Carbon-based brilliance: a novel approach to renewable energy in radiotherapy centers.

The energy produced from other sources which does neither come from fossil fuels nor contribute in the production of any greenhouse effects that causes climate changes is called as 'Alternative Energy'. Since our world's primary energy sources such as coal, oil and natural gases are exploited to a greater extent, we are in an urge to switch to an alternative energy. Scattered radiation, a common byproduct in radiation therapy and diagnostic radiology, presents a unique opportunity in the realm of alternative energy. As a potential source of interference, scattered radiation can be repurposed to contribute to sustainable energy solutions. Addressing the issue of scattered radiation wastage and utilizing it for alternative energy, an activated carbon-based solar cell emerges as a solution. This solar cell, a conventional one in which cadmium Telluride is replaced by coconut shell based carbon material, has the potential in producing a significant amount of electrical energy by utilizing scattered radiation from radiotherapy and radiology machines. Furthermore, this activated carbon based-material undergoes thorough characterization into various teletherapy and radiology machines, and it can be seamlessly integrated into clinical practices.

New boost type single phase inverters for photovoltaic applications with reduced device count.

In recent years, single-stage boost inverters with common ground have shaped the inverter markets due to the many benefits associated with these types of inverters, including their high efficiency, single control scheme, and integrated boost converter. A new boost-type inverter that utilizes a common ground and has fewer switches is proposed in this article. It uses two DC-link capacitors connected in parallel and discharged independently while being charged simultaneously. The voltage for the positive and negative half cycles is supplied by the capacitors located at the top and bottom of the circuit, respectively. In addition, a comparison is made between the proposed circuit and the boost inverter already in use in the literature. Using PLECS as the computing software, the efficiencies are determined depending on the various percentages of output power. To validate performance, present experimental data, and attain the best possible efficiency of 97%, a 400 W prototype model is constructed. In addition to that, the breakdown of the costs is shown.

Improved cooling of photovoltaic panels by natural convection flow in a channel with adiabatic extensions.

In hot dry regions, photovoltaic modules are exposed to excessive temperatures, which leads to a drop in performance and the risk of overheating. The present numerical study aims to evaluate the natural air cooling of PV modules by an inclined chimney mounted at the back. The basic equations were solved using the finite volume method. The validity of the model is verified by comparison with the data available in the literature. Thermal and dynamic flow patterns are analyzed for a variety of parameters: Rayleigh numbers from 102 to 106, PV panel tilt angle from 15° to 90°, and channel aspect ratios from 1/20 to 1/5. A critical aspect ratio has been determined to minimize overheating of the PV module. According to the computational results, the tilt angle and modified Rayleigh number increase the mass flow rate and mean Nusselt number. The overheating zone with maximum temperatures is located in the upper part of the photovoltaic panel. The addition of an extension to both channel's inlet and outlet was found to improve the cooling of the photovoltaic panels; however, only the extensions downstream of the channel are truly effective. The critical lengths at which channel performance improves significantly were identified by examining the impact of longer extensions on channel performance. Increasing the extension length from 0 to 3H improves the mass flow rate by 65%, the average Nusselt number by 13.4%, and leads to an 11% decrease in maximum temperature when Ra* = 106. This cooling technique is particularly promising for hot dry regions where water is scarce.

An artificial gorilla troops optimizer for stochastic unit commitment problem solution incorporating solar, wind, and load uncertainties.

The unit commitment (UC) optimization issue is a vital issue in the operation and management of power systems. In recent years, the significant inroads of renewable energy (RE) resources, especially wind power and solar energy generation systems, into power systems have led to a huge increment in levels of uncertainty in power systems. Consequently, solution the UC is being more complicated. In this work, the UC problem solution is addressed using the Artificial Gorilla Troops Optimizer (GTO) for three cases including solving the UC at deterministic state, solving the UC under uncertainties of system and sources with and without RE sources. The uncertainty modelling of the load and RE sources (wind power and solar energy) are made through representing each uncertain variable with a suitable probability density function (PDF) and then the Monte Carlo Simulation (MCS) method is employed to generate a large number of scenarios then a scenario reduction technique known as backward reduction algorithm (BRA) is applied to establish a meaningful overall interpretation of the results. The results show that the overall cost per day is reduced from 0.2181% to 3.7528% at the deterministic state. In addition to that the overall cost reduction per day is 19.23% with integration of the RE resources. According to the results analysis, the main findings from this work are that the GTO is a powerful optimizer in addressing the deterministic UC problem with better cost and faster convergence curve and that RE resources help greatly in running cost saving. Also uncertainty consideration makes the system more reliable and realistic.

