CN-121983947-A - Photovoltaic prediction method for high-precision satellite cloud image and ground monitoring fusion

CN121983947ACN 121983947 ACN121983947 ACN 121983947ACN-121983947-A

Abstract

The invention relates to the technical field of new energy and weather forecast, in particular to a photovoltaic prediction method for fusing high-precision satellite cloud image and ground monitoring, which comprises the steps of firstly acquiring satellite remote sensing cloud image data and ground weather monitoring data; the method comprises the steps of deducing an expected meteorological state based on satellite data, determining real-time confidence coefficient of each monitoring point through dynamic comparison of ground monitoring data and the expected state, then correcting the background field by using observation data weighted by the confidence coefficient by taking the satellite data as the background field to generate a fusion meteorological field, finally deducing future meteorological element change based on a fusion result, and obtaining a power prediction value through photovoltaic conversion.

Inventors

LIU CHENGYU
Lian Qifei
WU WEI
CHEN YUNPING
LONG HAIQUAN
LIU SHAOHUA
HU ZHONGHUA
HAN GUANG
WEI QINGHAI
LIU KUNHAO

Assignees

江西大唐国际新余发电有限责任公司
中国大唐集团科学技术研究总院有限公司中南电力试验研究院

Dates

Publication Date: 20260505
Application Date: 20251202

Claims (10)

