240 results about "Solar irradiance" patented technology

The invention relates to the technical field of solar irradiance prediction, and discloses a solar irradiance prediction method and system based on data fusion, and the method comprises the steps: obtaining a cloud layer gray image, a wind speed vector, a terrain elevation, a slope inclination angle, a solar azimuth angle and an elevation angle, calculating the movement speed and direction of a cloud layer based on the cloud layer image and the wind speed vector, and obtaining a prediction result. Calculating a shielding path in combination with a terrain elevation and a sun position; generating a terrain shielding influence coefficient matrix; fusing a gradient and shielding data to generate a dynamic effect graph; performing cloud-ground shielding analysis based on the dynamic effect graph and a cloud trajectory to obtain cloud-ground coupling influence distribution; using a random forest algorithm to fuse the coupling distribution and the shielding coefficient to calculate an initial irradiance probability, and forming a preliminary prediction result; and inputting the time sequence prediction model, the cloud trajectory and the shielding coefficient into a trained time sequence prediction network, and outputting an optimization result. According to the method, prediction requirements under complex terrains and rapid weather changes can be met.

The invention discloses a real-time calculation method and system for distributed photovoltaic power generation meteorological elements, and solves the technical problem of regional distributed photovoltaic power generation meteorological information loss. The method comprises the following steps: selecting adjacent sites based on power station operation data, and repairing target site data by using adjacent site data; constructing an irradiance calculation model by using the reconstructed power data, and establishing a model among numerical weather forecast, distributed photovoltaic station power, irradiance and environment temperature; and based on the established model, calculating the solar irradiance and temperature of the position of the distributed photovoltaic station by taking the real-time power and the current value of the numerical weather forecast as input. In order to complement the power of the distributed photovoltaic station, a meteorological information calculation model is established, a high-cost-performance and high-precision solution is provided for complementing the meteorological information of distributed photovoltaic power generation, and the accuracy of output power prediction of the distributed photovoltaic station is improved.

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 relates to the technical field of photovoltaic power generation, in particular to a photovoltaic effective solar irradiance measurement method, which comprises the following steps of: converting optical power into irradiance and tracing the irradiance to an SI unit system by taking trap detection transmitted by a low-temperature radiation meter as an optical power standard device and combining a precise diaphragm; respectively collecting the output voltage of the total radiometer, the output voltage of the monitoring radiometer, the zenith angle of the total radiometer in the non-shielding state and the solar irradiance output by the absolute radiometer when the total radiometer is in the shielding state and the non-shielding state, and calibrating the total radiometer; angle response, temperature response, sensitivity linear response and spectral response data of the total radiometer and the photovoltaic module under different influence parameters are obtained, a correction coefficient set is obtained through fitting and used for conducting temperature, angle, irradiance linearity and spectral mismatch correction on the irradiance measured by the total radiometer, photovoltaic effective solar irradiance data are obtained, and the photovoltaic effective solar irradiance data are obtained. Accurate measurement of the solar irradiance for photovoltaic power generation is realized.

The invention discloses a large-range high-frequency all-sky earth surface solar irradiance machine learning estimation method and system suitable for a cloudy area, and relates to the technical field of irradiance estimation, and the method comprises the steps: obtaining earth surface radiation observation data, generating a station position table, and constructing a training data set; constructing a ground solar radiation degree model, and dividing the training data set into a training set and a test set according to a proportion of 80% to 20%; determining hyper-parameters of the Xgboost algorithm through parameter search, training the model on the training set, and checking the model by using the test set; new satellite reflectivity is used for observing data and meteorological elements, data cleaning is carried out, and the model is input to estimate the space-time distribution of the total solar radiation irradiance of the earth surface. According to the method, the space and time precision of radiation inversion is improved, the complex nonlinear characteristics and data interaction relation are accurately captured, efficient inversion of the earth surface solar radiation degree is achieved, and the prediction performance and generalization ability of the model are remarkably improved.

The invention provides a multi-source feature fusion method for abnormal value self-correction and meta-learning confrontation of cloud cluster shielding features of a power distribution network in extreme weather. The method comprises the following steps: step S110, extracting and predicting cloud cluster shielding features; step S120, screening out meteorological variables with relatively high correlation with the solar irradiance; step S130, based on the cloud cluster shielding characteristics in the step S110, generating a cloud cluster shielding space range; step S140, executing abnormal value detection and correction for the space range shielded by the cloud cluster generated in the step S130; and step S150, fusing the corrected cloud cluster shielding features with meteorological variables based on a generative adversarial network algorithm of meta learning. Compared with a traditional method, focusing of a sudden change area under extreme weather is more accurate, and the high precision and efficiency of cloud cluster shielding feature extraction and prediction are remarkably improved.

