复杂地形区晴空太阳辐射模型构建与应用

以昆仑山提孜那甫河流域为研究区,利用中分辨率成像光谱仪(MODIS)提供的大气数据基于Iqbal Model C模型估算晴空大气透射率空间分布,并引入地形开阔度(SVF)和遥感地表反照率数据分别用于估算散射辐射地形阻挡以及反射辐射反照率系数空间分布,最后结合Kumar模型的直接辐射地形阻挡模拟过程,实现对Kumar模型的改进,改进后模型综合考虑了大气以及地形对太阳辐射的影响。利用改进后模型对研究区地表太阳辐射时空分布进行模拟和分析,基于地面气象站点观测数据对模拟结果进行验证。结果表明：模型估算值与站点观测值存在很好的一致性,相关系数R²为0.96,平均绝对误差(MAE)为1.47 MJ/m²,平均绝对相对误差(MARE)为12.26%。春季、夏季以及秋季模型的模拟精度较高,冬季模型的模拟精度较低,可能的原因为冬季MODIS大气数据有所低估。     

区域复杂地形晴日太阳辐射估算研究

为改进复杂地形区区域地表接收太阳入射辐射的算法,基于数字高程模型、气象数据与遥感数据,分直接辐射、来自天空的散射辐射及来自周围地形的反射辐射3部分对海河流域2001—2019年区域地表接收的晴日太阳辐射日总量进行估算与分析。经验证,模拟的晴日太阳入射辐射日通量与实测数据吻合度较高,相关系数约为0.9,区域不同地形接收的太阳入射辐射空间差异明显,结果在可接受精度范围内,此估算方法可为山区太阳能的合理利用提供科学基础。     

基于风云卫星数据的新疆太阳能资源潜力评价方法研究

利用风云二号(FY-2D)和风云三号(FY-3A)气象卫星上搭载的可见光辐射自旋扫描分析仪和中分辨率光谱成像仪等传感器数据,基于大气辐射传输原理建立地面太阳直射与散射辐射反演模型,并用来对新疆地区每小时的直射和散射太阳辐射能进行估算;再用地面气象观测站点数据对反演结果进行精度评价。结果表明,所提出的方法能较好地估算新疆地区高时间分辨率的地表太阳辐射,为该区域太阳能资源的利用提供基础数据支撑。     

基于GA-BP神经网络的逐时总辐射分组模型研究

通过分析影响太阳辐射的主要因素,提出以太阳高度角、季节和天气（晴空指数）作为数据划分依据的分组模型建立方法。以拉萨和西安地区的逐时气象数据和辐射数据为例,基于遗传算法（genetic algorithm,GA）优化的BP神经网络,建立太阳高度角、季节和天气类型的逐时总辐射分组模型。该研究揭示分组模型误差变化的规律,并将其估算误差与AllData模型比较。结果显示,相较于AllData模型,分组模型的估算误差均有降低。其中,天气分组模型误差最小,且西安的天气分组模型结果优于拉萨。西安天气分组模型平均绝对百分比误差（MAPE）和相对均方根误差（rRMSE）相较AllData模型结果分别下降3.96%和4.18%。研究结果表明分组模型能够降低逐时总辐射估算误差,可为估算逐时总辐射提供方法借鉴。     

神经网络模型在逐时太阳辐射预测中应用

设计了一种基于遗传算法的神经网络太阳辐射预测模型。该模型结合了历史逐时辐射数据和气象要素数据,并在训练和预测时加入了温度日较差和天气类型预报参数。还设计了预测因子选择方法、输入资料的处理方法和结果误差评估方法。利用武汉站2007年至2008年8月辐射数据对模型进行了训练,并对2009年8月的逐时辐射进行了诊断预报。预测结果表明,预测模型在天气类型稳定的情况下具有较高的精度,能够反映太阳辐射的日变化状况和辐射量级大小,但在天气类型剧烈变化的情况下预测精度有限。     

