1281 results about "Weather prediction" patented technology

The invention belongs to the technical field of intelligent control of heating, ventilation and air conditioning systems, and particularly relates to an intelligent optimizing and energy-saving control method for a central air conditioner based on deep learning, which comprises the following steps of: acquiring operation data of a central air conditioning system in real time through an internet of things technology; the operation data comprises operation parameters of cold and heat source equipment, flow and lift parameters of a water pump, fan frequency parameters of a cooling tower, temperature and humidity data of an air conditioner terminal, environment temperature and humidity data, weather forecast data and the like. Through deep integration of Internet of Things perception, deep learning prediction and a multi-objective optimization technology, the limitation of a traditional control framework is broken through, meanwhile, accurate prediction of building cooling and heating loads is realized through construction of a hybrid deep learning model, an optimization objective of a full life cycle perspective is established in combination with an equipment performance degradation model, and the system performance is improved. A federal learning framework is innovatively introduced into region-level energy efficiency management, and the model generalization ability is improved on the premise of ensuring data privacy.

The invention discloses a power distribution network power supply reliability evaluation method and system based on data analysis, and relates to the technical field of power distribution network detection, a meteorological station, a GIS and an equipment monitoring terminal collect temperature, rainfall, wind speed, load and fault record data, and after time sequence alignment and interpolation correction, a unified environment data set is generated to provide an accurate basis for model construction; secondly, using historical and online data, calling a nonlinear mapping function and an equipment function to train a meteorological-fault rate model, outputting the equipment fault probability in real time, and then calculating a system risk index and a line risk value in combination with confidence-weighted weather forecast and equipment load coupling operation to accurately identify a high-risk area; priority scheduling, load transfer and graded load shedding are carried out on the high-risk area, fault information is transmitted back, closed-loop control is achieved, and the operation efficiency is improved.

A double-path ultra-short-term wind power prediction method based on numerical weather forecast and multi-order time sequence dynamic gating fusion comprises the following steps: acquiring wind power generation historical data and numerical weather forecast data of a wind power plant, and screening weather factors highly related to wind power by using an MIC; the CEEMDAN is adopted to decompose the power sequence into a plurality of intrinsic mode functions (IMF); a dual-path prediction architecture is constructed, one path adopts xLSTM to predict an intrinsic mode function (IMF), all subsequences are superposed, and a prediction result is obtained; in the other path, the XGBoost is combined with key meteorological characteristics of an intrinsic mode function (IMF) and a numerical weather forecast (NWP) for prediction, and all the subsequences are superposed to obtain a prediction result; the method comprises the following steps: designing an MT-DGFusion module through an enhanced attention and dynamic gating network; and fusing the dual-path prediction results through an MT-DGFusion module to obtain a final prediction result. According to the method, double breakthrough of prediction precision and stability is realized, and a new technical path is provided for a complex time sequence prediction task.

The invention discloses an intelligent air conditioner water chilling unit control method and system based on predictive optimization. Operation data and weather forecast data are collected to establish a dynamic response reference, building cold load sudden change opportunity is recognized, the mismatching relation between magnetic suspension frequency and wet bulb temperature is detected, and energy-saving opportunity recognition data is generated to determine a cooling starting judgment table; identifying equipment response delay characteristics by using a dynamic response reference, extracting time sequence advantage parameters to determine dislocation configuration among multiple pieces of equipment, and generating coordination control parameters; cloud-local transmission delay analysis is carried out according to the coordination control parameters, and a coordination control sequence is generated through buffer opportunity identification and delay compensation; mechanical refrigeration suppression data is generated through energy efficiency mode classification, free cooling potential mining is implemented to form a cold source optimization factor, and an emergency response strategy is generated; an emergency response strategy is used to identify a multi-device linkage trigger critical zone, a prediction correction feedback network is constructed, a global collaborative optimization instruction is generated, and the system operation efficiency and the control precision are improved.

