This study reports the development of a hybrid thermo-hygroelectric nanogenerator integrating iron nanoparticles and red ceramic waste within a functional cementitious matrix, designed for decentralized renewable energy harvesting. The device operates via combined thermal and moisture-induced mechanisms, achieving a maximum output of 1.4 V, 23.3 μA, and 32.7 μW, corresponding to a power density of 13.08 μW/m² when stimulated with water at 98 °C. Materials were synthesized via the polyol method and characterized by XRD, XPS, and XAS, confirming the formation of mixed-valence iron oxides embedded in a silica- and calcium-rich support. Circularity is achieved through the reuse of abundant ceramic waste, while the fabrication process is low-energy and easily scalable. The device exhibited energy conversion efficiencies above 90 % and an estimated operational lifespan of 1.29–7.06 years, supporting applications in off-grid environmental sensing and low-power electronics. This work demonstrates a sustainable pathway to transform industrial residues into functional energy materials, advancing the circular economy and contributing to cleaner production practices.

向低碳未来的过渡需要可持续的、可扩展的、具有成本效益的能源技术，以最大限度地减少对环境的影响，同时使工业残留物增值。本研究报告了一种将铁纳米颗粒和红色陶瓷废料集成在功能性胶凝基质中的混合热-水电纳米发电机的开发，旨在用于分散的可再生能源收集。该器件采用热和湿相结合的工作机制，在98℃水刺激下，最大输出为1.4 V、23.3 μA和32.7 μW，对应的功率密度为13.08 μW/m²。材料通过多元醇法合成，并通过XRD， XPS和XAS进行表征，证实了在富硅和富钙载体中形成了混价氧化铁。循环是通过再利用大量的陶瓷废料来实现的，而制造过程低能耗，易于扩展。该器件的能量转换效率超过90%，预计使用寿命为1.29-7.06年，支持离网环境传感和低功耗电子产品的应用。这项工作展示了一条将工业残留物转化为功能性能源材料的可持续途径，促进了循环经济，并为清洁生产实践做出了贡献。

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引用次数: 0

Performance analysis of a hybrid power network using a multi-criteria approach (AHP-TOPSIS) 基于AHP-TOPSIS的混合电网性能分析

Cleaner Energy Systems

Pub Date : 2025-11-26 DOI: 10.1016/j.cles.2025.100224

Aristide Tolok Nelem , Yannick Antoine Abanda , Diane Tchuani Tchakonté , Mimosette Makem , Mathieu Jean Pierre Pesdjock , Pierre Ele , Ndjakomo E. Salomé

Cameroon has abundant but underutilized energy resources, with low electricity access and significant urban–rural disparities. Communities increasingly adopt hybrid systems combining conventional and renewable sources, yet intermittency affects load profiles and performance. We propose a LabVIEW-based AHP–TOPSIS framework within a predict–optimize–decide pipeline, where short-term forecasts inform evaluated alternatives for realistic rankings. The criteria—C1 (availability), C2 (demand satisfaction), C3 (CO₂ intensity), and C4 (levelized operating cost)—are weighted via normalized AHP. A local case study (PV, grid, diesel, batteries) with sensitivity analyses (OAT ±5–20 %, Dirichlet sampling) and robustness tests (demand surge, diesel stress, solar variability) shows grid preference under stress, while PV with storage is optimal under favorable irradiance. The lightweight, SCADA-deployable framework enables transparent, indicator-driven switching, enhancing reliability and cost-effectiveness.

