164 results about "Solar irradiance" patented technology

A system and method are disclosed for AI-orchestrated multi-source renewable energy harvesting and decentralized trading. A plurality of energy harvesting nodes convert renewable resources, including solar irradiance, wind, hydrodynamic flow, salinity gradients, geothermal heat, and waste-heat streams, into electrical power. Each node includes a sensor array that measures operating and environmental parameters and an edge AI processing unit that predicts performance degradation and computes control actions to maximize net power output subject to stress constraints. A power conversion subsystem conditions the generated power for delivery to storage, microgrids, or utility grids. A blockchain-based orchestration layer receives validated energy summaries, tokenizes discrete energy quanta as digital energy tokens with provenance metadata, and executes smart contracts for peer-to-peer trading, dynamic pricing, and programmable revenue distribution. A fleet-level AI coordination module performs federated learning across nodes and coordinates dispatch and curtailment among heterogeneous modalities, improving portfolio efficiency, grid stability, and environmental accounting.

The invention discloses a solar irradiance prediction system and method based on dual attention multi-modal fusion. The method comprises the following steps: performing spatial feature extraction on a sky imaging observation map after preprocessing and time synchronization by using a MobileNetV2 network to obtain a sky imaging time sequence feature sequence; inputting the sky imaging time sequence feature sequence into a DAT model to obtain a global sky imaging time sequence feature sequence, a local sky imaging time sequence feature sequence and a local convolution position coding residual term; obtaining an aggregated sky imaging time sequence feature sequence according to the global sky imaging time sequence feature sequence, the local sky imaging time sequence feature sequence and the local convolution position coding residual item; performing time-dependent modeling on the meteorological observation data by adopting a long short-term memory network to obtain a meteorological observation data feature sequence; and performing feature fusion on the aggregated sky imaging time sequence feature sequence and the meteorological observation data feature sequence through a trans-attention mechanism to obtain a fused joint feature sequence for solar irradiance prediction.

The invention discloses a distributed photovoltaic ultra-short-term solar irradiance prediction method based on multi-scale spatial-temporal feature fusion, and the method comprises the steps: constructing a multi-source spatial adjacency matrix through fusing a geographic distance adjacency matrix, a historical correlation matrix and a dynamic adjacency matrix generated by a graph self-attention network; therefore, the inter-site spatial dependency relationship is dynamically captured. Meanwhile, multi-scale time features are extracted in combination with a convolutional long-short-term memory network, dynamic weighted integration of spatial-temporal features is finally realized through a gating fusion unit, and high-precision prediction of future ultra-short-term irradiance is completed. A verification result on a certain eight distributed photovoltaic station data set shows that the model is remarkably superior to a traditional neural network model under different weather conditions, and particularly, the model shows higher adaptability and prediction precision in sudden change weather and an irradiance peak value interval; and reliable technical support can be provided for distributed photovoltaic power station power prediction and power grid dispatching.

The invention relates to the technical field of irradiance estimation, in particular to an irradiance estimation method and device, electronic equipment and a storage medium, and the method comprises the following steps: obtaining ground site historical data of a research region, reanalysis data of the research region and an aerosol public data set; dividing weather types into clear sky samples and cloudy sky samples according to the obtained historical ground station data of the research area; inputting the clear sky sample, the reanalysis data and the aerosol public data set into a solar irradiance model to calculate clear sky expected irradiance GHIcs; obtaining observation data of a geostationary orbit meteorological satellite, and estimating a cloud cover coefficient CI by using satellite reflectivity; inputting the multi-cloud sky sample and the cloud cover coefficient CI into a cloud index-based semi-empirical model, and calculating a clear sky coefficient Kc; and multiplying the clear sky expected irradiance GHIcs by the clear sky coefficient Kc to obtain an irradiance estimation value under the all-sky condition. Through the improvement of the method, the regional adaptability and the estimation accuracy of irradiance estimation are improved.

The invention relates to the technical field of power control, in particular to a photovoltaic hydrogen production power feed-forward control method and system based on shadow space-time prediction. The method comprises the following steps: constructing a spatio-temporal data acquisition system, acquiring solar irradiance, atmospheric temperature, cloud layer motion trail and component surface shadow distribution data of a photovoltaic array area through a multi-dimensional sensor network, dividing a photovoltaic array into a plurality of monitoring units, and performing spatio-temporal alignment processing on real-time data, and forming a standardized data set comprising the spatial position, the timestamp and the multi-dimensional physical quantity. By constructing a multi-dimensional spatio-temporal data acquisition system, high-precision real-time sensing of photovoltaic array area shadow distribution and environmental parameters is realized, a prediction architecture fused with spatio-temporal characteristics is combined, spatial correlation between monitoring units and a time sequence rule of shadow migration are effectively captured, and prospective input is provided for power control.