The invention discloses a hybrid deep learning model construction and output interpretation method for spaceborne GNSS-R sea surface significant wave height inversion. The method comprises the steps of obtaining CYGNSS L1-level GNSS-R data, an European mid-term weather forecast center (ECMWF) ERA5 reanalysis data set, IMERG rainfall product data and WaveWatch III SWH data; carrying out preprocessing and quality control on the acquired data; performing space-time matching on a data set obtained after quality control, and dividing the data set into a training set, a test set and a verification set according to a certain proportion; the method comprises the following steps of: constructing a hybrid deep learning model which is combined with efficient multi-scale attention and CNN-Transform-BiConvLSTM, and carrying out training and verification on the model; a MultiScaleWaveFormer model is created to deeply learn an SHAP interpreter, and the importance of the input features is calculated and visualized; the method comprises the following steps of: inputting an inversion result into a trained MultiScaleWaveFormer model to obtain an inversion result; and finally, comparing the SWH inversion result of the model with the SWH inversion result of the existing model, and evaluating the performance of the model. According to the invention, the five modules are combined, higher efficiency and functionality are realized, and the accuracy of spaceborne GNSS-R data inversion significant wave height is further improved.

Provided in the present invention is a power prediction method for distributed photovoltaic power stations, the method comprising: acquiring a satellite cloud image of distributed photovoltaic power stations at a historical moment and a satellite cloud image of the distributed photovoltaic power stations at a current moment to predict satellite cloud image features at a future moment; selecting several photovoltaic power stations to install solar irradiance collection apparatuses, and on the basis of the satellite cloud images, collected solar irradiance, and location information of the photovoltaic power stations, calculating the solar irradiance of all the photovoltaic power stations; on the basis of the satellite cloud image features at the future moment and the collected solar irradiance of the photovoltaic power stations, obtaining the solar irradiance of the distributed photovoltaic power stations at the future moment; and on the basis of the solar irradiance of the distributed photovoltaic power stations at the future moment and weather forecast information at the future moment, predicting the power of the distributed photovoltaic power stations at the future moment. In the solution of the present invention, solar irradiance data is expanded, and a mapping relationship between satellite cloud images and solar irradiance and a mapping relationship between solar irradiance and power are constructed by means of prediction, so that the power of distributed photovoltaic power stations is predicted, thereby improving the prediction precision.

The invention discloses an AI-NWP three-dimensional closed-loop bidirectional dynamic feedback coupling method for extreme rainfall event area simulation and a program product, and belongs to the technical field of meteorological numerical simulation and artificial intelligence fusion. According to the method, a mid-term forecast field is generated based on an AI global weather prediction model, an analysis field is constructed by assimilating multi-source observation data, and a high-resolution NWP region mode is driven to perform rolling simulation. Furthermore, a space-time residual field is constructed by using the difference between an NWP region simulation result and live data, a residual learning model is trained, an AI model prediction structure is fed back and corrected, and dynamic weight adjustment updating of the AI prediction model is realized. A closed-loop two-way feedback system among AI output, NWP simulation and residual evaluation is integrally formed, the space structure reduction capability and the area positioning precision of a medium-term heavy rainfall event are effectively improved, the continuous predictability and the simulation credibility of an extreme weather process are remarkably enhanced, and the method has good stability, universality and engineering expansion value.

The invention discloses a risk scheduling method for a water-wind-solar complementary system, and the method comprises the steps: collecting historical data and power grid topological parameters, accessing global and regional numerical weather forecast data, employing a coupling model, fusing meteorological grid data with a historical power station output sequence, and generating hourly reservoir incoming water amount and wind-solar power probability prediction results. According to the predicted time sequence and the generated scene, calculating the scene probability based on the generated scene; according to the prediction time sequence, using a quantification method to obtain a peak regulation risk quantification value; calculating power grid power flow distribution and critical clearing time according to the generation scene and the power grid topological parameters, and calculating a system stability margin; a multi-target optimization model is constructed according to a peak regulation risk quantized value after splitting and a system stability margin, the system stability margin is introduced as an optimization target, the overall stability of the system is improved, a meteorological-hydrological-output three-mode feature mapping method is provided, and meteorological feature extraction of a key grid region is enhanced through an attention mechanism.

