Gray Approximation Research Articles

Strategies to promote patient-centeredness within the healthcare industry: A grey-based multicriteria decision making methods.

The international policy agenda has recently advocated for the development of patient-centeredness in healthcare service delivery. Consequently, various stakeholders in the healthcare systems have expressed a vital need for identifying strategies and tools that can enhance patient-centeredness. The objective of this paper was to prioritise and benchmark the strategies that can improve patient-centeredness in healthcare service delivery. We employed a multi-stage research scenario that consisted of two phases: a phase including of a scoping review to identify the current strategies to improve patient-centeredness (PC); And, a phase including of a multicriteria best-worst method to assign weights to PC principles, and a questionnaire administered to a sample of experts for benchmarking the strategies derived from the literature using the Grey Multi-Attributive Border Approximation Area Comparison (MABAC-G) method. The most important principle of patient-centeredness was deemed to be access to care, while telehealth tools and Electronic Health Information Systems were respectively suggested as the most efficacious platforms for promoting patient-centeredness. We recommend that administrators and policy makers in the healthcare industry prioritise the implementation and research of strategies such as telehealth tools and electronic health information systems to enhance access and patient-centeredness in the healthcare systems.

Four types of grey [formula omitted]-covering models and their applications

To handle the limitations of a fuzzy β-neighbourhood, many researchers apply this notion to different structures to allow them to make a suitable decision in some real problems. In this article, we introduce the notions of grey β-neighbourhood and grey complementary β-neighbourhood, and then we establish the grey β-covering approximation space (GβCAS). The relevant characteristics are also examined. Furthermore, we construct two new GβCAS models to combine the definitions of a grey β-neighbourhood and a grey complementary β-neighbourhood and explain their relations. In addition, employing grey β-neighbourhoods, we investigate four types of β-neighbourhoods and use them to build four rough approximation models. Hence, in order to give a new approach to MADM in grey β-covering approximation space, we establish a novel methodology based on GCβAS models called multi-attribute grey decision making (MAGDM). Finally, with comparisons to current studies, a numerical example is given to demonstrate the feasibility and usefulness of our suggested methodologies.

The birth and early evolution of a low-mass protostar

Context. Understanding the collapse of dense molecular cloud cores to stellar densities and the subsequent evolution of the protostar is of importance to model the feedback effects such an object has on its surrounding environment, as well as describing the conditions with which it enters the stellar evolutionary track. This process is fundamentally multi-scale, both in density and in spatial extent, and requires the inclusion of complex physical processes such as self-gravity, turbulence, radiative transfer, and magnetic fields. As such, it necessitates the use of robust numerical simulations. Aims. We aim to model the birth and early evolution of a low-mass protostar. We also seek to describe the interior structure of the protostar and the radiative behavior of its accretion shock front. Methods. We carried out a high resolution numerical simulation of the collapse of a gravitationally unstable 1 M⊙ dense molecular cloud core to stellar densities using 3D radiation hydrodynamics under the gray flux-limited diffusion approximation. We followed the initial isothermal phase, the first adiabatic contraction, the second gravitational collapse triggered by the dissociation of H2 molecules, and ≈247 days of the subsequent main accretion phase. Results. We find that the subcritical radiative behavior of the protostar’s shock front causes it to swell as it accretes matter. We also find that the protostar is turbulent from the moment of its inception despite its radiative stability. This turbulence causes significant entropy mixing inside the protostar, which regulates the swelling. Furthermore, we find that the protostar is not fully ionized at birth, but the relative amount of ionized material within it increases as it accretes matter from its surroundings. Finally, we report in the appendix the results of the first 3D calculations involving a frequency-dependent treatment of radiative transfer, which has not produced any major differences with its gray counterpart.

An Adaptive Fuzzy C-Means segmentation and deep learning model for efficient mammogram classification using VGG-Net