喀麦隆拥有丰富但未充分利用的能源资源，电力普及率低，城乡差距明显。社区越来越多地采用结合传统和可再生能源的混合系统，但间歇性影响负载分布和性能。我们在预测-优化-决策管道中提出了一个基于labview的AHP-TOPSIS框架，其中短期预测通知评估现实排名的替代方案。标准c1（可用性）、C2（需求满意度）、C3（二氧化碳强度）和C4（平准化运营成本）通过归一化层次分析法进行加权。当地案例研究（光伏、电网、柴油、电池）的灵敏度分析（OAT±5 - 20%，Dirichlet抽样）和鲁棒性测试（需求激增、柴油压力、太阳能变异性）表明，在压力下电网优先，而在有利辐照度下，光伏+储能是最优的。轻量级的scada可部署框架可实现透明、指示器驱动的切换，提高可靠性和成本效益。

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引用次数: 0

Machine learning-based approach for PV energy forecasting for mono-Si, poly-Si and a-Si Grid-connected PV systems 基于机器学习的单晶硅、多晶硅和非晶硅并网光伏系统能量预测方法

Cleaner Energy Systems

Pub Date : 2025-11-08 DOI: 10.1016/j.cles.2025.100217

Abdellatif Ait-Mansour, Amine Tilioua

The growing global energy demand and the urgent need to reduce greenhouse gas emissions have intensified the search for renewable and sustainable energy sources. Among these, photovoltaic (PV) systems have emerged as a promising solution due to their long lifespan, low maintenance costs, and ability to operate under diverse climatic conditions. However, the intermittent nature of solar energy remains a major challenge for stable integration into electrical grids, especially in semi-desert regions. Despite existing research on PV performance, limited studies have focused on the comparative forecasting of different silicon-based PV technologies using advanced machine learning models in such environments. This study aims to forecast and compare the energy performance of grid-connected monocrystalline silicon, polycrystalline silicon, and amorphous silicon PV systems operating in a semi-desert region of Morocco. Using two years of real measured daily meteorological and energy production data (January 2021 to December 2022), we developed predictive models based on Random Forest and Deep Neural Networks. The models' accuracy was evaluated using multiple error metrics including mean squared error, mean absolute percentage error, mean absolute error, maximum error, and the coefficient of determination. The results demonstrate high predictive accuracy for both models, with amorphous silicon technology showing superior performance, achieving a coefficient of determination of 98.6 % for Random Forest and 98.3 %t for Deep Neural Networks. The MAPEs for amorphous silicon were 8.2 % for Random Forest and 18.7 % for Deep Neural Networks. Monocrystalline silicon achieved 98.5 % and 98.0 % for the coefficient of determination, with MAPEs of 9.3 %t and 20.4 % for Random Forest and Deep Neural Networks, respectively. For polycrystalline silicon, the coefficients of determination were 98.3 % and 98.1 %, with MAPEs of 9.1 % and 24.1 %, respectively. These findings highlight the effectiveness of machine learning models for accurate PV energy forecasting and underline the potential advantages of amorphous silicon technology in semi-desert climates

日益增长的全球能源需求和减少温室气体排放的迫切需要，促使人们加紧寻找可再生和可持续的能源。其中，光伏（PV）系统因其使用寿命长、维护成本低、能够在各种气候条件下运行而成为一种有前途的解决方案。然而，太阳能的间歇性仍然是稳定并入电网的主要挑战，特别是在半沙漠地区。尽管已有关于光伏性能的研究，但有限的研究集中在使用先进的机器学习模型在这种环境下对不同硅基光伏技术进行比较预测。本研究旨在预测和比较在摩洛哥半沙漠地区运行的并网单晶硅、多晶硅和非晶硅光伏系统的能源性能。利用两年的实际测量的每日气象和能源生产数据（2021年1月至2022年12月），我们开发了基于随机森林和深度神经网络的预测模型。采用均方误差、平均绝对百分比误差、平均绝对误差、最大误差和决定系数等多个误差指标评价模型的准确性。结果表明，两种模型的预测精度都很高，其中非晶硅技术表现出更好的性能，随机森林的决定系数为98.6%，深度神经网络的决定系数为98.3%。对于随机森林和深度神经网络，非晶硅的mape分别为8.2%和18.7%。单晶硅的确定系数达到98.5%和98.0%，随机森林和深度神经网络的mape分别为9.3%和20.4%。多晶硅的测定系数分别为98.3%和98.1%，mape分别为9.1%和24.1%。这些发现强调了机器学习模型对准确光伏能源预测的有效性，并强调了非晶硅技术在半沙漠气候下的潜在优势