The Effect of Conjugated Nitrile Structures as Acceptor Moieties on the Photovoltaic Properties of Dye-Sensitized Solar Cells: DFT and TD-DFT Investigation.

A major challenge in improving the overall efficiency of dye-sensitized solar cells is improving the optoelectronic properties of small molecule acceptors. This work primarily investigated the effects of conjugation in nitriles incorporated as acceptor moieties into a newly designed series of D-A-A dyes. Density functional theory was employed to specifically study how single-double and single-triple conjugation in nitriles alters the optical and electronic properties of these dyes. The Cy-4c dye with a highly conjugated nitrile unit attained the smallest band gap (1.80 eV), even smaller than that of the strong cyanacrylic anchor group (2.07 eV). The dyes lacking conjugation in nitrile groups did not contribute to the LUMO, while LUMOs extended from donors to conjugated nitrile components, facilitating intramolecular charge transfer and causing a strong bind to the film surface. Density of state analysis revealed a considerable impact of conjugated nitrile on the electronic properties of dyes through an effective contribution in the LUMO, exceeding the role of the well-known strong 2,1,3-benzothiadiazole acceptor unit. The excited state properties and the absorption spectra were investigated using time-dependent density functional theory (TD-DFT). Conjugation in the nitrile unit caused the absorption band to broaden, strengthen, and shift toward the near-infrared region. The proposed dyes also showed optimum photovoltaic properties; all dyes possess high light-harvesting efficiency (LHE) values, specifically 96% for the dyes Cy-3b and Cy-4c, which had the most conjugated nitrile moieties. The dyes with higher degrees of conjugation had longer excitation lifetime values, which promote charge transfer by causing steady charge recombination at the interface. These findings may provide new insights into the structure of conjugated nitriles and their function as acceptor moieties in DSSCS, which may lead to the development of extremely effective photosensitizers for solar cells.

Broadband sensitized near-infrared emission in Eu2+-Nd3+ co-doped BaAl2O4 as a potential spectral modulator for silicon solar cells.

The near-infrared (NIR) down-conversion process for broadband sensitization has been studied in Eu2+-Nd3+ co-doped BaAl2O4. This material has a broad absorption band of 200-480 nm and can convert photons in the visible region into NIR photons. The NIR emission at 1064 nm, attributed to the Nd3+:4F3/2 → 4I11/2 transition, matches the bandgap of Si, allowing Si solar cells to utilize the solar spectrum better. The energy transfer (ET) process between Eu2+ and Nd3+ was demonstrated using photoluminescence spectra and luminescence decay curves, and Eu2+ may transfer energy to Nd3+ through the cooperative energy transfer (CET) to achieve the down-conversion process. The energy transfer efficiency (ETE) and theoretical quantum efficiency (QE) were 68.61% and 156.34%, respectively, when 4 mol% Nd3+ was introduced. The results indicate that BaAl2O4:Eu2+-Nd3+ can serve as a potential modulator of the solar spectrum and is expected to be applied to Si solar cells.

DFT and TD-DFT Investigations for the Limitations of Lengthening the Polyene Bridge between N,N-dimethylanilino Donor and Dicyanovinyl Acceptor Molecules as a D-π-A Dye-Sensitized Solar Cell.

One useful technique for increasing the efficiency of organic dye-sensitized solar cells (DSSCs) is to extend the π-conjugated bridges between the donor (D) and the acceptor (A) units. The present study used the DFT and TD-DFT techniques to investigate the effect of lengthening the polyene bridge between the donor N, N-dimethyl-anilino and the acceptor dicyanovinyl. The results of the calculated key properties were not all in line with expectations. Planar structure was associated with increasing the π-conjugation linker, implying efficient electron transfer from the donor to the acceptor. A smaller energy gap, greater oscillator strength values, and red-shifted electronic absorption were also observed when the number of polyene units was increased. However, some results indicated that the potential of the stated dyes to operate as effective dye-sensitized solar cells is limited when the polyene bridge is extended. Increasing the polyene units causes the HOMO level to rise until it exceeds the redox potential of the electrolyte, which delays regeneration and impedes the electron transport cycle from being completed. As the number of conjugated units increases, the terminal lobes of HOMO and LUMO continue to shrink, which affects the ease of intramolecular charge transfer within the dyes. Smaller polyene chain lengths yielded the most favorable results when evaluating the efficiency of electron injection and regeneration. This means that the charge transfer mechanism between the conduction band of the semiconductor and the electrolyte is not improved by extending the polyene bridge. The open circuit voltage (VOC) was reduced from 1.23 to 0.70 V. Similarly, the excited-state duration (τ) decreased from 1.71 to 1.23 ns as the number of polyene units increased from n = 1 to n = 10. These findings are incompatible with the power conversion efficiency requirements of DSSCs. Therefore, the elongation of the polyene bridge in such D-π-A configurations rules out its application in solar cell devices.