Breast cancer mortality can be prevented by only early, accurate mammography screening and diagnosis. Although CNN-based computer-aided diagnosis (CAD) systems for breast cancer have made tremendous progress recently, accurate identification of mammography lesions is still difficult because of poor signal-to-noise ratio (SNR) and physiological features. In this manuscript, an Adaptive Fuzzy C-Means Segmentation and Deep Learning Model for Efficient Mammogram Classification Using VGG-Net (AFCM-DCNN) is proposed. The input image is given to Grey Code Approximation Pre-processing (GCAP) algorithm to enhance the quality of image by adjusting the pixel contrast. The preprocessed image is given to Adaptive Fuzzy C-Means (AFCM) algorithm and is applied in segmenting dominant regions in an input image. But in conventional AFCM technique, the centroid values get generated randomly, which consumes more computational time. Hence to enhance the performance of traditional AFCM, centroid value is optimally chosen by means of optimization algorithm. A technique for classifying images called DCNN analyses the input image and categorizes it as either benign, malignant and normal. The method extracts the features of the image and train with VGG-16 Net classifier. The neurons at the output layer have been designed to compute Class Centric Disease Support (CCDS) towards various classes. Accordingly, the mammogram class is identified towards detecting the brain tumor. The performance of the proposed method AFCM-DCNN exhibits higher accuracy of 29.3%, 25.6% and 24.6%, higher sensitivity of 15.4%, 16.6% compared with the existing methods. Therefore, in future work, hope to enhance the performance depending on transfer learning with similar data.

Rock physics and seismic reflectivity parameterization for amplitude-variation-with-angle inversion of low-maturity source rocks

Rock-physics inversion has been widely used in reservoir prediction. However, the prediction and evaluation of source rocks still predominantly rely on data-driven inversion methods. To directly incorporate the rock-physics relationship into the seismic inversion of source rocks, we first introduce a linearization approximation of the rock-physics model through the first-order approximation of the Taylor series. Model examples and well-logging tests substantiate the reliability of this linear approximation in establishing the connection between the elastic moduli and essential physical parameters (EPPs) of source rocks. Subsequently, we develop a novel equation for the P-wave reflection coefficient by combining the linear rock-physics relationship and Gray approximation. The newly developed reflectivity equation is a linear expression of differences in EPPs of source rocks. Model-simulating results demonstrate a favorable agreement between the novel P-wave reflectivity equation and the exact Zoeppritz equation until the incident angle reaches 60°. Furthermore, we explore the amplitude-variation-with-angle (AVA) effects of EPPs to validate their contributions meet the requirement of seismic inversion. Under the Bayesian scheme, we further develop an AVA inversion method, based on the novel P-wave reflectivity equation, to predict the EPPs of source rocks. Our AVA inversion method obtains satisfactory results under noise testing and demonstrates promising application potential in field examples. The predicted EPPs indicate a strong agreement with the actual well loggings. Comparative analysis against data-driven inversion results reveals that the EPPs predicted by our AVA inversion method possess enhanced rock-physics support and significance. Specifically, this study focuses on isotropic clay-rich source rocks with low maturity.

Acceleration of premixed H[formula omitted]-Air-Steam flames when accounting for thermal radiation

Diluting hydrogen flames with steam can increase flame stability and reduce nitrogen oxide emissions. While the effects of dilution on the flame characteristics due to variations in chemistry or transport properties have been well investigated, this is not the case for those related to thermal radiation. Thermal radiation is indeed often neglected in free flame simulations. However, it can be critical in diluted flames in which participating species are present in the fresh gases. This study explores thermal radiation’s impact on H2-Air flames diluted with steam through 1D laminar flame computations. To this end, a reactive fluid solver is coupled with a semi-analytical thermal radiation code. Both the grey gas approximation, for a preliminary understanding, and the more accurate CK narrow-band model, for detailed simulations that account for spectral-dependent gas properties, are used. The combustion numerical setup is validated against literature results. Three main thermal radiation effects are highlighted: a preheating of the fresh gases, an increased laminar flame speed, and a decreased temperature in the burnt gases. It is found that domain length considerably affects this preheating and flame speed, which can then affects laminar burning velocity measurements. It is shown that thermal radiation and combustion can often be decoupled and that a preheated adiabatic flame can recover the main characteristics of the corresponding radiative flame. The increase of the laminar flame speed by thermal radiation is reported for a wide range of equivalence ratios, dilution levels, and pressures. The observed acceleration is especially significant for lean and very lean flames which can be up to 500% faster when considering thermal radiation.