The invention provides a photovoltaic power station generating capacity prediction method, and belongs to the technical field of photovoltaic power stations, and the method comprises the steps: carrying out the feature extraction of a photovoltaic module image, building a health state feature vector, inputting a multi-scale meteorological feature into a multi-scale meteorological fusion prediction model, and outputting a solar irradiance prediction sequence; and inputting the component health state feature vector into a component attenuation dynamic tracking model to output a power attenuation coefficient, calculating an initial power generation prediction value according to irradiance prediction and the attenuation coefficient, carrying out weighted fusion, and carrying out Kalman filtering smoothing processing and Box-Cox conversion equalization processing on a prediction deviation sequence to obtain a final prediction result. The technical problem that the prediction precision is insufficient due to the fact that photovoltaic power generation capacity prediction cannot give consideration to multi-time-scale meteorological changes and long-term attenuation characteristics of assemblies at the same time is solved.

The invention relates to the technical field of power prediction of a power system, and discloses a sample adaptive iterative training method and system for existing model tuning, and the method comprises the steps: obtaining the net load and solar irradiance data of a power distribution network; determining a payload-irradiance response intensity indicator based on the response slope within the local window; determining reference values of a truncation mode and a response mode according to statistical distribution of the indexes, and calculating a non-truncation probability of the sample in a normal power generation state; calculating the marginal response intensity of the prediction model to the solar irradiance; constructing a critical factor for capturing an inverter action boundary and a sample training weight by using a non-truncation probability; constructing a photovoltaic bistable constraint item, and constraining marginal response intensity to approach corresponding modal reference values in different states; and finally, performing parameter updating on the model based on the weighted prediction error and the bistable constraint term. According to the method, the problem of model tuning failure caused by equipment control truncation is effectively solved, and the prediction performance of the model under different operation conditions is ensured.

Lowest cost usage scheduling of an energy system during a time interval of interest is achieved by utilizing two components of learning and optimization. First, several learning methods are used to forecast energy production and if needed energy consumption of a given group of energy components as a function of historical production data, weather, and solar irradiance data collected from weather and geo reports. A classification approach is used to select the learning model. A classification approach is used to select the learning model and approach. Then, an optimization problem is formulated and solved to create the optimal schedule of energy usage during time intervals of interests for the given group of energy components subject to scheduling and equipment constraints. Accordingly, integrated iterative methods, programs, and systems are described aiming at minimizing the cost of energy consumption for the given group of energy components within time intervals of interest.

The invention relates to the technical field of atmospheric electric field simulation, and discloses an atmospheric electric field diurnal variation simulation method and system based on solar irradiance, and the method comprises the steps: obtaining historical atmospheric electric field data and synchronous environmental factor data, employing the time sequence analysis and neural network technology to extract time sequence features, generating a coupling data set, and obtaining a coupling data set; a multi-parameter model is constructed based on the coupled data set, and the multi-parameter model can accurately simulate the hysteresis effect of environmental factors on the atmospheric electric field through optimization of a convolutional neural network and a memory kernel function; independent component analysis is adopted to separate direct and scattering components of irradiance, a component action vector is generated, and the prediction performance of a multi-parameter model can be further optimized; according to the invention, efficient simulation and accurate prediction of the daily variation of the atmospheric electric field can be realized.

The invention discloses a multi-energy system optimization method and system based on hydrogen energy storage, and belongs to the field of multi-energy system optimization, and the method comprises the steps: obtaining the historical wind speed, wind direction and solar irradiance data of an anemometer tower and a photovoltaic power station, training a time sequence analysis model through the historical wind speed, wind direction and solar irradiance data, and obtaining a time sequence analysis model; wind energy power and solar energy power prediction curves in a future time period are obtained; the method comprises the following steps: acquiring pressure and temperature data of a hydrogen storage tank, inputting the pressure and temperature data into a pre-established leakage rate dynamic compensation algorithm to obtain a real-time hydrogen loss amount, and if the hydrogen loss amount is greater than a preset threshold value, reducing the operation pressure or temperature of the hydrogen storage tank; and according to the wind and light power prediction curve, the hydrogen production equipment operation data, the hydrogen storage tank operation parameters, the hydrogen conversion equipment control signal, the constraint model and the income cost model as input of a linear programming algorithm, an equipment capacity configuration scheme for maximizing the system economic benefits is obtained.