Effects of nonlinear thermal radiation on the efficiency of building integrated photovoltaic systems with nanofluid cooling.

The nonlinear effects of thermal radiation on the free convection flow of certain nanofluids along a heated wall are studied numerically using an original finite-difference method. Nanofluids are used to improve the performance of flat and curved integrated photovoltaic modules. The partial differential equations governing the flow are difficult to solve due to the strong non-linearity of the radiative term. In contrast to previous studies, the problem is solved directly without linearization by Rosseland's nonlinear approximation. The proposed numerical method is validated with results from the literature. The effects of nonlinearity and various physical parameters such as time, volume fraction and radiation parameter on the velocity, temperature, Nusselt number and skin friction coefficient of the CuO-water nanofluid are analyzed and presented graphically. A comparative study between the solutions given by the linear and non-linear problems reveals that Rosseland's linear approximation is no longer valid when the effect of thermal radiation is significant. On the other hand, the non-linear model better reflects the physical phenomena involved in the cooling process. Finally, a comparison of the performance of five nanofluids (CuO, Ag, Al2O3, Cu and TiO2 in water) shows that the Cu-water nanofluid performs best, with a high heat transfer rate and low shear stresses.

Research on multi-defects classification detection method for solar cells based on deep learning.

Solar cells are playing a significant role in aerospace equipment. In view of the surface defect characteristics in the manufacturing process of solar cells, the common surface defects are divided into three categories, which include difficult-detecting defects (mismatch), general defects (bubble, glass-crack and cell-crack) and easy-detecting defects (glass-upside-down). Corresponding to different types of defects, the deep learning model with different optimization methods and a classification detection method based on multi-models fusion are proposed in the paper. In the proposed model, in order to solve the mismatch problem between the default anchor boxes size of YOLOv5s model and the extreme scale of the battery mismatch defect label boxes, the K-means algorithm was adopted to re-cluster the dedicated anchor boxes for the mismatch defect label boxes. In order to improve the comprehensive detection accuracy of YOLOv5s model for the general defects, the YOLOv5s model was also improved by the methods of image preprocessing, anchor box improving and detection head replacing. In order to ensure the recognition accuracy and improve the detection speed for easy-detecting defects, the lightweight classification network MobileNetV2 was also used to classify the cells with glass-upside-down defects. The experimental results show that the proposed optimization model and classification detection method can significantly improve the defect detection precision. Respectively, the detection precision for mismatch, bubble, glass-crack and cell-crack defects are up to 95.64%, 91.8%, 93.1% and 98.0%. By using lightweight model to train the glass-upside-down defect dataset, the average classification accuracy reaches 100% and the detection speed reaches 13.29 frames per second. The comparison experiments show that the proposed model has a great improvement in detection accuracy compared with the original model, and the defect detection speed of lightweight classification network is improved more obviously, which confirms the effectiveness of the proposed optimization model and the multi-defect classification detection method for solar cells defect detection.

Promoting distributed photovoltaic adoption: An evolutionary game model approach for stakeholder coordination.

Distributed photovoltaic (DPV) is a promising solution to climate change. However, the widespread adoption of DPV faces challenges, such as high upfront costs, regulatory barriers, and market uncertainty. Addressing these barriers requires coordinating the interests of stakeholders in the promotion of DPV. Therefore, this paper constructs a three-party evolutionary game model in a social network with the government, investment companies and residents as the main subjects and examines the influence of different subjects' behavioral strategies on the promotion of DPV under the social learning mechanism. The results show that: (1) In the game equilibrium, both the government and residents hold a positive attitude towards the promotion of DPV; (2) Companies will obtain most of the subsidies through market power and information differences, resulting in the increase of government subsidies that do not always benefit residents; (3) The increase of energy consumption and pollution prevention costs can promote companies' investment in DPV; (4) The increase of environmental protection taxes to a certain extent helps companies to take responsibility for promoting DPV, reducing the pressure on the government to promote it and increasing residents' income. This study provides insights into the sustainable development of DPV.

