Solar Energy最新文献_第3页

Data-driven models of a solar field used to power membrane distillation systems: A comparison study

IF 6 2区工程技术 Q2 ENERGY & FUELS

Solar Energy

Pub Date : 2025-02-25 DOI: 10.1016/j.solener.2025.113349

A. Bueso , J.D. Gil , G. Zaragoza

The pressing issue of water scarcity has led to increased research focussing on enhancing access to fresh water, with sustainable desalination emerging as a prominent solution. The use of solar energy is often proposed because of the geographical coincidence of high solar irradiance and water scarcity. However, the variability of the energy source in a stationary-designed process such as desalination must be addressed, and modelling solar desalination systems is crucial to understanding the dynamics and optimising the performance. Solar thermal energy is cheaper to store than photovoltaic energy, and powers advanced desalination technologies such as membrane distillation (MD) that can reach higher water recovery. This study investigates the application of data-driven modelling techniques to an innovative solar collector field providing heat for a MD system. The novelty of using mirrors in the solar field to boost the thermal power yielded renders the classical first-principles-based models presented in the literature invalid, as they cannot account for the nonlinear impact of mirrors in the solar field performance. This justifies the use of new data-driven techniques, and four modelling methodologies are compared, with the NARX artificial neural network that proves the most effective, with an R

^{2}

value of 0.9741 and an RMSE value of 6.3151. The best model is validated by simulation of a solar MD plant.

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引用次数: 0

Interpretable machine learning insights of power conversion efficiency for hybrid perovskites solar cells

IF 6 2区工程技术 Q2 ENERGY & FUELS

Solar Energy

Pub Date : 2025-02-25 DOI: 10.1016/j.solener.2025.113373

Yudong Shi , Jiansen Wen , Cuilian Wen , Linqin Jiang , Bo Wu , Yu Qiu , Baisheng Sa

Hybrid organic–inorganic perovskites (HOIPs) solar cells have presented broad application prospects in the photovoltaic field due to their high energy conversion efficiency, ease of preparation, and low production costs. With the flourishing development of artificial intelligence, machine learning (ML) has been recently used for novel HOIP designs. However, the practical application of ML models for the designing of HOIPs is hampered mainly due to the lack of interpretability. Herein, a data-driven interpretable ML approach is introduced to distill the universal simple descriptors for the power conversion efficiency (PCE) of HOIPs-based solar cells. It is highlighted that two descriptors consist of easily obtained parameters are proposed to accurately predict PCE, which are superior to the commonly used descriptor band gap (E_g). Remarkably, universal criterions for the high-throughput screening of HOIPs are proposed to accelerate the screening of HOIPs-based solar cells with high PCE performance. This work paves the way toward rapid and precise screening of efficient HOIPs-based solar cells using a data-driven interpretable ML approach.

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引用次数: 0

Laboratory experimental analysis of crystalline silicon photovoltaic module degradation after operating over 6 years: A case study in Ghana

IF 6 2区工程技术 Q2 ENERGY & FUELS

Solar Energy

Pub Date : 2025-02-25 DOI: 10.1016/j.solener.2025.113379

Qingfa MENG , Honglie SHEN , Jinjie ZHENG , Xuemei LI

This paper systematically analyzes and evaluates the performance of photovoltaic (PV) modules after six years of outdoor exposure in Winneba, Ghana, under a hot and humid climate. Unlike conventional field tests, all data were obtained through rigorous laboratory testing conducted on the PV modules and their materials in accordance with IEC 61215 standards to investigate the causes of power degradation. The results indicated an average power degradation of 35.36 %, equivalent to an annual degradation rate of 5.89 %. Although most modules met the wet insulation test criteria specified by IEC 61215, they exhibited potential safety risks for future field operations due to wet insulation values approaching the standard threshold of 24.5 MΩ. The primary cause of power degradation was identified as potential-induced degradation (PID), with modules experiencing power losses of 44.60 % and 99.57 % under PID testing with a 1000 V negative voltage stress. Electroluminescence (EL) images showing dark edges provided strong evidence supporting the presence of PID. Additionally, poor peel strength test results suggested a risk of delamination, which could be attributed to ion migration during the PID process. Four out of five tested module gel contents were comparable to those of typical unexposed ethylene–vinyl acetate (EVA), and two modules still met the standard requirements even after exposure to damp heat (DH1000), indicating that EVA degradation may not be the primary cause of power loss. The water vapor transmittance rate (WVTR) of the module backsheets, after six years of outdoor exposure, continued to meet the requirements of the Chinese National Standard, further suggesting that moisture ingress may not be a significant contributor to power degradation. This study represents a valuable effort to assess the long-term performance of field-exposed PV modules using accelerated aging tests conducted according to IEC 61215 standards.

