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.     

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.     

Contour maps for sunshine ratio for Oman using radial basis function generated data

Contour maps for sunshine hours and sunshine ratios for Oman have been generated. The data to generate these maps were obtained using an RBF neural network model. This model estimates sunshine hours and ratios for a given point based on its latitude, longitude, altitude and month of the year. Data from 25 locations were used to plot the contour maps. These maps provide a needed reference for the spatial distribution of sunshine hours and sunshine ratios on a monthly basis for the whole of Oman from which estimates can be made for any location.     

Stochastic modelling of daily global solar radiation measured in Marrakesh, Morocco

Two stochastic models are presented of the daily global solar radiation obtained from three years of data measured on a horizontal surface in Marrakesh, Morocco (latitude 31°37′N, longitude 08°02′W, elevation 463 m). The development of these models is based on the removal of the annual periodicity and seasonal variation of solar radiation using two types of normalisation. The first model is developed using a classical decomposition of the daily radiation as the sum of two components: a trend component and a stochastic component. This model is most useful for long simulated sequences. The second model is developed using a non-dimensional variable, the clearness index, which is modelled as a stochastic process after a preliminary transformation leading to a stationary time series. Both models have satisfactorily passed validation tests for forecasting and simulation of daily global solar radiation data.     

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。     

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.     

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.     

Using probabilistic finite automata to simulate hourly series of global radiation

A model to generate synthetic series of hourly exposure of global radiation is proposed. This model has been constructed using a machine learning approach. It is based on the use of a subclass of probabilistic finite automata which can be used for variable-order Markov processes. This model allows us to represent the different relationships and the representative information observed in the hourly series of global radiation; the variable-order Markov process can be used as a natural way to represent different types of days, and to take into account the “variable memory” of cloudiness. A method to generate new series of hourly global radiation, which incorporates the randomness observed in recorded series, is also proposed. As input data this method only uses the mean monthly value of the daily solar global radiation. We examine if the recorded and simulated series are similar. It can be concluded that both series have the same statistical properties.     

A Method of Estimating Monthly Global Radiation for Singapore

The design of solar systems and the determination of cooling and heating load of buildings require information on global radiation in addition to other meteorological data. In this study, equations have been developed for the clearness index K_T , which is the ratio of monthly average daily values of global radiation to that of extraterrestrial radiation on a horizontal surface, as a function of the ratio of monthly average daily values of sunshine hours and day length. The extraterrestrial radiation and the day length can be calculated from Eqs. (5) and (3) respectively. The sunshine hours can be obtained from the meteorological station of Singapore. A reasonable estimate of monthly average daily global radiation can be obtained from these equations.     

RECONSTRUCTION OF SYNTHETIC METEOROLOGICAL SEQUENCES BY USING MARKOV TRANSITION MATRIX FOR SOLAR THERMAL SIMULATION

A Markov Transition Matrix (MTM) approach used to reconstruct the synthetic sequences of both hourly global radiation and hourly ambient temperature, which has a strong effect on the output of solar thermal systems and has not been taken into account. The result shows that the main statistical features of natural sequences, i.e., probability density function, sequential characteristics and the variance of the fluctuations, can be simulated by Markov transition-matrix obtained from recorded meteorological data. Its quality depends upon data record number in order that the synthetic sequences match long-term statistic characters of natural sequences. Comparisons have been made among different record number and the minimum number of records is sought. It is shown that the minimum data to generate hourly MTM is 12,410 data number for global radiation and 43,800 data for ambient temperature.     

Estimating direct, diffuse, and global solar radiation for various cities in Iran by two methods and their comparison with the measured data

Two computational methods for calculating hourly, daily, and monthly average values of direct, diffuse, and global solar radiation on horizontal collectors have been presented in this article for location with different latitude, altitude, and atmospheric conditions in Iran. These methods were developed using two different independent sets of measured data from the Iranian Meteorological Organization (IMO) for two cities in Iran (Tehran and Isfahan) during 14 years of measurement for Tehran and 4 years of measurement for Isfahan. Comparison of calculated monthly average global solar radiation, using the two models for Tehran and Isfahan with measured data from the IMO, has indicated a good agreement between them. Then these developed methods were extended to another location (city of Bandar-Abbas), where measured data are not available. But the work of Daneshyar [1] predicts its monthly global radiation. The maximum discrepancy of 7% between the developed models and the work of Daneshyar [1] was observed.     