PV-Assisted grid connected multi output electric vehicle charger with PV2V, G2V and PV2G functions.

The demand for renewable energy-based Electric Vehicle (EV) charging infrastructure is increasing in recent years. Solar PV based EV charging method is preferred as it has simple energy harvesting technique. The PV system is an uncertain power source, where the power generation is varied with respect to the availability of sunlight. So, that the charging station requires a backup power supply for the uninterrupted charging. For the integrated power sources, the charging station requires a simple and efficient conversion unit for the DC/AC/DC conversion. In this work, a modified Z-source inverter (MZSI) is developed for the multiport EV charger using PV and grid. The proposed MZSI is connected between the input and output sides to boost the voltage as per the demand at the battery side. In order to connect many battery units with the charger, the capacitors used in the MZSI are split as per the required number of charging ports. This developed converter topology operates the systems in four different modes like PV-Grid, PV-battery, grid-battery, and battery-grid. The performance of this proposed work has been validated in MATLAB/Simulink® and in the experimental setup. The experimental setup has been developed with two charging ports for obtaining 250W at each charger end which cumulatively produces 500W output across both chargers with an efficiency of 90.18%.

Development of a novel LED-IoT photoreactor for enhanced removal of carbamazepine waste driven by solar energy.

This study introduces an innovative LED-IoT photoreactor, representing a significant advancement in response to the demand for sustainable water purification. The integration of LED-IoT installations addresses the challenge of intermittent sunlight availability, employing LEDs with a spectrum mimicking natural sunlight. Passive Infra-Red (PIR) sensors and Internet of things (IoT) technology ensure consistent radiation intensity, with the LED deactivating in ample sunlight and activating in its absence. Utilizing a visible light-absorbing photocatalyst developed through sol-gel synthesis and mild-temperature calcination, this research demonstrates a remarkable carbamazepine removal efficiency exceeding 95% under LED-IoT system illumination, compared to less than 90% efficiency with sunlight alone, within a 6-h exposure period. Moreover, the designed photocatalytic system achieves over 60% mineralization of carbamazepine after 12 h. Notably, the photocatalyst demonstrated excellent stability with no performance loss during five further cycles. Furthermore, integration with renewable energy sources facilitated continuous operation beyond daylight hours, enhancing the system's applicability in real-world water treatment scenarios. A notable application of the LED-IoT system at an operating sewage treatment plant showed nearly 80% efficiency in carbamazepine removal from sewage in the secondary settling tank after 6 h of irradiation, coupled with nearly 40% mineralization efficiency. Additionally, physicochemical analyses such as XPS and STA-FTIR confirm that the carbamazepine photooxidation process does not affect the surface of the photocatalyst, showing no adsorption for degradation products.

Synthesis and Photovoltaic Performance of ß-Amino-Substituted Porphyrin Derivatives.

New ß-amino-substituted porphyrin derivatives bearing carboxy groups were synthesized and their performance as sensitizers in dye-sensitized solar cells (DSSC) was evaluated. The new compounds were obtained in good yields (63-74%) through nucleophilic aromatic substitution reactions with 3-sulfanyl- and 4-sulfanylbenzoic acids. Although the electrochemical studies indicated suitable HOMO and LUMO energy levels for use in DSSC, the devices fabricated with these compounds revealed a low power conversion efficiency (PCE) that is primarily due to the low open-circuit voltage (Voc) and short-circuit current density (Jsc) values.

Submerged nanoporous anodized alumina structure for solar-powered desalination.

Development of nanoporous structures utilizing a single step of anodization technique is well recognized as a cost-effective and straightforward approach for several applications. In the current work, anodized alumina was developed with nanoporous structure by utilizing oxalic acid as an electrolyte with a continuous voltage of 40 V. The formed nanoporous structure was subjected to desalination application because of its high absorbance of broadband solar spectrum energy. The desalination setup consists of two solar stills namely conventional and modified. The developed structure is placed in the modified still to examine its performance. It was observed that the structure distributing heat to surrounding water by absorbing photon energy from the sun through the nanopores and giving an efficient pathway to the water vapours for developing effective desalination. The nanoporous structure having ~ 45 nm average diameter. Furthermore, the band gap energy of nanoporous structure was found to be ~ 2.5 eV (absorption spectrum fitting) and ~ 2.8 eV (Tauc plot). The nanoporous structure possess the visible light spectra in solar region which helps the band gaps of nanoporous structure to provide an additional supply of energy for generating more water to evaporate. Moreover, the Urbach energy of the structure is 0.5 eV which reveals less defects in the modified still. The overall distillate yield of modified still was increased to 21% in contrast to conventional. Water quality analysis was also carried out before and after the desalination experiments, and the results were within acceptable limits set by World Health Organization (WHO).