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引用次数: 0

Next-day average wind speed prediction in Taiwan: A comparison of neural network and hybrid wavelet-neural network models 台湾翌日平均风速预测：神经网路与混合小波-神经网路模式之比较

Cleaner Energy Systems

Pub Date : 2025-11-07 DOI: 10.1016/j.cles.2025.100214

Seemant Tiwari, Jeeng-Min Ling

One growing renewable energy resource that is crucial to the shift to a more sustainable energy system is wind energy. One of the most significant issues affecting wind energy is variation in its production. Planning and operating a wind energy station requires the use of wind speed prediction techniques, which are challenging due to the dynamic nature of wind and the impact of regional variables. Nevertheless, current prediction techniques face substantial difficulties in achieving long-term nonlinear prediction accuracy due to the complexity of wind speed data, resulting in a deficiency of wind energy projections that may lead to erroneous energy distributions. To address the prediction issues, this research proposes a hybrid approach for average wind speed models that combines the Wavelet Transform (WT) and Neural Network (NN) techniques. The next-day forecast of time series data in Taiwan is assessed in this research using an uncertainty metric related to average wind speeds. Additionally, this study compares the deep learning-based Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Seasonal Auto-Regression Integrated Moving Average (SARIMA) approaches. A hybrid strategy that combines WT with LSTM and WT with GRU. The WT-NN model outperforms the other models according to the results of the suggested strategy. The effectiveness of the suggested WT-NN is assessed in comparison to the different models. These findings demonstrate the effectiveness of WT-NN in enhancing the accuracy of wind speed prediction. The study's suggested approach may help predict wind speed and wind energy production.

风能是一种不断增长的可再生能源，对向更可持续的能源系统转变至关重要。影响风能的最重要的问题之一是其生产的变化。规划和操作一个风能站需要使用风速预测技术，由于风的动态性和区域变量的影响，这是具有挑战性的。然而，由于风速数据的复杂性，目前的预测技术在实现长期非线性预测精度方面面临着很大的困难，导致风能预测的不足，可能导致错误的能量分布。为了解决预测问题，本研究提出了一种结合小波变换（WT）和神经网络（NN）技术的平均风速模型混合方法。本研究以平均风速为不确定度，评估台湾地区时间序列资料的次日预报。此外，本研究还比较了基于深度学习的门控循环单元（GRU）、长短期记忆（LSTM）和季节性自回归综合移动平均（SARIMA）方法。将小波变换与LSTM、小波变换与GRU相结合的混合策略。根据建议策略的结果，WT-NN模型优于其他模型。通过与不同模型的比较，评估了所建议的WT-NN的有效性。这些结果证明了WT-NN在提高风速预测精度方面的有效性。该研究建议的方法可能有助于预测风速和风能生产。

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引用次数: 0

Closed-loop workflow for short-term optimization of wind-powered reservoir management 风电水库管理短期优化的闭环工作流

Cleaner Energy Systems

Pub Date : 2025-11-07 DOI: 10.1016/j.cles.2025.100213

Mathias M. Nilsen , Rolf J. Lorentzen , Olwijn Leeuwenburgh , Andreas S. Stordal , Eduardo Barros

This paper presents a closed-loop workflow for short-term optimization of reservoir management powered by offshore wind energy. Motivated by the need to reduce CO

_{2}

emissions in the Norwegian oil and gas sector, the workflow integrates wind power forecasts into optimization of daily well control while adhering to a long-term production strategy optimized for economic output. The workflow utilizes coarsened reservoir models calibrated through ensemble-based data assimilation to minimize computational costs. A realistic benchmark model, Drogon, is used to demonstrate the methodology. The workflow dynamically adjusts well rates to align power demand with wind availability, minimizing reliance on gas turbines and thereby reducing emissions. The numerical experiment demonstrates the potential of the workflow by significantly reducing short-term emissions without compromising the NPV.