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引用次数: 0

Solar irradiance separation with deep learning: An interpretable multi-task and physically constrained model based on individual–interactive features

IF 6 2区工程技术 Q2 ENERGY & FUELS

Solar Energy

Pub Date : 2025-02-24 DOI: 10.1016/j.solener.2025.113353

Mengmeng Song , Dazhi Yang , Bai Liu , Disong Fu , Hongrong Shi , Xiang’ao Xia , Martin János Mayer

As an essential part of solar forecasting and resource assessment, separation modeling has received widespread attention over the past half a century. Despite the numerous proposals thus far, most models are semi-empirical in nature, with limited accuracy. The other option, namely, machine-learning models, does not show a definitive advantage and usually lacks comparisons with the latest quasi-universal model. This study proposes an interpretable multi-task and physically constrained separation model based on individual–interactive features (IIF-IMCSM). The model has three blocks: (1) an informative predictor identification block, (2) an individual–interactive feature extraction block, and (3) a physically constrained irradiance component estimation block, each carrying some modeling innovations. Differing from other separation models, IIF-IMCSM simultaneously produces estimates for both the beam and diffuse components that satisfy the closure equation, and it overcomes the common drawback of lacking interpretability of machine-learning models. Based on five comprehensive datasets covering diverse radiation regimes of the globe, it is found that the overall normalized root mean square errors of IIF-IMCSM for beam normal irradiance and diffuse horizontal irradiance are 12.51% and 24.50%, as compared to 16.32%, 34.86%, and 13.47%, 26.56% for the top-performing semi-empirical and machine-learning models.

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引用次数: 0

A satellite-based novel method to forecast short-term (10 min − 4 h) solar radiation by combining satellite-based cloud transmittance forecast and physical clear-sky radiation model 将卫星云层透射率预报与物理晴空辐射模型相结合的基于卫星的短期（10 分钟 - 4 小时）太阳辐射预报新方法

IF 6 2区工程技术 Q2 ENERGY & FUELS

Solar Energy

Pub Date : 2025-02-23 DOI: 10.1016/j.solener.2025.113376

Bing Hu , Huaiyong Shao , Changkun Shao , Wenjun Tang

Short-term forecasting of solar radiation is crucial for grid integration of solar photovoltaic (PV) power and for grid scheduling and optimization. Enhancing the interpretability of satellite-based short-term forecasts that rely on artificial intelligence is a research focus. In this study, we presented a novel approach to forecast short-term solar radiation by combining satellite-based cloud transmittance forecast and physical clear-sky radiation forecast. The innovation of this study lies in its foundation on atmospheric physics principles, specifically forecasting cloud transmittance and distinguishing between cloudy and clear skies. The cloud transmittance prediction was conducted based on Himawari-8 observations using widely adopted and well-known convolutional neural network (CNN) and long short-term memory (LSTM) networks, while the clear-sky radiation forecast can be conducted with clear-sky radiation model or prediction based on numerical weather prediction (NWP). Compared to other satellite-based baseline forecasting frameworks, the accuracy of our developed framework for short-term forecasting of solar radiation is improved, with an average root mean square error of about 62 W/m² over 116 sites and an average relative root mean square error of about 14.36 % with a forecast horizon of 10 min. When the forecast horizon was increased to ranging from 20 min to 4 h, the corresponding average root mean square error increased from 72.16 W/m² to 159.75 W/m², and the relative root mean square error increased from 16.71 % to 37 %. This work can forecast solar radiation maps and assist in the flexible regulation of solar PV generation.

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引用次数: 0

Reducing the impact of climate change on renewable energy systems through wind–solar blending: A worldwide study with CMIP6

IF 6 2区工程技术 Q2 ENERGY & FUELS

Solar Energy

Pub Date : 2025-02-23 DOI: 10.1016/j.solener.2025.113365

Xiaokang Liu , Hongrong Shi , Dazhi Yang , Xiaolong Chen , Xiang'ao Xia , Yang Xie

Mitigating climate change has hitherto been a pressing issue for global sustainable development. Climate change can alter the frequency and intensity of extreme weather events, which in turn impacts solar and wind power generation. This study employs data from 10 CMIP6 models to estimate potential changes in global wind and photovoltaic (PV) power generation under three different Shared Socioeconomic Pathways (SSPs) during the critical period for global carbon neutrality (2040–2064). Results indicate that in Central Europe, PV potential (PV_POT) increases by 8%, while extreme low PV output days (PV10) decrease by 10 days under SSP245. Conversely, in the Arabian Peninsula, PV_POT decreases by 4% with PV10 increasing by 16 days. For wind power (WP), significant reductions up to 35% are observed in regions like Southern Russia or the Eastern United States, while WP increases by 40% in areas such as the Sahel or Central South America. Notably, the wind–solar hybrid system effectively mitigates extreme low-output events, with combined output variability reduced by 0.04 in Central Europe. The SSP585 scenario demonstrates favorable trends for wind power, with increases up to 34% in Northern India. These findings emphasize the importance of integrating hybrid systems to construct a resilient energy supply chain and adapt to spatially heterogeneous climate impacts.