The invention discloses a pollution emission monitoring and early warning method based on physical prior and information value driving, and relates to the technical field of environment monitoring and pollution control, and the method comprises the following steps: collecting production side certificate data, executing time alignment and self-calibration drift removal, and constructing a standardized multi-source database; judging whether a heat island effect occurs or not, and if yes, collecting volume heat capacity, solar irradiance and heat island intensity data of a single building; according to the method, the heat island effect is introduced to judge and generate a new time-space diagram network, real-time heat island intensity changes caused by building materials and solar irradiation can be dynamically captured, and building wind field and plume data are accurately corrected. The problem that a conventional method cannot adapt to irregular fluctuation of vertical wind shear caused by the real-time heat island effect is solved, the pollution plume low-altitude compression phenomenon caused by the heat island effect is effectively corrected, and the monitoring direction is always aligned with the real diffusion path of pollutants.

A system implements a weather-based irradiance forecasting algorithm that can aid in planning PV array operations. The system implements a cascaded temporal convolutional network (TCN) neural architecture that uses nine weather characteristics in a time-bound window to predict future solar irradiance. The system achieves a significant performance improvement with as little as a single day prior of weather data compared to baseline results.

The invention relates to the technical field of photovoltaic system prediction, in particular to a multi-mode prediction method and system for photovoltaic panel generation power, electronic equipment and a medium. According to the method, illumination data, atmospheric pollutant concentration data and meteorological parameters of the surface of the photovoltaic panel are obtained, and a real-time cleanliness index and an atmospheric transparency correction coefficient of the photovoltaic panel are calculated according to the illumination data, the atmospheric pollutant concentration data and the meteorological parameters; thirdly, inputting various data and parameters of the surface of the photovoltaic panel into the multi-modal prediction model, and outputting direct solar irradiance; and finally, calculating to obtain a power generation power prediction value based on the direct solar irradiance. By introducing a dual correction mechanism of a real-time cleanliness index and an atmospheric transparency correction coefficient, the multi-modal prediction model can dynamically adapt to environmental changes of different pollution degrees, and the problems of poor environmental adaptability and low prediction precision existing in a current photovoltaic power generation power prediction technology are solved. And the accuracy and environmental adaptability of photovoltaic power generation power prediction under the polluted weather condition are improved.

The invention discloses a day-ahead solar irradiance data correction method, and the method comprises the steps: screening historical numerical weather forecast data of the same weather type based on the weather type of day-ahead weather forecast; calculating a Spearman correlation coefficient and a variance of daily solar irradiance, dividing historical days into different clustering groups through a Kmeans clustering algorithm, and obtaining a clustering center; determining a cluster group to which the day-ahead numerical forecast belongs, and selecting historical data with matched weather types and groups to construct a training set; training a COSTLSTM model capable of decomposing an input sequence trend and a seasonal component to obtain a correction model; and inputting the numerical weather data of the day-ahead weather forecast into the trained solar irradiance correction model, and outputting to obtain the corrected day-ahead solar irradiance. Compared with the prior art, the method can effectively cope with complex mixed modes and seasonal periodic differences in the solar irradiance, and realizes accurate data correction of the day-ahead solar irradiance in complex weather.

The invention provides a distributed photovoltaic power generation capacity prediction method and system based on XGBoost, and relates to the technical field of photovoltaic power generation. Historical power generation capacity data of a distributed photovoltaic power station and hour-level solar irradiance data of a corresponding geographic position are collected, periodic mapping of a [0, 2pi] interval is carried out on a timestamp, sine and cosine characteristics are calculated, and the power generation capacity of the distributed photovoltaic power station is predicted. And taking the processed time period characteristics and solar irradiance data as input, and training an XGBoost regression model to realize generating capacity prediction. The method does not need an extra abnormal point screening step, naturally has robustness by means of a weighted tree integration mechanism of XGBoost, is light in model structure and high in training and prediction speed, remarkably reduces computing resource consumption while ensuring more than 95% of prediction accuracy, is suitable for a multi-site distributed deployment scene, and has a wide application prospect. And intelligent power grid dispatching and distributed energy management can be effectively supported.