Prediction of daily global solar radiation in different climatic conditions using metaheuristic search algorithms: a case study from Türkiye.

Today's many giant sectors including energy, industry, tourism, and agriculture should closely track the variation trends of solar radiation to take more benefit from the sun. However, the scarcity of solar radiation measuring stations represents a significant obstacle. This has prompted research into the estimation of global solar radiation (GSR) for various regions using existing climatic and atmospheric parameters. While prediction methods cannot supplant the precision of direct measurements, they are invaluable for studying and utilizing solar energy on a global scale. From this point of view, this paper has focused on predicting daily GSR data in three provinces (Afyonkarahisar, Rize, and Agri) which exhibit disparate solar radiation distributions in Türkiye. In this context, Gradient-Based Optimizer (GBO), Harris Hawks Optimization (HHO), Barnacles Mating Optimizer (BMO), Sine Cosine Algorithm (SCA), and Henry Gas Solubility Optimization (HGSO) have been employed to model the daily GSR data. The algorithms were calibrated with daily historical data of five input variables including sunshine duration, actual pressure, moisture, wind speed, and ambient temperature between 2010 and 2017 years. Then, they were tested with daily data for the 2018 year. In the study, a series of statistical metrics (R2, MABE, RMSE, and MBE) were employed to elucidate the algorithm that predicts solar radiation data with higher accuracy. The prediction results demonstrated that all algorithms achieved the highest R2 value in Rize province. It has been found that SCA (MABE of 0.7023 MJ/m2, RMSE of 0.9121 MJ/m2, and MBE of 0.2430 MJ/m2) for Afyonkarahisar province and GBO (RMSE of 0.8432 MJ/m2, MABE of 0.6703 MJ/m2, and R2 of 0.8810) for Agri province are the most effective algorithms for estimating GSR data. The findings indicate that each of the metaheuristic algorithms tested in this paper has the potential to predict daily GSR data within a satisfactory error range. However, the GBO and SCA algorithms provided the most accurate predictions of daily GSR data.

What Should be Considered While Designing Hole-Transporting Material for Perovskite Solar Cells? A Special Attention to Thiophene-Based Hole-Transporting Materials.

The molecular design and conformations of hole-transporting materials (HTM) have unravelled a strategy to enhance the performance of environmentally sustainable perovskite solar cells (PSC). Several attempts have been made and several are underway for improving the efficiency of PSCs by designing an efficient HTM, which is crucial to preventing corrosion, facilitating effective hole transportation, and preventing charge recombination. There is a need for a potential alternative to the current market-dominating HTM due to its high cost of production, dopant requirements, moisture sensitivity, and low stability. Among several proposed HTMs, molecules derived from thiophene exhibit unique behaviour, such as the interaction with under-coordinated Pb2+, thereby facilitating the passivation of surface defects in the perovskite layer. In addition, coupling a suitable side chain imparts a hydrophobic character, eventually leading to the development of a moisture-sensitive and highly stable PSC. Furthermore, thiophene-backboned polymers with ionic pendants have been employed as an interfacial layer between PSC layers, with the backbone facilitating efficient charge transfer. This perspective article comprehensively presents the design strategy, characterization, and function of HTMs associated with thiophene-derived molecules. Hence, it is observed that thiophene-formulated HTMs have an enhanced passivation effect, good performance in an open-circuit environment, longevity, humidity resistance, thermostability, good hole extraction, and mobility in a dopant-free condition. For a better understanding, the article provides a comparative description of the activity and function of thiophene-based small molecules and polymers and their effect on device performance.

[Assessment of carbon reduction and sink enhancement potential of photovoltaic+mining ecological restoration model]. / å ‰ä¼+矿山生态修复模式的减碳增汇潜力评估.