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引用次数: 0

Thermal and electrical performance analysis of nanofluid beam splitting PV/T system based on full coupling of light heat and electricity

IF 6 2区工程技术 Q2 ENERGY & FUELS

Solar Energy

Pub Date : 2025-02-20 DOI: 10.1016/j.solener.2025.113363

Jianqing Lin, Xianglong Chen, Lei Han, Gang Wang

Optimizing the system structure and introducing beam splitting technology are effective strategies for enhancing the operational performance of concentrating solar photovoltaic/thermal (PV/T) systems. In this study, a two-stage concentrating PV/T system based on beam splitting is proposed, and the system is analyzed using a fully coupled optical-thermal-electrical method. First, the reliability of the established discrete ordinates (DO) radiation model is verified using the Monte Carlo Ray Tracing (MCRT) method. Next, the obtained quantitative results are applied as a volumetric heat source in a 3D flow heat transfer model. Finally, the operational characteristics of the two-stage concentrating nanofluid PV/T system under various operating conditions are investigated parametrically. The results demonstrate that the radiative fluxes obtained from the 2D DO radiation model are in good agreement with those derived from the MCRT method. Under the series operating condition of the 3D flow heat transfer model, the electrical efficiency of the PV subsystem is 22.13 %, the thermal efficiency of the integrated system is 71.85 %, and the exergy efficiency is 20.77 %, with a nanofluid inlet temperature of 25 °C and an inlet mass flow rate of 0.03 kg/s. This study also evaluates the system’s operating efficiency under series and parallel configurations, showing that the series configuration achieves higher exergy efficiency, while the parallel configuration enhances the thermal efficiency of the system.

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引用次数: 0

Experimental impacts of transparency on strawberry agrivoltaics using thin film photovoltaic modules under low light conditions

IF 6 2区工程技术 Q2 ENERGY & FUELS

Solar Energy

Pub Date : 2025-02-20 DOI: 10.1016/j.solener.2025.113375

Uzair Jamil , Joshua M. Pearce

This study determines the effects of varying lighting conditions from agrivoltaics on strawberry growth and yield by investigating strawberry production under thin-film cadmium telluride PV modules. The goal of this study was to assess the impact of varying PV transparency on strawberry yield and growth under Cd-Te PV modules, and to evaluate the potential of agrivoltaics as a sustainable solution for strawberry production in northern climates. Seven levels of transparency (10%, 30%, 40%, 50%, 60%, 70%, and 80%) were tested, which resulted in varying photosynthetically active radiation (PAR) values under each PV module type. Modules were tested in a controlled environment designed to replicate outdoor conditions of London Ontario, with regulated temperature and lighting. Strawberry fresh weight, plant height, leaf count, and flower count were quantified. The results indicate that strawberries grown under 70% transparency PV exhibited a fresh weight 140.6% of the average control. Additionally, 40% transparency maintained a greater than 80% yield making them viable in all regions with agrivoltaics yield mandates. Increased transparency in PV modules also correlated with a higher number of leaves, while height correlation was complex. If Canadian strawberry farms converted to agrivoltaics, between 595 and 1,786 GWh of solar electricity could be generated and globally strawberry agrivoltaics offer an electrical potential ranging from 58 to 173 TWh. The adoption of agrivoltaics in the strawberry sector could facilitate energy self-sufficiency and transform it into a net electricity exporter, generating additional revenue for farmers.

本研究通过调查碲化镉薄膜光伏组件下的草莓生产情况，确定农业光伏的不同光照条件对草莓生长和产量的影响。这项研究的目的是评估不同的光伏透明度对碲化镉光伏组件下草莓产量和生长的影响，并评估农业光伏作为北方气候条件下草莓生产的可持续解决方案的潜力。测试了七种透明度水平（10%、30%、40%、50%、60%、70% 和 80%），从而得出每种光伏组件类型下不同的光合有效辐射 (PAR) 值。组件在受控环境中进行了测试，该环境旨在复制安大略省伦敦市的室外条件，并对温度和光照进行了调节。对草莓鲜重、株高、叶片数和花数进行了量化。结果表明，在 70% 透明度光电池下生长的草莓鲜重是平均对照的 140.6%。此外，40% 透明度下的草莓产量保持在 80% 以上，这使它们在所有规定了农业光伏产量的地区都能生存。光伏组件透明度的增加也与叶片数量的增加有关，而高度的相关性则比较复杂。如果加拿大草莓农场转用农业光伏技术，可产生 595 至 1786 千兆瓦时的太阳能电力，而全球草莓农业光伏技术可提供 58 至 173 太瓦时的电力潜力。在草莓行业采用农业光伏技术可促进能源自给自足，并将其转变为净电力出口国，为农民带来额外收入。