The invention discloses a new energy charging pile charging control method and system, and relates to the technical field of new energy, and the method comprises the steps: collecting user historical charging data, power grid load data, fixed electricity price information and weather forecast data in real time, and recognizing user charging habits and demands; generating a personalized charging scheduling strategy according to the charging habit of the user and the real-time power grid state; the availability of various energy sources is monitored in real time, the generating capacity and the cost of each energy source are evaluated, the priority of energy source selection is adjusted by using a personalized charging scheduling strategy, and an optimal charging mode is automatically selected; executing charging operation based on the charging mode selected by the user, and performing adjustment according to the power grid load and the energy consumption condition; the availability of various energy sources is monitored in real time, the generating capacity and the cost are evaluated, and the priority of the energy sources is dynamically adjusted, so that the response capability of the charging pile to environment change is improved, the charging cost of a user can be reduced, and renewable energy sources are fully utilized.

The invention discloses an agricultural irrigation area water resource intelligent management system and method, and relates to the field of intelligent management, and the method comprises the steps: building a correlation model of a crop growth stage and a basic irrigation threshold value through real-time collection of original weather station data, future 24-hour weather forecast, crop types, planting dates and other multi-dimensional data; and in combination with soil humidity sensor data and a channel pump station state, the basic irrigation starting threshold is dynamically adjusted by using an FAO formula or a deep learning algorithm to form a dynamic irrigation decision threshold. And then multi-source data is input into an irrigation decision module, an irrigation starting and stopping instruction is generated by judging the relation between the irrigation state and the soil humidity threshold value, and closed-loop management is achieved. Thus, the limitation of traditional irrigation static threshold management is broken through, and through meteorological data dynamic adaptation and multi-source data fusion decision making, on the premise of guaranteeing crop growth water demand, the water resource allocation efficiency is optimized, and the intelligent management level of an agricultural irrigation area is improved.

The invention discloses an automatic control method and device for a thermal management system of an electric vehicle, relates to the field of intelligent control, and realizes multi-step prediction of battery and motor heat production and thermal load change in the future by fusing external information such as a navigation path and weather forecast, plans a thermal management strategy in advance, and improves the response perspectiveness of the system. A multi-source disturbance dynamic structured correlation equalization technology is adopted, the coupling relation between heat sources is modeled, cross disturbance is compensated through a joint state function, and multi-heat-loop cooperative control is achieved. On the basis of a model predictive control (MPC) framework, temperature tracking errors and energy consumption penalty are optimized, the energy consumption of actuators such as a compressor and a water pump is reduced while a safe temperature interval is met, and the endurance is prolonged. And in combination with a state estimator fusion model and observation data, the control precision and robustness under a complex working condition are enhanced, and finally global optimal thermal management considering the service life of parts, the comfort of passengers and the energy efficiency is realized.

The invention relates to the technical field of new energy power systems, and discloses an energy storage coordination system and control method based on meteorological prediction and multi-energy complementation, and the system comprises a power generation end module which carries out the dynamic adjustment according to a power adjustment instruction of a power generation end; the meteorological prediction and data acquisition module is used for acquiring meteorological data in a future time period in real time and sending the meteorological data to the intelligent control and scheduling platform; the energy storage end module sends data including lithium battery SOC and hydrogen storage tank pressure to the intelligent control and scheduling platform and performs charging and discharging according to an energy storage end charging and discharging priority strategy; and the intelligent control and scheduling platform receives the electricity price data of the weather prediction and data acquisition module, the energy storage end module and the power grid in real time, generates a power generation end power regulation instruction and an energy storage end charging and discharging priority strategy, and meanwhile, improves the income and maximizes the hydrogen energy use proportion through electricity price peak-valley arbitrage and green electricity transaction premium. The power grid stability and the energy utilization rate are greatly improved, the energy storage life is prolonged, and the investment payback period is shortened.