本文提出了一种用于海上风电库区管理短期优化的闭环工作流程。为了减少挪威石油和天然气行业的二氧化碳排放，该工作流程将风电预测整合到日常井控的优化中，同时坚持优化经济产出的长期生产战略。该工作流程利用基于集成的数据同化校准的粗化油藏模型，以最大限度地减少计算成本。使用一个现实的基准模型Drogon来演示该方法。该工作流程动态调整井率，使电力需求与风力可用性保持一致，最大限度地减少对燃气轮机的依赖，从而减少排放。数值实验证明了该工作流在不影响NPV的情况下显著减少短期排放的潜力。

引用次数: 0

Optimizing electricity production from food waste: A case study of Bangladesh 利用食物垃圾优化发电：以孟加拉国为例

Cleaner Energy Systems

Pub Date : 2025-11-07 DOI: 10.1016/j.cles.2025.100215

Jabed Hasan , Md Mehedi Hasan Shaikot , Roman Islam , Nusrat Chowdhury , Wahiba Yaïci , Michela Longo

Rapid urbanization in developing nations has intensified municipal solid waste (MSW) generation, posing critical challenges to sustainable urban development and energy security. This study presents a comprehensive techno-economic and environmental evaluation of an anaerobic digestion-based waste-to-energy (WtE) system tailored for Dhaka, Bangladesh-where over 72.25 % of MSW is biodegradable. A 40 MW biogas power plant was modeled using HOMER Pro software, incorporating load profiles, grid interaction, and system cost dynamics. The proposed system achieves a competitive levelized cost of electricity (LCOE) of 8.7 Tk/kWh ($0.0733), significantly outperforming conventional Independent Power Producers (14.62 Tk/kWh), rental and quick rental plants (12.53 Tk/kWh) and imported power (14.02 Tk/kWh). Annual GHG emissions were reduced to 1143,159 kg CO₂ and 1150 kg NOₓ, compared to 638,442 tons CO₂e from open dumping, as quantified using the SP1 methane emission model. These findings establish anaerobic digestion as a scalable, low-carbon alternative for urban energy systems in resource-constrained settings, aligning with circular economy and climate resilience goals.

发展中国家的快速城市化加剧了城市固体废物的产生，对城市可持续发展和能源安全构成了严峻挑战。本研究为孟加拉国达卡量身定制了一种基于厌氧消化的废物能源（WtE）系统，该系统超过72.25%的生活垃圾是可生物降解的，该研究对该系统进行了全面的技术经济和环境评估。使用HOMER Pro软件对一个40兆瓦的沼气发电厂进行了建模，包括负荷概况、电网交互和系统成本动态。拟议的系统实现了8.7塔卡/千瓦时（0.0733美元）的竞争性平准化电力成本（LCOE），显著优于传统的独立发电机组（14.62塔卡/千瓦时）、租赁和快速租赁电厂（12.53塔卡/千瓦时）和进口电力（14.02塔卡/千瓦时）。使用SP1甲烷排放模型量化的年度温室气体排放量减少到1143,159 kg CO₂和1150 kg NOₓ，而露天倾倒的CO₂排放量为638,442吨。这些研究结果表明，厌氧消化是资源受限环境下城市能源系统可扩展的低碳替代方案，符合循环经济和气候适应目标。

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引用次数: 0

Machine learning-based prediction model of wind turbine power generation 基于机器学习的风力发电预测模型

Cleaner Energy Systems

Pub Date : 2025-11-05 DOI: 10.1016/j.cles.2025.100218

Zaid Allal , Hassan N. Noura , Ola Salman , Khaled Chahine

Renewable energy sources have become central to the transition toward cleaner energy systems, with wind energy demonstrating the most rapid global growth since 1990. However, its production is inherently dependent on variable and uncontrollable factors such as weather conditions and wind dynamics. In this work, we analyze a dataset spanning two and a half years, collected from wind turbines, and apply extensive exploratory data analysis and preprocessing to enable accurate forecasting of wind power generation. Initially, the dataset was evaluated using multiple regression models for baseline predictions, while the Prophet model was employed to extract long-term trends and seasonality. The processed data were then integrated and used as input for CatBoost and Random Forest models, incorporating a windowing mechanism informed by autocorrelation and partial autocorrelation analysis to optimize temporal dependencies. Forecasting was conducted across three horizons: 15 min, 1 day, and 1 week ahead. The proposed hybrid approach achieved a root mean square error of 30.6 for 15 min forecasting, 50 for one-day forecasting, and 41 for one-week forecasting, representing at least a 50% improvement over the best standalone regression. Results further confirm the expected trend that longer forecasting horizons increase RMSE and reduce