The energy oriented mine ecological restoration mode of photovoltaic+ecological restoration provides a breakthrough for alleviating the dilemma of photovoltaic land development and solving the urgent need for restoration of abandoned mining land. Taking a mining area in central Liaoning Province as an example, we established three photovoltaic+mining ecological restoration modes, including forest-photovoltaic complementary, agriculture-photovoltaic, and grass photovoltaic complementation. Combined with the life cycle assessment method, we calculated and assessed the potential of photovoltaic+mining ecological restoration in carbon reduction and sink enhancement. The average annual carbon reduction and sink increase was 514.93 t CO2·hm-2 under the photovoltaic+mining ecological restoration mode, while the average annual carbon reduction per megawatt photovoltaic power station was 1242.94 t CO2. The adoption of photovoltaic+ecological restoration mode in this mining area could make carbon reduction and sink enhancement 6.30-7.79 Mt CO2 during 25 years. The carbon reduction and sink increment mainly stemmed from the photovoltaic clean power generation induced carbon reduction, accounting for 96.4%-99.4%, while the contribution of ecosystem carbon sink increment was small, accounting for only 0.6%-3.7% of the total. Among different photovoltaic+ecological restoration modes, the carbon reduction and sink increment was the largest in forest-photovoltaic complementary (7.11 Mt CO2), followed by agriculture-photovoltaic (7.04 Mt CO2), and the least in grass photovoltaic complementation (6.98 Mt CO2). Constructing the development mode of "photovoltaic+mining ecological restoration" could effectively leverage the dual benefits of reducing emissions from photovoltaic power generation and increase sinks from mining ecological restoration, which would be helpful for achieving the goal of carbon neutrality in China.

Dual-mode harvest solar energy for photothermal Cu2-xSe biomineralization and seawater desalination by biotic-abiotic hybrid.

Biotic-abiotic hybrid photocatalytic system is an innovative strategy to capture solar energy. Diversifying solar energy conversion products and balancing photoelectron generation and transduction are critical to unravel the potential of hybrid photocatalysis. Here, we harvest solar energy in a dual mode for Cu2-xSe nanoparticles biomineralization and seawater desalination by integrating the merits of Shewanella oneidensis MR-1 and biogenic nanoparticles. Photoelectrons generated by extracellular Se0 nanoparticles power Cu2-xSe synthesis through two pathways that either cross the outer membrane to activate periplasmic Cu(II) reduction or are directly delivered into the extracellular space for Cu(I) evolution. Meanwhile, photoelectrons drive periplasmic Cu(II) reduction by reversing MtrABC complexes in S. oneidensis. Moreover, the unique photothermal feature of the as-prepared Cu2-xSe nanoparticles, the natural hydrophilicity, and the linking properties of bacterium offer a convenient way to tailor photothermal membranes for solar water production. This study provides a paradigm for balancing the source and sink of photoelectrons and diversifying solar energy conversion products in biotic-abiotic hybrid platforms.

Examining nonlinear effects of socioecological drivers on urban solar energy development in China using machine learning and high-dimensional data.

In the context of carbon neutrality target, renewable energy sources have been transforming from "supplementary energy" to "main energy", which have promoted the green and low-carbon transition of global energy supply system. In-depth analyzing the spatial patterns and driving mechanisms of renewable energy expansion are of significance for optimizing the spatial layout of clean power, and avoiding the phenomenon of wind and solar power curtailment. In this paper, we proposed an ensemble learning model to examine the nonlinear effects of physical geography, resource endowment, and socio-economic factors on solar photovoltaic (PV) capacity at the prefecture-level city scale in China. Using the city-level multi-sources geospatial big data, we extensively collected a total of 175 related explanatory variables and cumulative installed capacity of solar PV power for 295 prefecture-level cities of China. The recursive feature elimination algorithm (SVM-REF) is firstly used to extract the optimal feature subset of urban PV capacity from multi-dimensional features variables. Furthermore, three advanced machine learning models (random forest, decision tree, extreme gradient boosting) are developed to identify the key influencing factors and nonlinear driving effect of urban solar PV power expansion in China. The results show that China's PV installation capacity is highly concentrated in Northern and Northwest parts of China, with the occupancy over 70% in 2019. Moreover, the XGBoost model has the best prediction accuracy (R2 = 0.97) among three methods. We also found that total amount of urban water resources, average solar radiation, and population density are the most important controlling factors for urban solar PV capacity expansion in China, with contribution of 35.6%, 17.7%, and 13.3%, respectively. We suggested that urban solar PV layout mode in China is recommended to gradually shift from resource orientation to the "resource-environment-demand" comprehensive orientation. The paper provides a replicable, scalable machine learning models for simulating solar PV power capacity at the prefecture-level city scale, and serves as a motivation for decision-making reference of the macro siting optimization and sustainable development of China's green power industry.