The invention discloses a photovoltaic power generation day-ahead prediction method based on causal inference and multi-scale feature fusion, and belongs to the technical field of photovoltaic power generation prediction. According to the method, historical meteorological characteristic data is divided into a plurality of samples, and generated power of the samples in the future M steps is used as corresponding labels. A PCMCI causal inference method is used to calculate a causal inference weight between a sample and a label, the sample is weighted and then is input into three base learners of BiLSTM, TCN and GAF-SE-ResNet50 in parallel, the trend of an input sequence is recognized from three dimensions of global, local and periodical, and a photovoltaic power generation power day-ahead preliminary prediction value is obtained through corresponding training. Wavelet features of the local weather forecast sequence of the prediction point are extracted, and a final prediction value is obtained through fusion of a cross attention mechanism and the initial prediction value of the base learning device. Accurate and stable photovoltaic power generation prediction in a complex climate change environment is realized.

The invention discloses a photovoltaic power generation power prediction method and system based on a large language model. The method comprises the following steps: converting historical power data and numerical weather forecast data into time sequence embedded representation; through cross-modal semantic alignment, semantic embedding representation is generated; constructing a natural language prompt containing task context information, encoding the natural language prompt into prompt embedding, combining prompt embedding with semantic embedding representation to form a fusion input sequence, inputting the fusion input sequence into a pre-trained large language model, and outputting implicit features; synchronously generating an initial power prediction result and a weather prediction result obtained by correcting the numerical weather prediction data through a parallel collaborative prediction mechanism; and taking the meteorological prediction result as a correction signal, performing joint optimization on the preliminary power prediction result, and outputting a power generation power prediction value. According to the method, the problem of deep fusion of heterogeneous data is effectively solved, and the prediction accuracy is improved.

The invention discloses a medium and long term weather prediction system based on a physical law correction deep learning technology, and the system comprises a high-resolution data input module, a multi-resolution network construction module, a sub-graph processing module based on a graph neural network, and a linear attention processing module. The system comprises an advanced attention processing module used for reducing secondary calculation complexity to linear complexity, a feature fusion and adaptive fusion module, a multi-level decoding and physical corresponding area optimization correction model, a rolling forecasting module and a multi-step training fine tuning module based on DORA. The medium and long term weather prediction system based on the physical law correction deep learning technology can process multi-resolution input data, and realizes high-precision and long-time weather prediction. Furthermore, global weather forecast with the resolution ratio of 16 days and 13 kilometers can be achieved, and meanwhile refined forecast with the higher resolution ratio is achieved in China.

The invention discloses a refrigeration equipment control method and system for a communication machine room, and relates to the field of heat dissipation control, and the method comprises the steps: obtaining machine room indoor temperature and equipment heating data of the communication machine room, and determining a preset refrigeration index according to the machine room indoor temperature and the equipment heating data; the operation parameters of the air conditioning equipment are adjusted to refrigeration operation parameters; when the temperature difference value between the indoor temperature of the machine room and the outdoor environment temperature is higher than a preset starting threshold value, starting parameters of the natural cold energy exchange unit are determined; the natural cold energy exchange unit is controlled to operate according to the starting parameters, and operating parameters of the air conditioning equipment are adjusted to be optimized operating parameters; determining a temperature change trend curve of the outdoor environment temperature based on the weather forecast information, and determining an optimization adjustment node according to the temperature change trend curve; and when the adjusting nodes are optimized, the starting parameters of the natural cold energy exchange unit and the operation parameters of the air conditioning equipment are adjusted. By implementing the application, the energy use of the refrigeration equipment in the communication machine room can be saved.

The invention discloses a variational deviation correction method and device, and relates to the field of weather forecast, and the method comprises the steps: obtaining the observation data of a current time window and an updated deviation correction coefficient of a previous time window; calculating a satellite observation deviation correction amount based on the deviation correction coefficient, the observation information and the mode background field, calculating a deviation between simulation observation and actual observation based on an observation operator, and carrying out correction and quality control on the deviation; according to the satellite observation data after quality control, on the basis of the improved target functional and the gradient thereof, in the minimization iteration process, under the joint constraint of the observation data and a numerical weather forecast mode, a deviation correction coefficient is updated online; wherein the improvement process of the target functional comprises the steps that a deviation correction coefficient is used as a control variable to be combined with a mode state variable to construct a new target functional, and variable transformation is carried out in an increment form. The assimilation analysis quality of observation data is improved, and the numerical weather forecast level is improved.