The invention discloses a preparation method and application of a wood-based solar interface evaporator, and belongs to the technical field of solar evaporation seawater desalination. Natural wood is used as a raw material, and super-hydrophilic wood aerogel (WA) is prepared through delignification and freeze drying treatment; grafting the MOF material to the surface of the WA porous channel through an in-situ grafting technology to obtain MOF-coated WA; lignin is used as a raw material, and porous lignin carbon (PLC) is prepared through treatment such as high-temperature carbonization and activation; and dispersing PLC in a chitosan / acetic acid aqueous solution, coating the upper surface of the MOF-coated WA with the solution, and drying to obtain the PLC-coated MOF-coated WA wood-based evaporator. The evaporator has efficient photo-thermal conversion performance and evaporation rate, can be used for solar energy driven water evaporation, has the evaporation rate of 2.5594 kg.m <-2 >. H <-1 > under one solar illumination intensity (1kW / m < 2 >), can efficiently purify seawater, and is low in cost.

The invention provides a photoelectric solar irradiance meter self-calibration device for outdoor monitoring and a calibration method, and belongs to the technical field of photovoltaic monitoring. The device comprises a sky imager, a solar irradiance meter for monitoring, a standard solar irradiance meter, a sealed darkroom, a three-dimensional adjusting bracket and a waterproof sealed box, wherein a dual-channel temperature collector, a dual-channel voltage collector and a control, data processing and storage module are integrated in the waterproof sealed box. The sealed darkroom normally protects the standard irradiance meter to prevent the standard irradiance meter from aging and dust deposition; the dual-channel collector synchronously collects temperature and voltage of a standard solar irradiance meter for monitoring, and supports a calibration algorithm to realize dynamic updating of sensitivity; the three-dimensional adjusting bracket ensures that the device is parallel to the mounting plane of the photovoltaic module, and the angle response matching degree is improved; the sky imager judges the calibration weather condition to guarantee the effectiveness of the calibration environment; the control module realizes automation of a self-calibration process, adapts to an outdoor long-term monitoring scene, and realizes continuous and accurate measurement of photovoltaic solar irradiance.

The invention discloses a short-term earth surface solar irradiance prediction method and system in the technical field of solar energy resource prediction and artificial intelligence crossing. The method comprises the following steps: acquiring advanced calibration instant extraterrestrial horizontal plane solar irradiance auxiliary data according to a longitude and latitude list of a target area and set advanced time; constructing image modal input data according to satellite image data of a target area and the advanced calibration instantaneous extraterrestrial horizontal plane solar irradiance auxiliary data; and according to historical earth surface irradiance time sequence data and the image modal input data, performing prediction by using the trained multi-modal fusion network to obtain a short-term earth surface solar irradiance prediction result. According to the invention, by introducing advanced calibration of instantaneous extraterrestrial horizontal plane solar irradiance auxiliary data and a multi-mode deep fusion technology, the precision and robustness of short-term solar irradiance prediction are effectively improved, and the method is suitable for photovoltaic power generation power prediction and power grid dispatching.

The invention belongs to the technical field of quantitative remote sensing, and relates to a mid-infrared hyperspectral emission and reflection characteristic integrated inversion method, which comprises the following steps: acquiring mid-infrared hyperspectral satellite remote sensing data, and optimizing sensing entrance pupil radiance data to obtain optimized entrance pupil radiance data; the intermediate infrared hyperspectral satellite remote sensing data comprises sensing entrance pupil radiance data; performing atmospheric correction on the optimized entrance pupil radiance data by using a radiation transfer model to obtain surface brightness temperature and atmospheric downward radiation, and calculating solar irradiance reaching the surface; constructing a mid-infrared hyperspectral earth surface bi-directional reflectivity inversion framework, and performing earth surface bi-directional reflectivity inversion based on the mid-infrared hyperspectral earth surface bi-directional reflectivity inversion framework to obtain earth surface bi-directional reflectivity and earth surface equivalent radiance; constructing a scene self-adaptive framework, and performing inversion based on the scene self-adaptive framework to obtain the surface temperature and the surface emissivity; therefore, accurate separation of surface emission radiation and surface reflection radiation is completed.