结合太阳辐射量计算与CNN-LSTM组合的光伏功率预测方法研究

为了提高模型预测性能,提出一种综合太阳辐射模型及深度学习的光伏功率预测模型。首先,利用太阳辐射机理建立太阳辐射模型（SRM）,估算出水平面上总辐射值,再由斜面辐照度转换方法计算出光伏组件所接收的斜面辐射值。其次,通过皮尔逊相关分析法筛选出对光伏功率影响较大的主要因素,将斜面辐射计算值及主要影响因素作为输入,采用卷积神经网络（CNN）和长短期记忆网络（LSTM）建立光伏功率SRM-CNN-LSTM预测模型。分别利用春夏秋冬四季典型日的数据开展对比实验,结果表明：与几种其他方法相比,该文方法具有更好的预测效果。     

倾斜面太阳辐照度实用计算模型的研究

研究基于水平面上太阳辐照度数据计算任意倾斜面上对应太阳辐照度的方法,通过对直射、散射及反射现象的观察与分析,结合完整的实测数据,提出两种简单实用的计算模型。与经典模型相比,新模型所需的输入数据少,过程简单且结果准确。能够有效利用全国各地气象观测站的典型年太阳辐射数据,对不同朝向上的太阳能资源进行综合评价,建立相应的数据库,为太阳能发电系统的系统设计、工程安装和效益评估提供客观依据和科学指导。     

基于MLP-Garson模型的分钟尺度太阳辐照直、散射分离建模与验证研究

为模拟分钟尺度的太阳辐射波动,根据江苏省常州市2018—2021年逐分钟辐射数据,采用Garson权重算法优化模型输入特征,并引入前10分钟的清晰度指数k_t时序数据作为附加特征,建立基于时序数据与MLP神经网络的分钟尺度新分离模型。在此基础上,对Engerer2模型、Starke模型和Yang模型3个最新提出的分钟尺度分离模型进行参数本地优化,并设计测试实验验证。验证结果表明：采用时序数据与MLP神经网络的新模型可有效提取短时间内的太阳辐射波动信息,新模型的归一化均方根误差(e_nRMSE)为10.690%,新模型精度较Yang模型提高了17.08%。     

高层住宅建筑南立面太阳能热水系统水量配比特性研究

利用日均太阳辐射模型与集热器能量输出模型对平板型和真空管型太阳能热水系统水量配比进行建模分析。结果显示,模拟的水量配比与实验测试吻合较好,相对误差在10%以内。基于我国五大气候分区133个城市的气象数据,利用此模型对集热器件与建筑南立面大角度(70°～90°)集成后的太阳能热水系统的水量配比进行模拟计算与分析。为便于工程应用,利用多项式线性回归技术对水量配比与气象参数间的关系进行回归分析与整理。给出各类气候区夏半年和冬半年太阳能热水系统水量配比与气象参数间的回归关系式,为太阳集热器件与建筑南立面大角度集成的水量配比的优化提供方便。     

基于机器自学习的供热系统热负荷预测

传统的集中供暖系统热负荷预测取决于操作人员的经验，与系统实际热负荷相差过大，容易造成热用户侧温度过高或过低，影响热用户体验，不利于系统节能。采用机器自学习的方法，对大连某供热系统2019年至2021年的系统数据进行处理，基于多元线性回归、多项式回归、岭回归、Lasso回归、神经网络算法建立供热系统热负荷预测模型，并对比预测效果。结果显示：采用BP神经网络模型的供热系统热负荷预测结果比经验预测结果高8.7%，随着模型不断地自学习与自优化，预测结果的精度可进一步提高。     

Computation of monthly mean daily global solar radiation in China using artificial neural networks and comparison with other empirical models

In this paper, an artificial neural network (ANN) model is developed for estimating monthly mean daily global solar radiation of 8 typical cities in China. The feed-forward back-propagation algorithm is applied in this analysis. The results of the ANN model and other empirical regression models have been compared with measured data on the basis of mean percentage error (MPE), mean bias error (MBE) and root mean square error (RMSE). It is found that the solar radiation estimations by ANN are in good agreement with the measured values and are superior to those of other available empirical models. In addition, ANN model is tested to predict the same components for Kashi, Geermu, Shenyang, Chengdu and Zhengzhou stations over the same period. Data for Kashi, Geermu, Shenyang, Chengdu and Zhengzhou are not used in the training of the networks. Results obtained indicate that the ANN model can successfully be used for the estimation of monthly mean daily global solar radiation for Kashi, Geermu, Shenyang, Chengdu and Zhengzhou. These results testify the generalization capability of the ANN model and its ability to produce accurate estimates in China.     