The invention provides a greenhouse environment adaptive regulation and control method and system, and relates to the technical field of environment control, and the method comprises the steps: collecting multi-dimensional environment parameters in a greenhouse; based on the environmental parameters, a preset crop growth period database and weather prediction data, taking minimization of a preset cost function as a target, and adopting a model prediction control algorithm to generate an equipment linkage instruction set in a future preset time period, the preset cost function fusing environmental regulation and control deviation and operation cost; issuing the equipment linkage instruction set to each execution equipment, and executing linkage regulation and control; wherein when the equipment linkage instruction set is generated, a conflict resolution mechanism based on a dynamic priority is adopted to determine an execution sequence of a plurality of equipment instructions; the adaptive regulation and control method integrating multi-source data verification, multi-scale prediction, dynamic priority conflict resolution and multi-target cost optimization improves the accuracy, economy and crop suitability of greenhouse environment regulation and control.

The invention relates to the technical field of air conditioners, in particular to an air conditioner internet-of-things control method. An IoT (Internet of Things) module is integrated in each air conditioner, and environment data including temperature, humidity and air quality of a room are collected in real time through connection of an intelligent sensor and a cloud platform; data of all air conditioning equipment is summarized to form a centralized management system of equipment state and environment information; an intelligent cooperative control algorithm based on communication between the equipment is constructed by means of internet-of-things intercommunication between the air conditioning equipment; the air conditioning equipment detects environmental data in real time through a local sensor, accesses prediction data of an external environment including weather forecast and climate change trend, and adjusts a working mode in advance in combination with a self-learning function of the equipment; a multi-dimensional energy-saving optimization algorithm is adopted for intelligent control; meanwhile, the overall energy consumption is optimized in combination with external weather change and equipment use frequency; the optimization strategy comprises time period distribution and equipment priority control; and performing personalized user control based on big data analysis.

The invention relates to a photovoltaic power grid energy storage optimization regulation and control method based on multi-scale prediction. The method comprises the following steps: A1, obtaining historical power generation data, real-time meteorological data and numerical weather forecast of a photovoltaic power station; a2, generating a multi-time-scale photovoltaic output prediction sequence; a3, establishing an energy storage dynamic model of charge and discharge efficiency, capacity attenuation and operation constraint; a4, generating an energy storage charging and discharging demand curve under different time scales; a5, constructing a multi-time scale coupled optimization model by taking power grid operation cost minimization and renewable energy consumption maximization as targets; a6, updating an energy storage scheduling instruction in a rolling manner based on latest prediction data by adopting a model prediction control framework; a7, monitoring the deviation between the actual photovoltaic output and the power grid load, and dynamically adjusting the energy storage charging and discharging power; and A8, correcting a prediction error through Kalman filtering and a closed-loop feedback mechanism. According to the invention, high-efficiency operation can be realized, and power grid cost minimization and renewable energy consumption maximization can be realized.

The invention discloses a photovoltaic module cleanliness evaluation method and system based on multi-dimensional data fusion. The method comprises the following steps: acquiring multi-dimensional data of a photovoltaic power station, including generating capacity data, power station state data, irradiation data, meteorological data, geographic position data, cleaning record data, weather forecast data and economic index data; preprocessing the data; constructing a multi-dimensional feature; a pre-trained Transform architecture time sequence model is adopted to carry out training; evaluating the cleanliness state of the photovoltaic module in real time and predicting the change trend; and determining the optimal cleaning time and generating decision suggestions based on the cleanliness evaluation result and economic analysis. Through multi-dimensional data fusion and a large model algorithm, precise evaluation and scientific decision making of the cleanliness of the photovoltaic module are realized, the evaluation accuracy is improved by 15-20%, and the method is suitable for photovoltaic power stations at different geographic positions and under different climate conditions.