A method for characterization and inter-comparison of sites with regard to solar energy utilization by statistical analysis of their solar radiation data as performed for three sites in the Israel Negev region

A method for the characterization and inter-comparison of sites with regard to their suitability for utilization of solar energy based upon the statistical analysis of their solar radiation intensities is presented. In this method each monthly data set of the daily global, horizontal beam and diffuse radiation intensities was analyzed and the following parameters were determined: monthly average daily radiation intensity, coefficient of variation, skewness and kurtosis. The values of the skewness and kurtosis have been applied, using generally accepted rules, to describe the distribution curves for each of the radiation intensity data sets. In addition, the same type of statistical analysis was applied to the monthly average daily ratios of the horizontal beam to global radiation, diffuse to global and the clearness index for the three sites. In this investigation, this statistical analysis method has been applied to the global and beam radiation measured at three sites located in the southern, Negev region of Israel, viz., Beer Sheva, Sde Boker and Eilat. The southern region of Israel is characterized by relatively high average daily irradiation intensities for both global and normal incidence radiation. They have been characterized with regard to the distribution of their intensity levels and a site inter-comparison has also been performed. An inter-comparison of the results of these analyses for the three sites has been performed on a monthly basis. The results of this analysis are used both to characterize and compare the composition of the solar radiation at the three sites under investigation. The results of this study will be presented in detail.     

Methodology for predicting sequences of mean monthly clearness index and daily solar radiation data in remote areas: Application for sizing a stand-alone PV system

In this paper, a suitable adaptive neuro-fuzzy inference system (ANFIS) model is presented for estimating sequences of mean monthly clearness index () and total solar radiation data in isolated sites based on geographical coordinates. The magnitude of solar radiation is the most important parameter for sizing photovoltaic (PV) systems. The ANFIS model is trained by using a multi-layer perceptron (MLP) based on fuzzy logic (FL) rules. The inputs of the ANFIS are the latitude, longitude, and altitude, while the outputs are the 12-values of mean monthly clearness index . These data have been collected from 60 locations in Algeria. The results show that the performance of the proposed approach in the prediction of mean monthly clearness index is favorably compared to the measured values. The root mean square error (RMSE) between measured and estimated values varies between 0.0215 and 0.0235 and the mean absolute percentage error (MAPE) is less than 2.2%. In addition, a comparison between the results obtained by the ANFIS model and artificial neural network (ANN) models, is presented in order to show the advantage of the proposed method. An example for sizing a stand-alone PV system is also presented. This technique has been applied to Algerian locations, but it can be generalized for any geographical position. It can also be used for estimating other meteorological parameters such as temperature, humidity and wind speed.     

Statistical analysis of solar measurements in Algeria using beta distributions

A method of smoothing solar data by beta probability distributions is implemented in this paper. In the first step, this method has been used to process daily sunshine duration data recorded at thirty-three meteorological stations in Algeria for eleven year periods or more. In the second step, it has been applied to hourly global solar irradiation flux measured in Algiers during the 1987/89 period. For each location and each month of the year, beta probability density functions fitting the monthly frequency distributions of the daily sunshine duration measurements are obtained. Both the parameters characterising the resulting beta distributions are then mapped, enabling us to build the frequency distributions of sunshine duration for every site in Algeria. In the case of solar radiation for Algiers, the recorded data have been processed following two different ways. The first one consists in sorting the hourly global solar irradiation data into eight typical classes of the daily clearness index. The second one is based on the repartition of these data per month. The results of the first classification show that for each class of daily clearness index, the hourly data under consideration are modelled by only one beta distribution. When using the second classification, linear combinations of two beta distributions are found to fit the monthly frequency distributions of the hourly solar radiation data.     

Correlations for estimating solar radiation using sunshine hours and temperature measurement in Osogbo,Osun State,Nigeria

In this study, the global solar radiation on horizontal surface in Osogbo, Osun state, Nigeria was analyzed using 11-year data (1997–2007). Correlations using linear and quadratic expressions were developed to relate the global solar radiation on horizontal surface based on relative sunshine hours and temperature measurements for evaluating the monthly average daily global solar radiation. The calculated monthly clearness index values indicate that the prevailing weather condition in Osogbo is heavily overcast. All the developed quadratic correlations gave better correlation coefficients (0.834, 0.872 and 0.823 respectively) than the linear models. However, the Hargreaves and Samani related based quadratic model gave the best among the three developed quadratic expressions and is therefore suggested for estimating the monthly global radiation in this site and its surroundings.     