The invention discloses a composite new energy electric power spot transaction auxiliary decision-making system and method, and belongs to the technical field of electric power transaction. The data collection module is used for continuously collecting a wind speed real-time monitoring value, solar irradiance measurement data, dynamic electricity price information of an electric power spot market, historical transaction records and charge state and charge and discharge capacity data of an energy storage battery; the prediction optimization module is connected with the data acquisition module and comprises a wind speed sudden change prediction sub-module and a cloud layer shielding sudden change prediction sub-module; the multi-source collaborative decision-making module is used for receiving an output result of the prediction optimization module and comprises a strategy generation unit, a risk assessment unit, a scheme screening unit and a decision-making execution unit; and the feedback learning module is used for executing closed-loop optimization after each transaction is completed. According to the invention, through four technical innovations of a special mutation prediction model, three-level collaborative dynamic optimization, self-learning risk prevention and control and energy storage life protection, the prediction precision in composite new energy electric power spot transaction is effectively improved.

The invention relates to the technical field of new energy, and discloses a photovoltaic array fault intelligent diagnosis method and system, and the method comprises the steps: collecting the historical operation data, assembly temperature and solar irradiance data of a photovoltaic string in a normal state and a fault state, and taking the data as an original sample feature data set; preprocessing the original sample data, and randomly dividing the original sample data into a training data set, a verification data set and a test data set; based on an LSTM-CNN-ArcLoss fusion network algorithm, constructing an intelligent fault diagnosis model of the photovoltaic array; and inputting the training data set, the verification data set and the test data set into a photovoltaic array intelligent fault diagnosis model, and outputting a photovoltaic string fault classification result by the photovoltaic array intelligent fault diagnosis model. The method not only can effectively reduce the operation and maintenance difficulty, save the operation and maintenance cost and improve the operation efficiency, but also assists the photovoltaic power station to continuously, stably and efficiently operate, and has certain engineering application value and economic and social benefits.

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 relates to a photovoltaic power interval prediction method based on time sequence decomposition and conformal quantile regression, and the method comprises the steps: collecting historical power data, solar irradiance data, temperature data, relative humidity data and wind speed data of a photovoltaic power station, supplementing missing values in the photovoltaic power data through employing a linear interpolation method, and carrying out the prediction of the photovoltaic power interval. Secondly, normalizing all data by using a Z-Score standard method, dividing the processed data into a training set, a calibration set and a test set, constructing a time sequence decomposition model based on a NeuralProphet framework, obtaining a deterministic prediction model, training two conditional quantile models respectively by using a quantile regression method, and obtaining a time sequence decomposition model based on the NeuralProphet framework; the method comprises the following steps: determining a power interval upper limit and lower limit prediction model according with a preset confidence level, deploying the model in a calibration set, calculating to obtain an empirical quantile, inputting test data into the constructed model, determining a final prediction interval, and realizing interval prediction of ultra-short-term photovoltaic power.

The invention discloses a preparation method of a composite micro-groove super-hydrophobic low-ice-adhesion photo-thermal film. The preparation method comprises the following steps: (1) constructing a low-elasticity-modulus photo-thermal composite sponge substrate through a solvothermal method; (2) uniformly coating the surface of a substrate with a polydimethylsiloxane (PDMS) precursor solution by adopting an aerosol-assisted deposition technology, and carrying out thermocuring to form a functional composite interface layer; and (3) constructing a multi-stage scallop surface-imitating micro-nano groove composite structure on the surface of the interface layer by using a femtosecond laser micro-nano processing technology. The prepared film has super-hydrophobicity, the surface of the film is in a Cassie-Baxter wetting state with a 164-degree contact angle and a 1-degree rolling angle, and the film has a surface self-cleaning function. Under the icing condition, the adhesion strength of an ice layer of the system is obviously reduced to 2.57 kPa, the static delay icing time is effectively prolonged to 1587 s, and the system has the photo-thermal response characteristic and can realize rapid icing ablation under the standard solar irradiance; the preparation method has the characteristics of high process controllability, environment friendliness and the like, and is suitable for large-scale continuous production.