The invention discloses an intelligent operation and maintenance scheduling and resource optimization method and system for a new energy power station, and belongs to the technical field of data processing and management.The method comprises the steps that real-time power generation data, equipment sensor data, weather forecast data, a power grid scheduling instruction and electricity market electricity price information of the new energy power station are obtained; a unified data matrix is generated through space-time alignment processing to execute bidirectional feedback prediction, and a corrected power generation plan is generated; a cooperative scheduling decision is generated through a multi-target dynamic balance algorithm, and a personnel dispatching scheme and a material allocation path are generated through real-time path planning; and generating a historical decision data set based on the execution result and the actual execution deviation, and adjusting prediction parameters and scheduling parameters through an incremental learning model. A closed-loop optimization method combining data fusion, collaborative prediction, multi-target scheduling and adaptive learning is adopted, global collaborative optimization of multiple factors such as power generation, operation and maintenance, energy storage and markets can be achieved, and the economic benefit and the intelligent level of overall operation of a power station are improved.

The invention relates to the field of optical storage, and particularly discloses an optical storage system cooperative control method and device, a terminal and a medium, and the method comprises the steps: collecting multi-dimensional data in real time, including photovoltaic array data, user side load data, power grid side information data, meteorological data and weather forecast information; generating a photovoltaic power generation power prediction curve, a user load demand prediction curve and a power grid electricity price prediction curve by using the multi-dimensional data through a machine learning model; and taking the current state of the optical storage system and each prediction curve as input parameters, carrying out optimization problem solving based on a multi-objective optimization function and constraint conditions through a model prediction control algorithm, generating an optimal control sequence in a period of time in the future, and controlling corresponding equipment through the optimal control sequence. According to the method, the response speed to uncertainties such as illumination abrupt change and load fluctuation is increased, source-storage-load-network coordination is realized, the sub-optimal problem of independent control of each unit is avoided, the operation cost is reduced, and renewable energy consumption is improved.

The invention relates to an electric power virtual simulation optimization method based on a digital twin technology, and the method comprises the following steps: S1, deploying sensors at key nodes of an electric power system, and integrating an intelligent electric meter, weather forecast, social media information and market data to form a comprehensive data pool; s2, constructing a digital twinborn model of the power system; s3, optimizing the digital twin physical model based on a Bayesian optimization method and an adversarial neural network; s4, applying a bee colony algorithm to formulate a cooperation strategy, and optimizing transmission and load adjustment of power in different areas; s5, introducing an adaptive neural fuzzy inference system, and further optimizing a cooperation strategy; and S6, based on the optimized digital twinborn model and the operation strategy, performing large-scale distributed simulation by using cloud resources. According to the invention, accurate construction of the electric power simulation model and accurate implementation of electric power virtual simulation are realized.

The invention provides a multifunctional controller for a new energy automobile and the new energy automobile, and relates to the technical field of new energy automobile control. The multifunctional controller for the new energy automobile comprises a data collection module, a data processing module, a prediction module, a storage system, a communication system, a user interface, a software system, a battery management system, an auxiliary driving system, a safety system, an emotion recognition module, a power supply system and a continuous learning system. Through the prediction model and time sequence analysis, the charging and discharging strategy of the battery is optimized, the service life of the battery is prolonged, the energy efficiency is improved, a power system is optimized according to real-time data and a prediction result, the performance and efficiency of the vehicle are improved, and thermal management of the vehicle is optimized according to weather forecast and real-time traffic information. The optimal performance can be kept under different weather conditions, and the system can automatically adjust the internal environment, such as temperature and music playing, of the vehicle by analyzing the emotional state of the driver so as to adapt to the emotion of the driver.

