Estimation of monthly average daily global solar irradiation using artificial neural networks

This study explores the possibility of developing a prediction model using artificial neural networks (ANN), which could be used to estimate monthly average daily global solar irradiation on a horizontal surface for locations in Uganda based on weather station data: sunshine duration, maximum temperature, cloud cover and location parameters: latitude, longitude, altitude. Results have shown good agreement between the estimated and measured values of global solar irradiation. A correlation coefficient of 0.974 was obtained with mean bias error of 0.059 MJ/m² and root mean square error of 0.385 MJ/m². The comparison between the ANN and empirical method emphasized the superiority of the proposed ANN prediction model.     

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.     

Improvement in artificial neural network-based estimation of grid connected photovoltaic power output

This paper presents a method to improve the accuracy of artificial neural network (ANN)–based estimation of photovoltaic (PV) power output by introducing two more inputs, solar zenith angle and solar azimuth angle, in addition to the most widely used environmental information, plane-of-array irradiance and module temperature. Solar zenith angle and solar azimuth angle define the solar position in the sky; hence, the loss of modeling accuracy due to impacts of solar angle-of-incidence and solar spectrum is reduced or eliminated. The observed data from two sites where local climates are significantly different is used to train and test the proposed network. The good performance of the proposed network is verified by comparing with existing ANN model, algebraic model, and polynomial regression model which use environmental information only of plane-of-array irradiance and module temperature. Our results show that the proposed ANN model greatly improves the accuracy of estimation in the long term under various weather conditions. It is also demonstrated that the improvement in estimating outdoor PV power output by adding solar zenith angle and azimuth angle as inputs is useful for other data-driven methods like support vector machine regression and Gaussian process regression.     

Improved artificial neural network method for predicting photovoltaic output performance

To ensure the safety and stability of power grids with photovoltaic (PV) generation integration, it is necessary to predict the output performance of PV modules under varying operating conditions. In this paper, an improved artificial neural network (ANN) method is proposed to predict the electrical characteristics of a PV module by combining several neural networks under different environmental conditions. To study the dependence of the output performance on the solar irradiance and temperature, the proposed neural network model is composed of four neural networks, it called multi- neural network (MANN). Each neural network consists of three layers, in which the input is solar radiation, and the module temperature and output are five physical parameters of the single diode model. The experimental data were divided into four groups and used for training the neural networks. The electrical properties of PV modules, including I–V curves, P– V curves, and normalized root mean square error, were obtained and discussed. The effectiveness and accuracy of this method is verified by the experimental data for different types of PV modules. Compared with the traditional single-ANN (SANN) method, the proposed method shows better accuracy under different operating conditions.     

Computation of beam solar radiation at normal incidence using artificial neural network

In this paper, an artificial neural network (ANN) model is developed for estimating beam solar radiation. Introducing a newly defined parameter, known as reference clearness index (RCI), computation of monthly mean daily beam solar radiation at normal incidence has been carried out. This RCI is defined as the ratio of measured beam solar radiation at normal incidence to the beam solar radiation as computed by Hottel's clear day model. Solar radiation data from 11 stations having different climatic conditions all over India have been used for training and testing the ANN. The feedforward back-propagation algorithm is used in this analysis. The results of ANN model have been compared with measured data on the basis of root mean square error (RMSE) and mean bias error (MBE). It is found that RMSE in the ANN model varies 1.65–2.79% for Indian region.     

基于长短期记忆神经网络的地表太阳辐照度预测

针对地表太阳辐照度(GHI)短期预测问题,提出一种基于长短期记忆神经网络的短期太阳辐照度预测模型。采用递归结构的训练样本,以保证训练样本内部的时间耦合性。为验证所提模型预测GHI的有效性,采用算例与传统人工神经网络模型预测结果进行对比分析。结果表明:基于长短期记忆神经网络预测模型将均方误差降低88.48%,表明所建模型更适用于GHI预测。     

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.     