R^{2}

, due to resampling constraints and the need for more extensive input data. Nonetheless, the hybrid methodology consistently outperformed standalone models, demonstrating stability and robustness across different horizons. By leveraging the complementary strengths of multiple regressors within a unified framework, this study highlights the potential of hybrid machine learning approaches to significantly enhance the predictive accuracy of wind energy forecasting.

可再生能源已成为向清洁能源系统过渡的核心，自1990年以来，风能已成为全球增长最快的能源。然而，它的生产本质上取决于可变和不可控的因素，如天气条件和风动力。在这项工作中，我们分析了从风力涡轮机收集的跨越两年半的数据集，并应用广泛的探索性数据分析和预处理，以实现对风力发电的准确预测。最初，使用多重回归模型对数据集进行评估，以进行基线预测，同时使用Prophet模型提取长期趋势和季节性。然后将处理后的数据整合并用作CatBoost和Random Forest模型的输入，并结合自相关和部分自相关分析的窗口机制来优化时间依赖性。预测分三个阶段进行：提前15分钟、提前1天、提前1周。所提出的混合方法在15分钟预测中实现了30.6的均方根误差，在一天预测中实现了50的均方根误差，在一周预测中实现了41的均方根误差，比最好的独立回归至少提高了50%。结果进一步证实了预期的趋势，即由于重采样限制和需要更广泛的输入数据，较长的预测范围会增加RMSE并降低R2。尽管如此，混合方法始终优于独立模型，在不同的范围内显示出稳定性和稳健性。通过利用统一框架内多个回归量的互补优势，本研究强调了混合机器学习方法在显著提高风能预测准确性方面的潜力。

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引用次数: 0

Integration of Hydrogen Production Using High Temperature Steam Electrolysis with Advanced Nuclear Energy Systems 高温蒸汽电解制氢与先进核能系统的集成

Cleaner Energy Systems

Pub Date : 2025-11-05 DOI: 10.1016/j.cles.2025.100216

Seth J. Dana, Hailei Wang

With the promise of increased economics and improved safety, advanced nuclear reactors, such as the Natrium design by TerraPower and GE Hitachi, can help many electricity energy markets transition to carbon-free power smoothly. Operating at higher temperatures, the Natrium design based on a sodium fast reactor is suitable for co-located hydrogen production using high temperature steam electrolysis. This study models and analyzes three Natrium integrated energy systems with thermal energy storage and co-located hydrogen production. The first two configurations focus on improving thermal efficiency of the reheat Rankine cycle used in the Natrium design, while the final configuration improves hydrogen production efficiency. Results indicate that coupling the Natrium system with hydrogen production can boost its energy efficiency by 1%, and using low grade steam directly from the Natrium steam cycle for hydrogen production significantly reduces system complexity and increases the overall system efficiency by 3%.

随着经济效益的提高和安全性的提高，先进的核反应堆，如泰拉能源公司和通用日立公司设计的Natrium，可以帮助许多电力能源市场顺利过渡到无碳电力。基于钠快堆的Natrium设计在更高温度下运行，适用于使用高温蒸汽电解的同地制氢。本研究对三个Natrium集成能源系统进行了建模和分析，该系统具有储热和共置制氢功能。前两种配置侧重于提高Natrium设计中使用的再热朗肯循环的热效率，而最后一种配置则提高了制氢效率。结果表明，将Natrium系统与制氢相结合可使其能源效率提高1%，而直接使用Natrium蒸汽循环中的低品位蒸汽进行制氢可显著降低系统复杂性，并使整体系统效率提高3%。

引用次数: 0