Prediction of monthly mean daily diffuse solar radiation using artificial neural networks and comparison with other empirical models

An artificial neural network (ANN) model for estimating monthly mean daily diffuse solar radiation is presented in this paper. Solar radiation data from 9 stations having different climatic conditions all over China during 1995–2004 are used for training and testing the ANN. Solar radiation data from eight typical cities are used for training the neural networks and data from the remaining one location are used for testing the estimated values. Estimated values are compared with measured values in terms of mean percentage error (MPE), mean bias error (MBE) and root mean square error (RMSE). The results of the ANN model have been compared with other empirical regression models. The solar radiation estimations by ANN are in good agreement with the actual values and are superior to those of other available models. In addition, ANN model is tested to predict the same components for Zhengzhou station over the same period. Results indicate that ANN model predicts the actual values for Zhengzhou with a good accuracy of 94.81%. Data for Zhengzhou are not included as a part of ANN training set. Hence, these results demonstrate the generalization capability of this approach and its ability to produce accurate estimates in China.     

Assessment of diffuse solar energy under general sky condition using artificial neural network

In this paper, artificial neural network (ANN) models are developed for estimating monthly mean hourly and daily diffuse solar radiation. Solar radiation data from 10 Indian stations, having different climatic conditions, all over India have been used for training and testing the ANN model. The coefficient of determination (R²) for all the stations are higher than 0.85, indicating strong correlation between diffuse solar radiation and selected input parameters. The feedforward back-propagation algorithm is used in this analysis. Results of ANN models have been compared with the measured data on the basis of percentage root-mean-square error (RMSE) and mean bias error (MBE). It is found that maximum value of RMSE in ANN model is 8.8% (Vishakhapatnam, September) in the prediction of hourly diffuse solar radiation. However, for other stations same error is less than 5.1%. The computation of monthly mean daily diffuse solar radiation is also carried out and the results so obtained have been compared with those of other empirical models. The ANN model shows the maximum RMSE of 4.5% for daily diffuse radiation, while for other empirical models the same error is 37.4%. This shows that ANN model is more accurate and versatile as compared to other models to predict hourly and daily diffuse solar radiation.     

Generation of synthetic daily global solar radiation data based on ERA-Interim reanalysis and artificial neural networks

Four variables (total cloud cover, skin temperature, total column water vapour and total column ozone) from meteorological reanalysis were used to generate synthetic daily global solar radiation via artificial neural network (ANN) techniques. The goal of our study was to predict solar radiation values in locations without ground measurements, by using the reanalysis data as an alternative to the use of satellite imagery. The model was validated in Andalusia (Spain), using measured data for nine years from 83 ground stations spread over the region. The geographical location (latitude, longitude), the day of the year, the daily clear sky global radiation, and the four meteorological variables were used as input data, while the daily global solar radiation was the only output of the ANN. Sixty five ground stations were used as training dataset and eighteen stations as independent dataset. The optimum network architecture yielded a root mean square error of 16.4% and a correlation coefficient of 94% for the testing stations. Furthermore, we have successfully tested the forecasting capability of the model with measured radiation values at a later time. These results demonstrate the generalization capability of this approach over unseen data and its ability to produce accurate estimates and forecasts.     