Global high-order numerical schemes for the time evolution of the general relativistic radiation magneto-hydrodynamics equations

Modeling correctly the transport of neutrinos is crucial in some astrophysical scenarios such as core-collapse supernovae and binary neutron star mergers. In this paper, we focus on the truncated-moment formalism, considering only the first two moments (M1 scheme) within the grey approximation, which reduces Boltzmann seven-dimensional equation to a system of equations closely resembling the hydrodynamic ones. Solving the M1 scheme is still mathematically challenging, since it is necessary to model the radiation-matter interaction in regimes where the evolution equations become stiff and behave as an advection-diffusion problem. Here, we present different global, high-order time integration schemes based on Implicit-Explicit Runge-Kutta methods designed to overcome the time-step restriction caused by such behavior while allowing us to use the explicit Runge-Kutta commonly employed for the magneto-hydrodynamics and Einstein equations. Finally, we analyze their performance in several numerical tests.

Tree-based solvers for adaptive mesh refinement code FLASH – III: a novel scheme for radiation pressure on dust and gas and radiative transfer from diffuse sources

ABSTRACT Radiation is an important contributor to the energetics of the interstellar medium, yet its transport is difficult to solve numerically. We present a novel approach towards solving radiative transfer of diffuse sources via backwards ray tracing. Here, we focus on the radiative transfer of infrared radiation and the radiation pressure on dust. The new module, TreeRay/RadPressure, is an extension to the novel radiative transfer method TreeRay implemented in the grid-based Magneto-Hydrodynamics code Flash. In TreeRay/RadPressure, every cell and every star particle is a source of infrared radiation. We also describe how gas, dust, and radiation are coupled via a chemical network. This allows us to compute the local dust temperature in thermal equilibrium, leading to a significantly improvement over the classical grey approximation. In several tests, we demonstrate that the scheme produces the correct radiative intensities as well as the correct momentum input by radiation pressure. Subsequently, we apply our new scheme to model massive star formation from a collapsing, turbulent core of 150 M⊙. We include the effects of both, ionizing and infrared radiation on the dynamics of the core. We find that the newborn massive star prevents fragmentation in its proximity due to radiative heating. Over time, dust and radiation temperature equalize, while the gas temperature can be either warmer due to shock heating or colder due to insufficient dust–gas coupling. Compared to gravity, the effects of radiation pressure are insignificant for the stellar mass on the simulated time-scale in this work.

Ease of Doing Business: Performance Comparison of G20 Countries Using Gray MCDM

The ubiquity of data, and in particular in MCDM situations, makes it challenging for the Decision Makers (DM) to figure out a way of making proper use of data. This paper presents a three-stage decision framework for DMs to consider the performance range of alternatives holistically. The framework consists of (i) data preparation, (ii) two distance-based Gray Multi-Criteria Decision-Making (MCDM-G) methods using gray interval data to rank the alternatives and (iii) a decision analysis template. For comparison, gray Evaluation based on Distance from Average Solution (EDAS) and gray Multi-Attributive Border Approximation area Comparison (MABAC) methods that rely on arithmetic and geometric mean respectively are used to generate the ranks. The mean-based ranking methods produce stable and efficient ranks in comparison to extremum-based comparison methods, due to their innate nature. The correlation of ranks is analyzed to conclude that the stability of ranks is better when gray interval data is considered. As an example, this paper considers performance range of the 10 criteria used in computing Ease of Doing Business (EDB) index as the gray interval. The sample performance of the G20 countries during the period 2004 to 2020 was used to illustrate the calculations. Further, a general analytic template based on the rank deviation on account of differences in upper and lower bounds of performance helped in classifying the economies as stable leaders, predictable middle and volatile followers. The paper contributes a suitable MCDM and analysis approach when the DM is presented with a gray interval as the alternatives’ performance.

Application of Deep Learning and Speech Recognition Technology for Pedestrian Face Recognition in health sectors

Aiming at the problems of traditional face recognition methods, this paper proposes the application of deep learning and speech recognition technology in pedestrian face recognition. Firstly, the pedestrian face image information is collected, and the face image is decomposed by wavelet scale. The improved detail enhanced face image is obtained, and Harris adaptive threshold corner detection is performed on the enhanced face image. The feature points of pedestrian face image is extracted and matched, and the local radial transformation of points and lines and the Epipolar constraint between multiple planes are adopted. Combined with the constraints of the angle and gray approximation measure of the line features of the face image, the line matching of the face close range image is completed. The 3D line features of the pedestrian face image are extracted and fitted by using the principle of face to face intersection. Combined with the pedestrian face image recognition algorithm, the pedestrian face recognition is realized. The experimental results show that the pedestrian face recognition method based on deep learning and speech recognition technology has better performance.