基于CA-SE-GA-BP的光伏发电功率预测

针对光伏发电功率预测精度低的问题,以澳大利亚爱丽丝泉地区某200kW的光伏电站为例,选用遗传算法(GA)优化BP神经网络,采用相关性分析法(CA)确定太阳辐照度、温度、湿度为影响光伏发电功率的主要因子,结合经样本熵(SE)量化的天气类型作为模型输入量,提出CA-SE-GA-BP神经网络的光伏发电功率预测模型。结果表明,多云天气下CA-SE-GA-BP神经网络均方根误差、平均绝对百分比误差分别为4.48%、2.27%,晴天、雾霾、雨天三种天气类型下的预测误差也基本上不超过10%,相较于SE-GA-BP、CA-GA-BP、GA-BP神经网络,CA-SE-GA-BP神经网络预测误差降低,为解决光伏系统发电功率预测提供了一种高效准确可行的方法。     

Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network

This study deals with artificial neural network (ANN) modeling of a diesel engine using waste cooking biodiesel fuel to predict the brake power, torque, specific fuel consumption and exhaust emissions of the engine. To acquire data for training and testing the proposed ANN, a two cylinders, four-stroke diesel engine was fuelled with waste vegetable cooking biodiesel and diesel fuel blends and operated at different engine speeds. The properties of biodiesel produced from waste vegetable oil was measured based on ASTM standards. The experimental results revealed that blends of waste vegetable oil methyl ester with diesel fuel provide better engine performance and improved emission characteristics. Using some of the experimental data for training, an ANN model was developed based on standard Back-Propagation algorithm for the engine. Multi layer perception network (MLP) was used for non-linear mapping between the input and output parameters. Different activation functions and several rules were used to assess the percentage error between the desired and the predicted values. It was observed that the ANN model can predict the engine performance and exhaust emissions quite well with correlation coefficient (R) 0.9487, 0.999, 0.929 and 0.999 for the engine torque, SFC, CO and HC emissions, respectively. The prediction MSE (Mean Square Error) error was between the desired outputs as measured values and the simulated values were obtained as 0.0004 by the model.     

Modeling of heat transfer and energy analysis of potato slices and cylinders during solar drying

In the present work, a method based on energy balance considering the effects of heat capacity of the food product, radiative heat transfer from food product to the drying chamber and solar radiation absorbed in the product during drying is proposed for determination of convective heat transfer coefficient, h_c. A natural convection mixed-mode solar dryer is used for performing the experiments on potato cylinders and slices of same thickness of 0.01 m with respective length and diameter of 0.05 m. The present investigation indicates that the cylindrical samples exhibit higher values of h_c and faster drying rate compared to those of slices, as expected. The h_c values for each sample shape are correlated by an equation of the form Nu = C(Ra)ⁿ. Laplace transform is applied to solve the proposed heat transfer diffusion model considering the effect of moisture transfer rate to predict the transient sample temperature. The model is validated through a close agreement between calculated and experimental results of transient sample temperature. Results of energy analysis reveal that for both the sample geometries, decreasing product moisture content during drying resulted in significant reduction in specific energy consumption. For almost similar drying conditions, a considerable amount of reduction in specific energy consumption is achieved for cylinders, as expected.     

基于GRU-RF模型的太阳辐照度短时预测

针对现有太阳辐照度短期预测方法的建模复杂、准确度低等问题,提出一种基于深度学习的GRU-RF动态权值组合预测方法。大气因素与太阳辐照度数据融合,将运算速度较快且模型复杂度较低的随机森林（RF）模型与带有时序记忆的门控循环单元（GRU）神经网络进行动态权值的加权集成,分别将地表接收到的太阳辐照度、近地层气温、相对湿度、近地层风速和相对气压等变化特征进行预测研究。通过几种模型对比分析,结果表明使用GRU-RF模型预测短时（9 h）太阳辐照度结果较好,运行速度较快,在不同时间间隔（5、10以及15 min）下能够很好地预测太阳辐照度数据。     