Modelling of solar energy potential in Nigeria using an artificial neural network model

In this study, an artificial neural network (ANN) based model for prediction of solar energy potential in Nigeria (lat. 4–14°N, log. 2–15°E) was developed. Standard multilayered, feed-forward, back-propagation neural networks with different architecture were designed using neural toolbox for MATLAB. Geographical and meteorological data of 195 cities in Nigeria for period of 10 years (1983–1993) from the NASA geo-satellite database were used for the training and testing the network. Meteorological and geographical data (latitude, longitude, altitude, month, mean sunshine duration, mean temperature, and relative humidity) were used as inputs to the network, while the solar radiation intensity was used as the output of the network. The results show that the correlation coefficients between the ANN predictions and actual mean monthly global solar radiation intensities for training and testing datasets were higher than 90%, thus suggesting a high reliability of the model for evaluation of solar radiation in locations where solar radiation data are not available. The predicted solar radiation values from the model were given in form of monthly maps. The monthly mean solar radiation potential in northern and southern regions ranged from 7.01–5.62 to 5.43–3.54 kW h/m² day, respectively. A graphical user interface (GUI) was developed for the application of the model. The model can be used easily for estimation of solar radiation for preliminary design of solar applications.     

Daily UV radiation modeling with the usage of statistical correlations and artificial neural networks

The information regarding solar UV radiation (UVA + UVB) in Brazil and around the world is scarce with low spatial and temporal coverage. This information scarcity, due to the small number of measuring stations, has directed some researchers towards the creation of computational parametric models or the generation of statistical models for the estimation of the UV radiation from the measurement of the global radiation. Information about UV irradiation is expanded for other places where there is only global radiation data. Thus, two stations were set up, in 2008, one in the city of Pesqueira and the other in Araripina, both in the state of Pernambuco, for simultaneous measurements of daily global solar and UV radiation. Another station is being set up in Recife-PE, completing a group of stations that are located between latitudes 8 and 10° and longitudes 34–38° W, representing the typical climate of the region. The daily values of G global and UV ultraviolet radiation (A + B) striking the horizontal plane in Pesqueira and Araripina during the time period (2008–2010) were measured, analyzed and compared. The collected data enabled the generation of three different statistical models for estimating the daily UV solar radiation from the daily global radiation: a) linear correlation between global and UV radiation (model 1), b) polynomial correction of the average fraction of UV irradiation, 〈F_UV〉 as a function of the transmittance index of global solar irradiation 〈K_T〉 (model 2) and c) the UV atmospheric transmittance index 〈K_TUV〉 is obtained by multiple regression of the air mass 〈m_r〉and 〈K_T〉 (model 3). Besides, they were modeled by two artificial neural networks: a) estimative of the (F_UV), considering the same physical variables of model 2 (model ANN1) and b) estimative of (K_TUV) from the same physical variables of model 3 (model ANN2). The statistical models and the artificial neural networks displayed a good statistical performance with RMSE% inferior to 5% and MBE between ?0.4%–2%. All the models can be used for estimating the UV radiation in places where there is only global irradiation data.     

Monthly mean hourly global solar radiation estimation

In this paper, selected empirical models were used to estimate the monthly mean hourly global solar radiation from the daily global radiation at three sites in the east coast of Malaysia. The purpose is to determine the most accurate model to be used for estimating the monthly mean hourly global solar radiation in these sites. The hourly global solar radiation data used for the validation of selected models were obtained from the Malaysian Meteorology Department and University Malaysia Terengganu Renewable Energy Station. In order to indicate the performance of the models, the statistical test methods of the normalized mean bias error, normalized root mean square error, correlation coefficient and t-statistical test were used. The monthly mean hourly global solar radiation values were calculated by using six models and the results were compared with corresponding measured data. All the models fit the data adequately and can be used to estimate the monthly mean hourly global solar radiation. This study finds that the Collares-Pereira and Rabl model performed better than the other models. Therefore the Collares-Pereira and Rabl model is recommended to estimate the monthly mean hourly global radiations for the east coast of Malaysia with humid tropical climate and in elsewhere with similar climatic conditions.     