1. The photovoltaic prediction method for the fusion of the high-precision satellite cloud picture and the ground monitoring is characterized by comprising the following steps of: S1, acquiring satellite remote sensing cloud image data and ground meteorological monitoring data in a target area; S2, deducing expected meteorological states of a target area based on satellite remote sensing cloud image data, comparing the ground meteorological monitoring data with the expected meteorological states in a consistent mode, and determining real-time confidence of each monitoring point according to comparison results; S3, taking satellite remote sensing cloud image data as a background field, taking all ground weather monitoring data as observation points, controlling the assimilation intensity of all the observation points through real-time confidence, dynamically correcting the background field, and generating a fusion gas image field of a target area; and S4, calculating the evolution of the meteorological elements in the future period of the target area based on the fusion meteorological field, and obtaining the predicted value of the output power of the photovoltaic power station through a preset conversion relation according to the evolution of the meteorological elements.
2. The method for photovoltaic prediction by fusion of high-precision satellite cloud image and ground monitoring according to claim 1, wherein the step S2 comprises the following steps: S21, analyzing the space-time evolution characteristics of cloud layers in a target area based on satellite remote sensing cloud image data to generate an expected meteorological state sequence taking grid units as units; s22, extracting ground meteorological monitoring data of the current moment and adjacent historical time periods of each ground monitoring point to form a monitoring data sequence, and carrying out point-by-point deviation calculation on the monitoring data sequence and an expected meteorological state sequence of a corresponding grid unit to obtain data deviation values of all the monitoring points; S23, according to the data deviation amount, evaluating the abnormal degree of the data of each monitoring point by combining the spatial distribution consistency of the data deviation of adjacent monitoring points in the target area; S24, converting the degree of abnormality into a standardized confidence score, and directly giving corresponding real-time confidence to each monitoring point according to the score.
3. The method for photovoltaic prediction by fusion of high-precision satellite cloud image and ground monitoring according to claim 2, wherein S23 comprises the following steps: s231, constructing a space topological relation containing adjacent monitoring points by taking a target monitoring point as a center, and constructing a space association network according to geographic coordinates of each monitoring point; s232, calculating a space consistency index of the data deviation amount of the target monitoring point and each adjacent monitoring point based on a space correlation network; S233, calculating to obtain an abnormal degree quantization value of the target monitoring point by combining the data deviation amount and the space consistency index of the target monitoring point; S234, carrying out normalization processing on the abnormal degree quantized values, mapping the abnormal degree quantized values to a preset abnormal degree level interval, and finally outputting abnormal degree evaluation results corresponding to all monitoring points.
4. The method for photovoltaic prediction by fusion of high-precision satellite cloud image and ground monitoring according to claim 3, wherein the step S232 specifically comprises: s2321, calculating the spatial dispersion of the data deviation amount of the target monitoring point and each adjacent monitoring point based on the geographic distribution characteristics of each adjacent monitoring point in the spatial correlation network; s2322, establishing an azimuth weighting coefficient matrix according to the relative azimuth relation between the target monitoring point and each adjacent monitoring point; S2323, combining the spatial dispersion and the azimuth weighting coefficient matrix, and calculating to obtain a spatial consistency index of the target monitoring point through a spatial consistency algorithm; s2324, carrying out standardization processing on the space consistency index, limiting the space consistency index to a preset evaluation range, and outputting a final space consistency index value.
5. The method for photovoltaic prediction by fusion of high-precision satellite cloud image and ground monitoring according to claim 1, wherein the step S3 comprises the following steps: S31, constructing a plurality of meteorological state scenes, wherein each meteorological state scene is formed by introducing small-amplitude random disturbance with preset amplitude to a background field to form an initial scene set; S32, comparing the ground meteorological monitoring data with the numerical value of each scene at the corresponding observation point, and adjusting the influence weight of each observation point in each scene according to the real-time confidence; S33, calculating the overall matching degree of each scene and all observation data based on the adjusted influence weights, sorting the importance of each scene according to the matching degree, and screening out an optimal matching scene subset; And S34, synthesizing all scenes in the optimal matching scene subset, extracting common characteristics of the scenes, eliminating random disturbance influence, and generating a fusion gas image field of the target area.
6. The method for photovoltaic prediction by fusion of high-precision satellite cloud image and ground monitoring according to claim 5, wherein the step S31 specifically comprises: Determining main meteorological parameters for disturbing a background field and a corresponding disturbance amplitude range based on meteorological element space distribution characteristics obtained by analyzing satellite remote sensing cloud image data; According to the correlation characteristics of meteorological elements in space, a disturbance mode with spatial structural characteristics is designed; Scaling the disturbance mode according to a preset amplitude level to generate a plurality of disturbance fields with different intensity levels, and respectively superposing each disturbance field with a background field to form an initial scene set.
7. The method for photovoltaic prediction by fusion of high-precision satellite cloud image and ground monitoring according to claim 5, wherein S33 specifically comprises: S331, respectively calculating the matching degree of the ground meteorological monitoring data at each observation point for each scene, and calculating the weighted comprehensive matching degree of each scene by combining the influence weights of the observation points; S332, constructing a scene ordering sequence based on the weighted comprehensive matching degree of each scene, simultaneously analyzing the consistency characteristics of each scene on the spatial distribution of the key meteorological elements, and identifying scene clusters with similar spatial distribution modes; s333, selecting a scene with highest weighted comprehensive matching degree from each scene cluster by combining scene cluster analysis results on the basis of the scene sequencing sequence to form a scene subset for preliminary screening; s334, performing spatial smoothness test on the preliminarily screened scene subset, eliminating scenes with obvious abnormal mutation in spatial distribution, and determining the rest scenes as the optimal matching scene subset.
8. The method for photovoltaic prediction by fusion of high-precision satellite cloud image and ground monitoring according to claim 7, wherein the step S333 specifically comprises the following steps: S3331, calculating the consistency intensity of the spatial distribution among the scenes in each scene cluster, and identifying the scene clusters with stable spatial distribution characteristics; s3332, adjusting the priority order of the scene clusters according to the spatial distribution consistency intensity of each scene cluster; S3333, sequentially selecting scenes with highest weighted comprehensive matching degree from all scene clusters according to the adjusted priority order, and simultaneously ensuring that the selected scenes meet preset requirements on the evolution continuity of meteorological elements; S3334, sorting the selected scenes according to the priorities of the scene clusters to which the selected scenes belong to form a primary screening scene subset with a hierarchical structure.
9. The photovoltaic prediction method of the fusion of the high-precision satellite cloud image and the ground monitoring according to claim 1, wherein the step S4 specifically comprises the following steps: S41, generating a plurality of meteorological element evolution sequences along different meteorological evolution paths respectively based on a fusion meteorological field to form a multi-stage power prediction sequence; S42, calculating the weather element variation coordination of each predicted sequence at a key time node, evaluating the space-time consistency index of each sequence, and identifying a predicted path with the optimal coordination; S43, carrying out dynamic weight distribution on each predicted sequence according to the space-time consistency index, and comprehensively scoring by combining the prediction accuracy of each sequence in the history synchronization; and S44, performing optimal prediction track synthesis based on the comprehensive scores of the prediction sequences, and obtaining a final photovoltaic power station output power predicted value through track smoothing processing.
10. The method for photovoltaic prediction by fusion of high-precision satellite cloud image and ground monitoring according to claim 9, wherein S44 comprises the following steps: s441, constructing an initial predicted track based on the comprehensive scores of all the predicted sequences, and selecting the predicted sequence with the highest comprehensive score as a reference track; S442, performing time dimension coordination analysis on the initial predicted track, identifying mutation points which do not accord with the continuous evolution rule of the meteorological elements in the initial predicted track, and recording the positions and the amplitude characteristics of the mutation points; s443, dynamically adjusting the initial predicted track according to the characteristics of the mutation points; And S444, performing photovoltaic power conversion verification on the adjusted initial predicted track, ensuring that the power predicted value is within the actual operation range of the photovoltaic power station, and outputting a final photovoltaic power station output power predicted value.