Large-eddy simulation of lab-scale ethylene buoyant diffusion flames: Effects of subgrid turbulence/soot production interaction and radiation models

Large eddy simulation of the 15-kW ethylene buoyant diffusion flames burning under O2/N2 oxidizers with oxygen concentrations, XO2, of 0.209, 0.168 and 0.152, investigated experimentally at FM Global, are performed by using a flamelet/presumed filtered density function (FDF) combustion model, an acetylene/benzene-based soot production model and the rank-correlated full-spectrum k (RCFSK) with non-grey soot as radiative property model. The presumed FDF considers the spatial intermittent nature of soot and the coupling between soot quantities and mixture fraction. Subgrid-scale (SGS) absorption turbulence/radiation interaction (TRI) is neglected whereas the filtered emission term is modelled from the presumed FDF approach. Model predictions reproduce with fidelity the flame structure, the soot production dynamics and the radiative loss for the three flames. Model results show that soot and radiating gas have a similar contribution to radiation for the flame burning in air and the contribution of radiating gas becomes increasingly dominant with reducing XO2 due to a decrease in soot volume fraction (SVF) throughout the overall flames. A sensitivity study is performed on the influence of the modelling approach of SGS turbulence/soot production interaction (TSI) and radiative heat transfer. Model predictions show that neglecting the coupling between soot quantities and mixture fraction overestimates substantially soot oxidation, resulting in significantly lower soot production and radiative loss. For the present LES where more than 80% of temperature variance is resolved, disregarding the SGS emission TRI has also a significant impact with an overprediction of mean and rms SVF by a factor of two. On the other hand, the approximation of grey soot is valid for these lab-scale flames characterized by moderate optical thicknesses and the use of a non-grey weighted-sum-of-grey-gases (WSGG) model coupled to the grey soot approximation represents an alternative to the full-spectrum k-distribution models.

Numerical study of thermal radiation heat transfer using lattice Boltzmann method

In this work, we present an extension of the lattice Boltzmann method for solving radiative transfer equation (RTE) in emitting, absorbing, and non-scattering gaseous mixtures produced by combustion. A numerical procedure based on LBM is first developed for a single absorption coefficient avoiding spectral effect (gray gas model). Therefore, an infrared radiative global model is associated with the LBM to assess the radiative heat exchanges in molecular gaseous mixtures. A global model based on absorption distribution function (ADF) and a D2Q16 scheme are implemented to solve the 2D RTE. Results are presented in terms of temperature, radiative heat flux, and volumetric radiative power. Effects of radiative behavior and spectral structure of gaseous mixtures are put in evidence. The application of the LBM-ADF8 to gaseous mixture produced by combustion emphasizes the importance of taking into account its spectral structures. The error induced by the gray media approximation is put in evidence.

Publisher's Note: “The gray body approximation for radiative heat transfer in evacuated tube solar collectors: Effects of envelope infrared transparency” [J. Appl. Phys. 131, 125001 (2022)

First Page

The gray body approximation for radiative heat transfer in evacuated tube solar collectors: Effects of envelope infrared transparency

A theoretical and experimental analysis is carried out of radiative heat transfer in the coaxial geometry of evacuated tube solar collectors. The gray body approximation implicit in the use of an effective emissivity does not strictly apply to evacuated tube solar collectors due to selective absorber coating and partially transmitting outer glass in the thermal infrared, especially when constructed from borosilicate. We develop analytic expressions for the heat transfer through the outer envelope and show the equations no longer follow a simple form where an effective emissivity for the system can be defined. To test all approximations in practice, an experiment is performed using an evacuated solar collector manufactured in the 1980s by the Nitto Kohki company in Japan using the effective emissivity approximation to determine the typical heat transfer characteristics using net radiative heat flows in both directions. This method enabled a good fit to temperature–time data for cooling and heating of the inner tube for temperatures between 10 and 85 °C. The results confirm that the effective emittance method can be used in situations with typical glass wall thickness and temperatures. At temperatures greater 100 °C, the spectral distribution of the emitted radiation falls significantly within the transmitting region of the outer glass and can no longer be neglected. The work has verified the stability of the vacuum in this type of collector as the tube still functions well, maintaining a low emissivity and good vacuum after approximately 40 years in storage conditions.