Effects of air pollution for estimating global solar radiation in India

Solar energy is the primary resource for all biological, chemical and physical processes. The amount of global solar radiation is an important parameter for solar energy applications. It is common to estimate a monthly average of daily global solar radiation using different regression models. These models in turn exploit the correlation between solar radiation and various atmospheric factors. These factors are commonly derived from meteorological, geographical and climatological data that are readily available for majority of weather stations across the world. In this paper, a novel regression model that can predict location-independent daily global solar radiation is presented. The proposed exponential quadratic model captures the correlation between measured global solar radiation values, sunshine hour and Air Pollution Index for Indian cities. In addition to this, an extended study of several other regression models (e.g. linear, quadratic, exp.-linear and exp.-quadratic) is also presented. This analysis with real data from Indian cities suggests that air pollution is a more significant factor than location when predicting solar radiation. Finally, the model parameters (regression coefficients) for each model are listed out. Additionally, the generalised model equation for the best performing model is also presented.     

Clear-sky solar luminous efficacy determination using artificial neural networks

Under cloudless conditions, the effect of atmospheric variables, such as turbidity or water vapour, on luminous efficacy is an important source of variability, often limiting the use of simple empirical models to those sites where they were developed. Due to the complex functional relationship between these atmospheric variables and the luminous efficacy components, deriving a non-local model considering all these physical processes is nearly impossible if standard statistical techniques are employed. To avoid this drawback, the use of a new methodology based on artificial neural networks (ANN) is investigated here to determine the luminous efficacy of direct, diffuse and global solar radiation under cloudless conditions. In this purpose, a detailed spectral radiation model (SMARTS) is utilized to generate both illuminance and solar radiation values covering a large range of atmospheric conditions. Different input configurations using combinations of atmospheric variables and radiometric quantities are analyzed. Results show that an ANN model using direct and diffuse solar irradiance along with precipitable water is able to accurately reproduce the variations of the three components of luminous efficacy caused by solar zenith angle and the various atmospheric absorption and scattering processes. This proposed model is considerably simpler than the SMARTS radiation model it is derived from, but still can retain most of its predicting power and versatility. The proposed ANN model can thus be used worldwide, avoiding the need of using detailed atmospheric information or empirical models of the literature if radiometric measurements and precipitable water data (or temperature and relative humidity data) are available.     

Techniques for the precise estimation of hourly values of global, diffuse and direct solar radiation

The method usually used to compute solar radiation, when no measured data are available, is the well-known regression technique relating mean daily totals of global and diffuse solar radiation with the mean duration of sunshine. Using this method and taking into account the first order multiple reflections between the ground and the atmosphere, regression parameters were obtained from the monthly mean values of daily totals of global solar radiation and sunshine at a network of 16 stations in India. Daily values of global and diffuse solar radiation were then computed for 121 stations, where sunshine data are available for periods of 6–28 yr, using interpolated values of the regression parameters. Where no sunshine data were available, global and diffuse solar radiation were computed from cloud observations, using the inverse relationship between sunshine and cloudiness. Further, using the empirical relationship between daily totals and the corresponding hourly values of global and diffuse solar radiation, two sets of curves were prepared valid for the whole country, using which mean hourly values of global and diffuse radiation could be deduced from the corresponding daily totals, with a high degree of accuracy. The paper discusses the validity of the techniques used for computing daily and hourly values of global and diffuse solar radiation from sunshine and cloud amounts at an extended network of 145 stations in India and stresses the fact that such techniques are successful, only if accurate data on both radiation and sunshine are available at a widely distributed network of stations for a minimum period from at least 5 to 6 yr, using carefully calibrated and well-maintained instruments of the required quality. Theoretical models have also been used to compute clear sky noon values of global, diffuse and direct solar radiation from the solar constant, allowing for attenuation by atmospheric constituents such as ozone, water vapour, dust and aerosols. Using a simple model, calculations of global and diffuse solar radiation on clear days were made for 145 stations from values of the solar constant and measured values of ozone, water vapour and atmospheric turbidity. A method of extending the technique to overcast skies and partly clouded skies is discussed. The values of the mean annual transmission factor for global solar radiation under cloud-free conditions using the two methods show excellent agreement and establishes the soundness of the regression technique on one hand and the reliability of the theoretical model used for computing clear sky radiation, on the other.