Description

Photovoltaic prediction method for high-precision satellite cloud image and ground monitoring fusion Technical Field The invention relates to the technical field of new energy and weather forecast, in particular to a photovoltaic prediction method for integrating high-precision satellite cloud pictures and ground monitoring. Background With the increasing of the duty ratio of the photovoltaic power generation in the energy structure, the output characteristic of the photovoltaic power generation is obviously influenced by meteorological conditions, and accurate prediction of the photovoltaic power becomes a key for guaranteeing the stable operation of a power grid. At present, the photovoltaic prediction technology mainly depends on satellite remote sensing cloud pictures or ground monitoring data, the satellite data has wide area coverage advantages but limited local precision, and the ground monitoring can provide accurate punctiform data and is easily interfered by local environments. In the existing method, although the two are combined, the method stays at the level of simple data superposition or static weight fusion, the space-time dynamic change of the ground monitoring data quality cannot be fully considered, particularly, under the complex weather condition, the local sensor fault or abnormal data is easy to cause the distortion of a prediction result, and the further improvement of the prediction precision is restricted. The existing photovoltaic prediction method lacks a dynamic evaluation and fault tolerance mechanism for ground data quality when integrating satellite and ground monitoring data. When abnormal data is generated by the local sensor due to temporary shielding, equipment failure and the like, the traditional fusion algorithm treats the abnormal data with normal data, so that abnormal values are brought into the fusion process, and the whole prediction field is polluted. The single-point fault propagates in space-time dimension through a data assimilation chain, so that local distortion and even systematic deviation of irradiance and power prediction are caused, the reliability of a prediction result is seriously influenced, and the actual requirements of a power grid on high-precision and high-reliability photovoltaic prediction cannot be met. Disclosure of Invention The invention aims to provide a photovoltaic prediction method for fusing high-precision satellite cloud pictures and ground monitoring so as to solve the problems in the background. The aim of the invention can be achieved by the following technical scheme: a photovoltaic prediction method for high-precision satellite cloud image and ground monitoring fusion comprises the following steps: S1, acquiring satellite remote sensing cloud image data and ground meteorological monitoring data in a target area; S2, deducing expected meteorological states of a target area based on satellite remote sensing cloud image data, comparing the ground meteorological monitoring data with the expected meteorological states in a consistent mode, and determining real-time confidence of each monitoring point according to comparison results; S3, taking satellite remote sensing cloud image data as a background field, taking all ground weather monitoring data as observation points, controlling the assimilation intensity of all the observation points through real-time confidence, dynamically correcting the background field, and generating a fusion gas image field of a target area; And S4, calculating the evolution of the meteorological elements in the future period of the target area based on the fusion meteorological field, and obtaining the predicted value of the output power of the photovoltaic power station through a preset conversion relation according to the evolution of the meteorological elements. As a further scheme of the invention, the S2 comprises the following steps: S21, analyzing the space-time evolution characteristics of cloud layers in a target area based on satellite remote sensing cloud image data to generate an expected meteorological state sequence taking grid units as units; s22, extracting ground meteorological monitoring data of the current moment and adjacent historical time periods of each ground monitoring point to form a monitoring data sequence, and carrying out point-by-point deviation calculation on the monitoring data sequence and an expected meteorological state sequence of a corresponding grid unit to obtain data deviation values of all the monitoring points; S23, according to the data deviation amount, evaluating the abnormal degree of the data of each monitoring point by combining the spatial distribution consistency of the data deviation of adjacent monitoring points in the target area; S24, converting the degree of abnormality into a standardized confidence score, and directly giving corresponding real-time confidence to each monitoring point according to the score. As a further scheme of the invention, th