Performance analysis of a hybrid photovoltaic thermal double pass air collector using ANN

This paper presents the use of artificial neural network for performance analysis of a semi transparent hybrid photovoltaic thermal double pass air collector for four weather conditions (a, b, c and d type) of New Delhi. The MATLAB 7.1 neural networks toolbox has been used for defining and training of ANN for calculations of thermal energy, electrical energy, overall thermal energy and overall exergy. The ANN model uses ambient air temperature, global solar radiation, diffuse radiation and number of clear days as input parameters for four weather conditions. The transfer function, neural network configuration and learning parameters have been selected based on highest convergence during training and testing of network. About 2000 sets of data from four weather stations (Bangalore, Mumbai, Srinagar, and Jodhpur) have been given as input for training and data of the fifth weather station (New Delhi) has been used for testing purpose. It has been observed that the best transfer function for a given configuration is logsig. The feedforward back-propagation algorithm has been used in this analysis. Further the results of ANN model have been compared with analytical values on the basis of root mean square error.     

Simulation model of ANN based maximum power point tracking controller for solar PV system

In this paper the simulation model of an artificial neural network (ANN) based maximum power point tracking controller has been developed. The controller consists of an ANN tracker and the optimal control unit. The ANN tracker estimates the voltages and currents corresponding to a maximum power delivered by solar PV (photovoltaic) array for variable cell temperature and solar radiation. The cell temperature is considered as a function of ambient air temperature, wind speed and solar radiation. The tracker is trained employing a set of 124 patterns using the back propagation algorithm. The mean square error of tracker output and target values is set to be of the order of 10⁻⁵ and the successful convergent of learning process takes 1281 epochs. The accuracy of the ANN tracker has been validated by employing different test data sets. The control unit uses the estimates of the ANN tracker to adjust the duty cycle of the chopper to optimum value needed for maximum power transfer to the specified load.     

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.     

Artificial neural networks for energy analysis of office buildings with daylighting

An artificial neural network (ANN) model was developed for office buildings with daylighting for subtropical climates. A total of nine variables were used as the input parameters – four variables were related to the external weather conditions (daily average dry-bulb temperature, daily average wet-bulb temperature, daily global solar radiation and daily average clearness index), four for the building envelope designs (solar aperture, daylight aperture, overhang and side-fins projections), and the last variable was day type (i.e. weekdays, Saturdays and Sundays). There were four nodes at the output layer with the estimated daily electricity use for cooling, heating, electric lighting and total building as the output. Building energy simulation using EnergyPlus was conducted to generate daily building energy use database for the training and testing of ANNs. The Nash–Sutcliffe efficiency coefficient for the ANN modelled cooling, heating, electric lighting and total building electricity use was 0.994, 0.940, 0.993, and 0.996, respectively, indicating excellent predictive power. Error analysis showed that lighting electricity use had the smallest errors, from 0.2% under-estimation to 3.6% over-estimation, with the coefficient of variation of the root mean square error ranging from 3% to 5.6%.     

Exergy analysis of direct expansion solar‐assisted heat pumps using artificial neural networks

Artificial neural network (ANN) is applied for exergy analysis of a direct expansion solar‐assisted heat pump (DXSAHP) in the present study. The experiments were conducted in a DXSAHP under the meteorological conditions of Calicut city in India. An ANN model was developed based on backpropagation learning algorithm for predicting the exergy destruction and exergy efficiency of each component of the system at different ambient conditions (ambient temperature and solar intensity). The experimental data acquired are used for training the network. The results showed that the network yields a maximum correlation coefficient with minimum coefficient of variance and root mean square values. The results confirmed that the use of an ANN analysis for the exergy evolution of DXSAHP is quite suitable. Copyright © 2009 John Wiley & Sons, Ltd.     