The invention discloses an intelligent cooperative control and energy optimization method of a transcritical CO2 refrigeration system. The method is realized by executing the following steps: acquiring an environment temperature, solar irradiance, system high-pressure side pressure and a user cooling capacity demand signal in real time; based on the environment temperature and the cooling capacity demand signal, the optimal high pressure set value under the current working condition is dynamically calculated through an energy efficiency optimization model; by adjusting the opening degree of a throttling element of the system and the operation frequency of a compressor, the pressure of the high-pressure side of the system tracks the optimal high-pressure pressure set value; and meanwhile, according to the solar irradiance, the power grid electricity price signal and a preset electric energy scheduling strategy, a power supply source is dynamically decided so as to preferentially utilize photovoltaic electric energy to drive the system to operate. The energy efficiency optimization and economical operation of the transcritical CO2 refrigerating system under the variable working conditions are achieved.

Sun sensor for a vehicle (100), vehicle with the sun sensor, method for operating the vehicle, wherein the sun sensor comprises several module cells of a photovoltaic module (101) of a photovoltaic system for the vehicle (100), wherein the sun sensor is configured to detect and / or output a sensor value, in particular a radiant power, a voltage, and / or a current, wherein the sensor value characterizes a solar irradiance into a module cell or a solar irradiance into several module cells.

This invention discloses a wastewater treatment system coupled with a solar PV / T-driven photocatalytic and microbial degradation method. This system can degrade dibutyl phthalate (DBP), a recalcitrant substance in wastewater, while simultaneously generating electricity and heat. The system includes a solar PV / T module, a photocatalytic module, and a bioreactor module. The solar PV / T module and the photocatalytic module are integrated into one unit. The PV / T module includes a substrate, an EVA material layer, photovoltaic cells, a solar collector, water pipes, a battery, an inverter, and a hot water storage tank. The photocatalytic module includes a photocatalytic reactor and a pollutant concentration sensor. This invention couples photocatalysis with a microbial method, utilizing renewable solar energy for wastewater treatment. Simultaneously, it couples the wastewater treatment system with a wastewater treatment plant, fully utilizing the electricity and heat generated by the photovoltaic cells within the wastewater treatment plant. Furthermore, when solar irradiance is insufficient, waste heat and electricity from the wastewater treatment plant are connected to the system to ensure normal operation, creating a complementary system.

This disclosure provides a digital twin system and method for solar high-temperature heat pump steam supply with energy storage coupling, belonging to the field of solar high-temperature heat pump technology. The system includes a data acquisition module, a time-series prediction module, a digital twin module, a system control module, and a feedback verification module. The data acquisition module collects and preprocesses historical and real-time multi-source data. The time-series prediction module predicts solar irradiance and heat consumption at multiple future moments based on machine learning. The digital twin module calculates future terminal parameters using real-time and predicted data based on a physical information model. The system control module generates and executes a heat pump advance control scheme based on optimization objectives. The feedback verification module compares actual and predicted parameters to correct the digital twin model. This invention achieves proactive prediction, advance control, and closed-loop optimization of industrial steam supply systems, solving the control lag and energy-saving optimization problems caused by solar energy volatility and complex system coupling.

Deep learning is used to train a neural network that predicts short-term solar irradiance (e.g., 20 minutes or less) from camera images and metadata. The neural network architecture includes a recurrent network to account for time. Images and metadata are fed into the neural network at different points. The resulting neural network, trained using machine learning, predicts solar irradiance from the camera images and metadata, enabling the control of a solar power plant and backup power supply to minimize variations in output power.

The application provides an XGBoost-based distributed photovoltaic power generation amount prediction method and system, relates to the photovoltaic power generation technical field, and collects historical power generation amount data of a distributed photovoltaic power station and corresponding hourly solar irradiance data of a geographical position, performs periodic mapping of a [0, 2pi] interval on a time stamp, and calculates sine and cosine characteristics, so as to take the processed time period characteristics and the solar irradiance data as input, train an XGBoost regression model, and realize power generation amount prediction. The application does not need an additional abnormal point screening step, has natural robustness by means of the weighted tree integration mechanism of XGBoost, and has a lightweight model structure, fast training and prediction speed, can significantly reduce the consumption of computing resources while ensuring a prediction accuracy of more than 95%, is suitable for multi-station distributed deployment scenes, and can effectively support intelligent power grid dispatching and distributed energy management.