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引用次数: 0

Optoelectronic evaluation of SrMnO3 cubic perovskite for prospective visible light solar photovoltaic application 对 SrMnO3 立方包晶进行光电评估，以展望可见光太阳能光伏应用前景

IF 6 2区工程技术 Q2 ENERGY & FUELS

Solar Energy

Pub Date : 2025-02-19 DOI: 10.1016/j.solener.2025.113334

N.P. Vikas , Manisha Kar , Archana Hota , Sougat Purohit , Salila Kumar Sethy , Kamatchi Jothiramalingam Sankaran , Ravi P. Srivastava , Amritendu Roy

Next-generation solar cell materials with superior optoelectronic and photovoltaic properties must circumvent the toxicity and degradation issues of hybrid lead halide perovskites. In this regard, transition metal oxide perovskites with favourable optoelectronic properties are of significant relevance. In this work, we report a combined theoretical–experimental investigation into the optoelectronic properties of cubic-perovskite, SrMnO₃ (SMO) for prospective visible-light photovoltaic application. Using first-principles density functional theory calculations, we show that SMO with cubic symmetry demonstrates a direct bandgap character (∼0.62 eV), exceptional absorption behaviour (∼10⁵ cm⁻¹ in the visible range), substantial dielectric constant (∼11) and a reasonably small exciton binding energy (∼44 meV) promising a sizeable photovoltaic response (PCE_SLME ∼ 16 %). Accordingly, thin films of SMO were grown on fluorine-doped tin oxide (FTO) coated glass substrate using pulsed laser deposition (PLD) technique. Structural characterization demonstrated phase pure SMO with cubic symmetry. Room-temperature Hall measurement allowed the determination of the nature (p-type) and concentration (1.37 × 10¹² cm⁻³) of majority charge carriers, conductivity (5.56 × 10⁻⁶ S/cm), and carrier mobility (24.5 cm²/V·s) which are reasonably comparable to those of archetypal halide perovskite, CH₃NH₃PbI₃ (MAPbI₃). Ultraviolet photoelectron spectroscopy further allowed the determination of energies corresponding to valence and conduction band edges, crucial for device fabrication. Initial device characterization demonstrates small yet finite photovoltaic response, suggesting the requirement of thorough optimization of the device fabrication parameters and development of a suitable hole transport layer.

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引用次数: 0

Leveraging large-scale aerial data for accurate urban rooftop solar potential estimation via multitask learning

IF 6 2区工程技术 Q2 ENERGY & FUELS

Solar Energy

Pub Date : 2025-02-19 DOI: 10.1016/j.solener.2025.113336

Alessia Boccalatte , Ankit Jha , Jocelyn Chanussot

Convolutional Neural Networks (CNNs) have shown remarkable success in remote sensing tasks. In urban contexts, recent research has utilized CNNs to generate rooftop segmentation masks and determine rooftop section orientation from aerial images. This cost-effective approach is especially valuable for large-scale rooftop solar potential estimations when detailed three-dimensional data is unavailable. This research introduces SolarMTNet, a novel multitask dense-prediction network designed for rooftop solar potential prediction using only aerial images. Unlike previous studies that focus on small manually labeled datasets (approximately 2000 scenes) and only segment rooftop orientations while typically assuming constant slopes, SolarMTNet simultaneously segments both orientations and slopes, enhancing the accuracy of solar potential estimations by 40%. SolarMTNet leverages a large, automatically labeled dataset (up to 280000 scenes) created from open-source Swiss geospatial and aerial data, significantly improving generalization. The model is trained on rooftop data from the Zurich and Geneva cantons and cross-validated on the Canton of Vaud, Switzerland. The results show a mean Intersection over Union (mIoU) of 0.67 for orientation segmentation and 0.40 for slope segmentation. The estimated irradiance exhibits an absolute mean percentage difference of only 5% compared to real solar cadaster data derived from detailed model-based calculations, primarily due to shading issues. Finally, SolarMTNet has also been tested in different geographical areas outside Switzerland (France and Germany), demonstrating consistent performance across diverse regions and pixel resolutions.

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引用次数: 0