Modeling global solar radiation using Particle Swarm Optimization (PSO)

The quantity of solar radiation received by the earth’s surface is very important to numerous renewable energy applications. However, direct measurement of solar data is not widely available, especially in developing countries. This paper uses Particle Swarm Optimization (PSO) to train an artificial neural network (PSO–ANN) using data from available measurement stations to estimate monthly mean daily Global Solar Radiation (GSR) at locations where no measurement stations are available. The inputs to the networks are: month of the year, latitude, longitude, altitude, and sunshine duration, and the output is the monthly mean daily GSR at the specified location. Using training data from 31 stations and testing data from 10 locations, the PSO–ANN outperforms a neural network trained using the standard backpropagation (BP) algorithm (BP–ANN) with an average Mean Absolute Percentage Error (MAPE) of 8.85% for the PSO–ANN and 12.61% for the BP–ANN. The performance is improved significantly, when we use the leave-one-out method, where data from 40 locations is used for training and data from the 41st station is used for assessing the performance. In this case the average of MAPE on data from the 10 testing stations is about 7%. We used the same method to assess the performance of the PSO–ANN on testing data from each of the 41 stations with an overall average MAPE of about 10.3%. Comparison with BP–ANN and an empirical model showed the superiority of the PSO–ANN.     

Predicting total solar irradiation values using artificial neural networks

This study explores the possibility of developing an artificial neural networks model that could be used to predict monthly average daily total solar irradiation on a horizontal surface for locations in Uganda based on geographical and meteorological data: latitude, longitude, altitude, sunshine duration, relative humidity and maximum temperature. Results have shown good agreement between the predicted and measured values of total solar irradiation. A correlation coefficient of 0.997 was obtained with mean bias error of 0.018 MJ/m² and root mean square error of 0.131 MJ/m². Overall, the artificial neural networks model predicted with an accuracy of 0.1% of the mean absolute percentage error.     

Measurement of solar energy radiation in Abu Dhabi,UAE

This paper presents data on measurement of actual solar radiation in Abu Dhabi (24.43°N, 54.45°E). Global solar radiation and surface temperatures were measured and analyzed for one complete year. High resolution, real-time solar radiation and other meteorological data were collected and processed. Daily and monthly average solar radiation values were calculated from the one-minute average recorded values. The highest daily and monthly mean solar radiation values were 369 and 290 W/m², respectively. The highest one-minute average daily solar radiation was 1041 W/m². Yearly average daily energy input was 18.48 MJ/m²/day. Besides the global solar radiation, the daily and monthly average clearness indexes along with temperature variations are discussed. When possible, global solar energy radiation and some meteorological data are compared with corresponding data in other Arab state capitals. The data collected indicate that Abu Dhabi has a strong potential for solar energy capture.     

A simplification of weather data to evaluate daily and monthly energy needs of residential buildings

Hourly, daily, monthly and annual heating and cooling requirements of a residential building located in Ottawa, Ontario, Canada were estimated, employing ENERPASS as the energy simulation tool, and performing hour-by-hour energy analysis. The following weather data were employed:

1. (i) Ten years (1967–1976) of weather data. The ten-year average of the results is identified as TYA.

2. (ii) A typical meteorological year (TMY) generated using the same ten years of data.

3. (iii) Two different hourly ambient air temperature distributions (T1 and T2) for a typical day in each month. The solar radiation on each surface was estimated using the mean monthly clearness index.

The house use patterns, including heat generation and the thermostat setting, were taken the same when using TYA, TMY, T1 or T2. The analysis was carried out for the house as it is (well insulated and airtight), and for two modifications: one with larger infiltration rate and lower wall thermal resistance, and the other with larger south-facing window area and using super-windows. The results of this study show that the long-range hourly, daily, monthly and annual heating and cooling requirements of a residential building located in a cold climate can be predicted by employing mean daily maximum and minimum temperatures and the mean monthly clearness index for each month. This amounts to substantial savings in computational costs, in either using many years of weather data or generating a TMY for the site. For locations lacking detailed hourly weather data, the use of data and the procedure outlined in this study may be employed to predict the long-range thermal performance of simple residential buildings.     

Unveiling the solar resource potential for photovoltaic applications in Mauritius

Mauritius is considered to have high solar resource potential but it has not yet been fully quantified and exploited due to the lack of valid solar energy data. This paper unveils the solar potential of Mauritius. Ground-based measurements were performed at intervals of 30 s in order to obtain accurate global horizontal irradiance data which can depict all changes in solar power. The latter is used to evaluate average monthly global horizontal irradiance, maximum irradiance, monthly average insolation and monthly sky clearness index. A solar geometry model was used to define the average monthly, seasonal and yearly maximum elevations and extraterrestrial radiation. Measurement data were compared to Meteonorm and NASA SSE 3-hourly averaged solar data. Comparison shows that average irradiance values are in good agreement, whereas insolation and sky clearness values obtained from external sources are inferior to high quality measurement data. The results, presented in this paper, complement solar data of Meteonorm and NASA SSE and secondly, provides PV and solar engineers as well as scientists with highly valuable information on the solar resource of Mauritius that can be used during planning and design of PV systems as well as for conducting further research in Mauritius and surrounding regions.