A novel hourly PM2.5 concentration prediction model based on feature selection, training set screening, and mode decomposition-reorganization

Accurate prediction of PM2.5 and other air pollutants concentration can provide early warning information for sustainable urban pollution control, urban construction and travel planning. In this paper, combined with feature selection, training set selection, mode decomposition and reorganization, machine learning, a new PM2.5 concentration hybrid prediction model is established. Firstly, historical data were screened by random forest (RF) and grey system approximation model (GSA). Secondly, the processed data is decomposed by time varying filtering based empirical mode decomposition (TVFEMD). Then, the extreme learning machine (ELM) optimized by moth flame optimization algorithm (MFO) is used for prediction. Based on the data of four cities in Beijing Tianjin Hebei region, the following conclusions can be drawn: (1) The effectiveness and robustness of the proposed model are verified, and the evaluation indexes are the best. (2) RF-GSA can effectively improve the quality of training set. (3) Mode decomposition and reorganization can effectively improve the prediction accuracy. The model can provide a reference for government policy-making and residents travel.

Radiative analysis of luminescence in photoreactive systems: Application to photosensitizers for solar fuel production.

Most chemical reactions promoted by light and using a photosensitizer (a dye) are subject to the phenomenon of luminescence. Redistribution of light in all directions (isotropic luminescence emission) and in a new spectral range (luminescence emission spectrum) makes experimental and theoretical studies much more complex compared to a situation with a purely absorbing reaction volume. This has a significant impact on the engineering of photoreactors for industrial applications. Future developments associated with photoreactive system optimization are therefore extremely challenging, and require an in-depth description and quantitative analysis of luminescence. In this study, a radiative model describing the effect of luminescence radiation on the calculation of absorptance is presented and analyzed with the multiple inelastic-scattering approach, using Monte Carlo simulations. The formalism of successive orders of scattering expansion is used as a sophisticated analysis tool which provides, when combined with relevant physical approximations, convenient analytical approximate solutions. Its application to four photosensitizers that are representative of renewable hydrogen production via artificial photosynthesis indicates that luminescence has a significant impact on absorptance and on overall quantum yield estimation, with the contribution of multiple scattering and important spectral effects due to inelastic scattering. We show that luminescence cannot be totally neglected in that case, since photon absorption lies at the root of the chemical reaction. We propose two coupled simple and appropriate analytical approximations enabling the estimation of absorptance with a relative error below 6% in every tested situation: the zero-order scattering approximation and the gray single-scattering approximation. Finally, this theoretical approach is used to determine and discuss the overall quantum yield of a bio-inspired photoreactive system with Eosin Y as a photosensitizer, implemented in an experimental setup comprising a photoreactor dedicated to hydrogen production.

A Two-moment Radiation Hydrodynamics Scheme Applicable to Simulations of Planet Formation in Circumstellar Disks

Abstract We present a numerical code for radiation hydrodynamics designed as a module for the freely available PLUTO code. We adopt a gray approximation and include radiative transfer following a two-moment approach by imposing the M1 closure to the radiation fields. This closure allows for a description of radiative transport in both the diffusion and free-streaming limits, and is able to describe highly anisotropic radiation transport as can be expected in the vicinity of an accreting planet in a protoplanetary disk. To reduce the computational cost caused by the timescale disparity between radiation and matter fields, we integrate their evolution equations separately in an operator-split way, using substepping to evolve the radiation equations. We further increase the code’s efficiency by adopting the reduced speed of light approximation (RSLA). Our integration scheme for the evolution equations of radiation fields relies on implicit-explicit schemes, in which radiation-matter interaction terms are integrated implicitly while fluxes are integrated via Godunov-type solvers. The module is suitable for general astrophysical computations in one, two, and three dimensions in Cartesian, spherical, and cylindrical coordinates, and can be implemented on rotating frames. We demonstrate the algorithm performance on different numerical benchmarks, paying particular attention to the applicability of the RSLA for computations of physical processes in protoplanetary disks. We show 2D simulations of vertical convection in disks and 3D simulations of gas accretion by planetary cores, which are the first of their kind to be solved with a two-moment approach.