The invention relates to the technical field of photoelectrochemical water decomposition, and discloses a solar cell hydrogen production method, which comprises the following steps: S1, collecting solar irradiation data and electrolyte state parameters through a photovoltaic-electrolytic cell integrated new energy battery device, S2, carrying out wave band division processing on a solar spectrum, and S3, carrying out wave band division processing on the solar spectrum, the visible light wave band is directionally transmitted to a photovoltaic unit for power generation, and the infrared wave band is guided to a heat exchange unit for preheating electrolyte; by establishing a frequency division processing mechanism of visible light and infrared bands, solar spectrum energy is distributed to photovoltaic power generation and electrolyte preheating according to needs, irradiation intensity fluctuation and thermoelectric requirements of an electrolytic cell can be matched in real time, cooperative supply of photovoltaic output electric energy and heat exchange power is guaranteed, attenuation of hydrogen production efficiency caused by abnormal spectrum distribution is avoided, and the service life of the hydrogen production system is prolonged. The stability and continuity of the energy conversion process are improved, and the membrane electrode coupling protection end is arranged, so that the long-term operation reliability of the new energy battery device under complex working conditions is guaranteed.

The invention relates to the field of photovoltaic technology, and discloses a photovoltaic power prediction method, device and equipment under the influence of sand and dust and a storage medium, and the method comprises the steps: predicting a first dust coverage degree of a first photovoltaic module based on a dust influence factor of the first photovoltaic module and a first prediction model; determining the atmospheric transmissivity under the influence of sand and dust based on the sand and dust concentration of the target area where the first photovoltaic module is located; obtaining a first dust pollution rate based on the first dust coverage and a pre-constructed fitting relationship between the dust coverage and the dust pollution rate of the photovoltaic module; determining the target irradiance of the first photovoltaic module based on the atmospheric transmissivity, the first dust pollution rate and the solar irradiance of the target area; and predicting the target photovoltaic power of the first photovoltaic module based on the target irradiance, the first dust pollution rate, the second prediction model and the power influence factor of the first photovoltaic module. According to the invention, the problem of poor photovoltaic power prediction accuracy of the photovoltaic module under the sand storm weather can be solved.

The application provides a kind of unmanned aerial vehicle endurance enhancement control system and method based on dual energy collaborative power generation, belongs to unmanned aerial vehicle endurance enhancement control technical field;To solve the technical problems of poor endurance, low energy utilization efficiency, poor scene adaptability and complex endurance control algorithm of existing unmanned aerial vehicle, each sensor arranged on unmanned aerial vehicle real-time acquisition environment wind speed, solar irradiance, vertical speed, horizontal speed, attitude angle, each motor speed and battery state parameter of unmanned aerial vehicle flight;According to the data collected, based on the wind power generation power model, solar power generation power model and flight power consumption grading model built in energy management module, the current total wind power generation power, solar power generation power and flight total power consumption are calculated respectively;Based on the energy self-sustaining coefficient decision model built in energy management module, the energy self-sustaining coefficient is calculated, and its belonging interval is judged;The application is applied to unmanned aerial vehicle strong endurance operation site.

The invention relates to a new energy output prediction method and system based on an NHITS model, and the method comprises the steps: obtaining the input characteristics of a to-be-tested new energy object, forming new energy time series data, and enabling the new energy to comprise photovoltaic or wind power; the input characteristics corresponding to photovoltaic output prediction comprise time, solar irradiance, air temperature, air pressure, humidity and historical power data, and the input characteristics corresponding to wind power output prediction comprise time, meteorological variables and wind speeds and wind directions at different heights; new energy output prediction is carried out by adopting an NHITS prediction model, the NHITS prediction model carries out multi-time scale decomposition on a time sequence through a hierarchical recursive structure, the time sequence is divided into a plurality of modules stacked according to layers, and each module carries out partial interpretation on input new energy time sequence data under different time scales and outputs a prediction component; and superposing the prediction components of each layer to obtain a final prediction result. Compared with the prior art, the method can maintain high prediction precision and stability.

The invention discloses an ultraviolet index-based solar irradiation intensity prediction and correction method for a photovoltaic power station, and the method comprises the steps: obtaining free ultraviolet indexes, cloud layer parameters and conventional meteorological data through an API interface, firstly calculating the total ultraviolet intensity in an ideal state, and then correcting the total ultraviolet intensity in combination with an atmospheric attenuation factor and a cloud layer correction factor; dynamically determining the proportion of ultraviolet rays in the total solar irradiation intensity according to the solar altitude, and reversely deducing the initial total solar irradiation intensity; and finally, through real-time data of a local meteorological instrument, a baseline correction and dynamic error correction dual mechanism is constructed, and a prediction result is optimized. Through fusion of multiple meteorological factors, the prediction error is less than 10%, the photovoltaic power prediction system can be directly connected, and low-cost and high-precision data support is provided for optimized operation of the photovoltaic power station.