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 provides a solar energy-radio frequency energy dual-driven low-altitude polarization switching dynamic power management system, and relates to the technical field of low-altitude flight. The system integrates a high-efficiency solar energy collection unit and a radio frequency energy collection unit with dynamic polarization adaptive switching capability, is assisted by a high-performance hybrid energy storage unit, and carries out unified coordination control through an intelligent power management unit. The radio frequency energy collection unit adopts a reconfigurable antenna technology, can sense the polarization characteristic of an environment radio frequency signal in real time, and dynamically switches the polarization state of an antenna to maximize the instantaneous energy capture efficiency; the intelligent power management unit executes a dynamic and collaborative energy flow scheduling strategy based on comprehensive analysis of solar irradiance, environment radio frequency spectrum and polarization characteristics, energy storage unit state, future energy income and expenditure prediction and real-time load demand; and the energy self-sustaining capability, the task endurance time and the working stability of low-altitude equipment in a complex and changeable environment 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 photovoltaic abnormal data processing method and related equipment, and the method comprises the steps: firstly collecting the historical total power and solar irradiance data of a target photovoltaic field station, generating a first data set through negative value zeroing and obvious abnormality elimination, and eliminating error data caused by sensor faults and the like; and calculating the neighborhood density deviation degree of data points by using a local abnormal factor algorithm, identifying and eliminating discrete abnormal points generated by instant faults of the sensor and the like to obtain a second data set, and eliminating the interference of discrete data on image identification. Then, the second data set is preprocessed to generate a scatter diagram, accumulation type abnormal areas caused by sudden change of system parameters and the like are recognized by means of image recognition technologies such as edge detection and the like, normal data are marked, and a final data set is output through reverse normalization. According to the method, two algorithms are fused, discrete abnormal points are recognized in a self-adaptive mode, accumulation type anomalies are analyzed based on morphological characteristics, normal data mistaken deletion is reduced, and the anomaly detection accuracy is 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 relates to a daytime passive radiation refrigeration coating material and a preparation method and application thereof.The preparation method comprises the steps that perovskite oxide Sr (Zr < 1-y > Xy) Oz ceramic, polyvinylidene fluoride and an N-methyl pyrrolidone solution are blended, and organic-inorganic mixed slurry is obtained; coating the slurry on the surface of a substrate through a blade coating method, a pulling method or a cold spraying process; and airing or drying to obtain the product. The coating material disclosed by the invention has excellent sunlight high reflectivity and mid-infrared high emissivity, so that the coating can effectively dissipate heat to outer space in a daytime high-temperature environment, and a passive cooling effect is realized. Under the conditions that the sunlight intensity is 772 W / m < 2 > and the environment relative humidity is 19%, the refrigeration performance of 6.8 DEG C can be achieved. Meanwhile, the slurry formula and the preparation process of the coating are simple and easy to implement, raw materials are easy to obtain, the cost is low, the coating is suitable for various coating processes, and stable coatings can be formed on the surfaces of matrixes in different shapes.

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 present invention belongs to the field of architectural design and discloses a method for calculating a building's solar energy acquisition potential index. The method comprises the following steps: obtaining the solar azimuth and solar altitude corresponding to sunrise and sunset to form a fan-shaped calculation range, and obtaining a plurality of calculation line segments within the fan-shaped calculation range; obtaining the tangent value of the solar altitude at the time corresponding to the calculation line segment, and the ratio of the height of the blocking building to the distance between the blocking building and the building to be measured, and subtracting the tangent value of the solar altitude from the ratio to obtain a difference; obtaining the approximate direct solar irradiance at the time corresponding to the calculation line segment, and assigning the approximate direct solar irradiance as a weight to the difference to obtain a weighted value; obtaining the central angle ratio of the area to be measured, and obtaining the building shielding factor of the building to be measured based on the central angle ratio and the weighted value. The present invention can be used to compare, select, and optimize different building complex layout plans from the perspective of solar energy acquisition potential.

The invention belongs to the technical field of radiation refrigeration, and provides a radiation refrigeration power testing device and method, and the device comprises a testing device main body which comprises a case, and a water-cooling reverse synchronous verification module, a heating constant-temperature power testing module and a synchronous parallel comparison testing module which are disposed in the case; the small meteorological station is independently arranged around the testing device main body, comprises a temperature sensor, a humidity sensor, a wind speed sensor, an atmospheric pressure sensor, a solar irradiance sensor and a wireless transmission module, and is used for monitoring and transmitting environmental parameters in real time; and the monitoring terminal is used for receiving and storing the environment parameters and the test data sent by the wireless transmission module. Compared with the prior art, the water-cooling reverse synchronous verification module and the heating constant-temperature power test module are introduced, so that the accuracy of a test result can be verified in real time, active correction is performed through comparison with a theoretical value, and the accuracy of the test result is ensured.