Ballistic-Diffusive Heat Conduction in Thin Films by Phonon Monte Carlo Method: Gray Medium Approximation Versus Phonon Dispersion

Abstract The gray medium approximation treating all phonons with an averaged and representative mean-free-path (MFP) is an often used method in analyzing ballistic-diffusive heat conduction at nanoscale. However, whether there exists a reasonable value of the average MFP which effectively represents the entire spectrum of modal MFPs remains unclear. In this paper, phonon Monte Carlo (MC) method is employed to study the effects of the gray medium approximation on ballistic-diffusive heat conduction in silicon films by comparing with dispersion MC simulations. Four typical ways for calculating the average MFP with gray medium approximation are investigated. Three of them are based on the weighted average of the modal MFPs, and the remaining one is based on the weighted average of the reciprocals of the modal MFPs. The first three methods are found to be good at predicting effective thermal conductivity and heat flux distribution, but have difficulties in temperature profile, while the last one performs better for temperature profile than effective thermal conductivity and heat flux distribution. Therefore, none of the average MFPs can accurately characterize all the features of ballistic-diffusive heat conduction for the gray medium approximation. Phonon dispersion has to be considered for the accurate thermal analyses and modeling of ballistic-diffusive heat transport. Our work could be helpful for further understanding of phonon dispersion and more careful use of the gray medium approximation.

Birth of convective low-mass to high-mass second Larson cores

Context. Stars form as an end product of the gravitational collapse of cold, dense gas in magnetized molecular clouds. This fundamentally multi-scale scenario occurs via the formation of two quasi-hydrostatic Larson cores and involves complex physical processes, which require a robust, self-consistent numerical treatment. Aims. The primary aim of this study is to understand the formation and evolution of the second hydrostatic Larson core and the dependence of its properties on the initial cloud core mass. Methods. We used the PLUTO code to perform high-resolution, one- and two-dimensional radiation hydrodynamic (RHD) core collapse simulations. We include self-gravity and use a grey flux-limited diffusion approximation for the radiative transfer. Additionally, we use for the gas equation of state density- and temperature-dependent thermodynamic quantities (heat capacity, mean molecular weight, etc.) to account for effects such as dissociation of molecular hydrogen, ionisation of atomic hydrogen and helium, and molecular vibrations and rotations. Properties of the second core are investigated using one-dimensional studies spanning a wide range of initial cloud core masses from 0.5 M⊙ to 100 M⊙. Furthermore, we expand to two-dimensional (2D) collapse simulations for a selected few cases of 1 M⊙, 5 M⊙, 10 M⊙, and 20 M⊙. We follow the evolution of the second core for ≥100 years after its formation, for each of these non-rotating cases. Results. Our results indicate a dependence of several second core properties on the initial cloud core mass. Molecular cloud cores with a higher initial mass collapse faster to form bigger and more massive second cores. The high-mass second cores can accrete at a much faster rate of ≈10−2 M⊙ yr−1 compared to the low-mass second cores, which have accretion rates as low as 10−5 M⊙ yr−1. For the first time, owing to a resolution that has not been achieved before, our 2D non-rotating collapse studies indicate that convection is generated in the outer layers of the second core, which is formed due to the gravitational collapse of a 1 M⊙ cloud core. Additionally, we find large-scale oscillations of the second accretion shock front triggered by the standing accretion shock instability, which has not been seen before in early evolutionary stages of stars. We predict that the physics within the second core would not be significantly influenced by the effects of magnetic fields or an initial cloud rotation. Conclusions. In our 2D RHD simulations, we find convection being driven from the accretion shock towards the interior of the second Larson core. This supports an interesting possibility that dynamo-driven magnetic fields may be generated during the very early phases of low-mass star formation.

Short-term photovoltaic power generation forecasting based on random forest feature selection and CEEMD: A case study

To mitigate solar curtailment caused by large-scale development of photovoltaic (PV) power generation, accurate forecasting of PV power generation is important. A hybrid forecasting model was constructed that combines random forest (RF), improved grey ideal value approximation (IGIVA), complementary ensemble empirical mode decomposition (CEEMD), the particle swarm optimization algorithm based on dynamic inertia factor (DIFPSO), and backpropagation neural network (BPNN), called RF-CEEMD-DIFPSO-BPNN. PV power generation is affected by many factors. The RF method is used to calculate the importance degree and rank the factors, then eliminate the less important factors. Then, the importance degree calculated by RF is transferred as the weight values to the IGIVA model to screen the similar days of different weather types to improve the data quality of the training sets. Then, the original power sequence is decomposed into intrinsic mode functions (IMFs) at different frequencies and a residual component by CEEMD to weaken the fluctuation of the original sequence. We empirically analyzed a PV power plant to verify the effectiveness of the hybrid model, which proved that the RF-CEEMD-DIFPSO-BPNN is a promising approach in terms of PV power generation forecasting.