Hydrogen purification layered bed optimization based on artificial neural network prediction of breakthrough curves

Artificial neural network has generally been used for a quantity of tasks such as classification, prediction, clustering and association analysis in different application fields. To the best of our knowledge, there are few researches on breakthrough curve used artificial neural network. In this paper, an artificial neural network model is established for breakthrough curves prediction in relation to a ternary components gas with a two-layered adsorbent bed piled up with activated carbon (AC) and zeolite, and an optimization is concluded by the artificial neural network. The performance data which acquired by Aspen model has been utilized for training artificial neural network (ANN) model. The ANN model trained has great competence for making prediction of hydrogen purification performance of PSA cycle with impressive speed and rational accuracy. On the strength of the ANN model, we implemented an optimization for seeking first-rank PSA cycle parameters. The optimization is concentrated on the effect of inlet flow rate, pressure and layer ratio of activated carbon height to zeolite height. Furthermore, this paper shows that the PSA cycle's optimal operation parameters can be obtained by use of ANN model and optimization algorithm, the ANN model has been trained according to the data generated by Aspen adsorption model.     

Study of flow field,heat transfer,and entropy generation of nanofluid turbulent natural convection in an enclosure utilizing the computational fluid dynamics‐artificial neural network hybrid method

In this study, the turbulent natural convection of Ag‐water nanofluid in a tall, inclined enclosure has been investigated. The main objective of this study is finding the optimized angle of the enclosure with operational boundary condition in cooling from ceiling utilizing the computational fluid dynamics‐artificial neural network (CFD‐ANN) hybrid method, which has not been noticed in previous studies. To achieve this, we proposed two approaches. First, the simulations have been done with a deviation angle of 0 to 90° by using water and Ag‐water nanofluid. And second, a new prediction approach is proposed based on radial basis function artificial neural networks (RBF‐ANN) to predict the mean Nusselt number and entropy generation with the variation of Rayleigh numbers, deviation angles, and volume fractions as inputs. The results from the first approach indicate that the Rayleigh number has a considerable function in the determination of optimized angle. The results from the second approach, which used the first approach simulation results as training data set, could predict the mean Nusselt number and entropy generation with 1.4577e^?022 and 1.552e^?015 mean square error, respectively. Moreover, a new set of data for Rayleigh numbers, deviation angles, and volume fractions were used to test the performance of the prediction model, which shows promising and superior prospects for RBF‐ANN.     

A simplified model for generating sequences of global solar radiation data for isolated sites: Using artificial neural network and a library of Markov transition matrices approach

The purpose of this work is to develop a hybrid model which will be used to predict the daily global solar radiation data by combining between an artificial neural network (ANN) and a library of Markov transition matrices (MTM) approach. Developed model can generate a sequence of global solar radiation data using a minimum of input data (latitude, longitude and altitude), especially in isolated sites. A data base of daily global solar radiation data has been collected from 60 meteorological stations in Algeria during 1991–2000. Also a typical meteorological year (TMY) has been built from this database. Firstly, a neural network block has been trained based on 60 known monthly solar radiation data from the TMY. In this way, the network was trained to accept and even handle a number of unusual cases. The neural network can generate the monthly solar radiation data. Secondly, these data have been divided by corresponding extraterrestrial value in order to obtain the monthly clearness index values. Based on these monthly clearness indexes and using a library of MTM block we can generate the sequences of daily clearness indexes. Known data were subsequently used to investigate the accuracy of the prediction. Furthermore, the unknown validation data set produced very accurate prediction; with an RMSE error not exceeding 8% between the measured and predicted data. A correlation coefficient ranging from 90% and 92% have been obtained; also this model has been compared to the traditional models AR, ARMA, Markov chain, MTM and measured data. Results obtained indicate that the proposed model can successfully be used for the estimation of the daily solar radiation data for any locations in Algeria by using as input the altitude, the longitude, and the latitude. Also, the model can be generalized for any location in the world. An application of sizing PV systems in isolated sites has been applied in order to confirm the validity of this model.     

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.