The invention relates to the technical field of meteorological prediction, in particular to a meteorological prediction precision improvement method and device, equipment, a storage medium and a computer program product. The method comprises the following steps: constructing a plurality of initial weather prediction models based on a Transform structure in combination with a PIDL (Precision Independent Design Language) algorithm; taking the power curve of the wind turbine generator and the performance characteristic curve of the photovoltaic module as constraint conditions, and integrating the constraint conditions into each initial meteorological prediction model to obtain a plurality of basic meteorological prediction models; based on a preset automatic hyper-parameter tuning tool, performing hyper-parameter optimization on each basic meteorological prediction model in combination with a cross validation algorithm; fusing the plurality of basic meteorological prediction models after hyper-parameter optimization by adopting an integrated learning algorithm to obtain a target meteorological prediction model; and according to the meteorological characteristic data and the target meteorological prediction model, the target meteorological prediction result is determined, and the meteorological prediction precision is improved.

The invention belongs to the technical field of wind power prediction, and particularly relates to a wind power prediction method and system based on multi-source data fusion. According to the method, a cascade prediction architecture composed of a wind speed prediction model and a power prediction model is constructed, and the wind speed prediction module extracts cross-variable association features of multi-dimensional meteorological parameters from numerical weather forecast data by adopting a variable-level attention mechanism and a patch-level attention mechanism; capturing a long and short time dependence mode from the actually measured historical operation data; the power prediction model decomposes a predicted wind speed sequence through one-dimensional average pooling, and constructs a nonlinear power mapping model. Besides, an attention fusion module is provided, a learnable global variable is introduced as an information exchange bridge, information interaction between meteorological data and actually measured data is realized, and the collaborative utilization efficiency of multi-source data is effectively improved. Experiments show that the method is superior to an existing prediction model based on deep learning.

The invention relates to the field of photovoltaic power prediction, in particular to a photovoltaic power prediction method, and the method comprises the steps: obtaining the spectral response function data of different photovoltaic modules in a photovoltaic power station, and measuring the spectral irradiance distribution of the surface of each photovoltaic module in real time; according to the spectral response function data and the spectral irradiance distribution, calculating the effective spectral irradiance of each component type in a grouping manner according to the component types; counting the temperature coefficient of the batch to which each photovoltaic module belongs according to historical data, and establishing a module-level temperature-power correction factor; according to the string topological relation, string-level temperature correction power is obtained, and the string-level temperature correction power, the effective spectral irradiance and the global irradiance data of the numerical weather forecast are output into a corrected power station-level photovoltaic power predicted value; the problem of low photovoltaic power prediction precision caused by inaccurate modeling due to parameter difference and temperature influence of photovoltaic modules in photovoltaic power station power prediction is solved.

The invention provides a low-altitude logistics meteorological prediction method and system based on multi-source meteorological data fusion, and relates to the technical field of meteorological prediction and low-altitude logistics cross.The method comprises the steps that acquired multi-source data is preprocessed, and a comprehensive meteorological data set is generated; echo evolution deduction is carried out on real-time continuous radar echo monitoring data; the method comprises the following steps: identifying a severe convective weather process, forming a low-altitude severe convective weather prediction model, performing identification prediction on severe convective weather by using the low-altitude severe convective weather prediction model based on a real-time echo evolution deduction prediction result and multi-source real-time data, and performing early warning processing if an early warning threshold value is reached. The industrial problems that matched meteorological data and product services are lacked in low-altitude logistics flight activities, comprehensive channel integration of low-altitude multi-source data is lacked, and the accuracy and timeliness of an existing traditional numerical forecasting technology cannot meet business requirements are solved.

The invention discloses a new energy station intelligent micrometeorological monitoring device suitable for extreme weather and a power prediction method, and relates to the technical field of new energy station meteorological monitoring and power prediction. The device integrates a plurality of meteorological sensors, combines satellite meteorological data and ground meteorological station data, improves meteorological monitoring accuracy through data fusion, and gives 8-15-day long-time scale weather forecast. According to the method, based on meteorological data collected in real time and historical meteorological data of a new energy station, a long-short term memory network model and a segmentation multi-target risk method are adopted to carry out risk assessment on extreme weather, and an extreme weather early warning signal is given. A deep neural network is utilized to establish a power nonlinear prediction model of a new energy station, and a higher-precision power prediction result is given through a random forest dynamic correction algorithm. The meteorological and generated power prediction accuracy of the new energy station is improved, and the operation safety of the new energy station is improved.