Estimation of daily total and diffuse insolation in India from weather data

Insolation and weather data for a large number of cities in India is analysed and correlated. Correlations based on a citywise regression analysis indicate that daily total insolation correlates best with sunshine duration, all clouds and precipitation. However these relations are not useful for predicting insolation at locations where this data is not measured. Monthwise correlations which are valid over a region are more useful. Hence such correlations have been developed for Indian conditions. In order to increase the accuracy of prediction of these correlations, India is divided into two regions on the basis of the climatic characteristics of the winter monsoon.Finally the Liu and Jordan model for predicting daily diffuse radiation from daily total radiation has been tested and found to be applicable for Indian conditions. However the numerical values obtained are very different from those obtained for conditions in the United States.     

Classification of daily solar radiation distributions using a mixture of Dirichlet distributions

In order to characterize the fluctuating nature of solar radiation in tropical climate, we classify daily distributions of the clearness index k_t by estimating a finite mixture of Dirichlet distributions without assuming any parametric hypothesis on these daily distributions. The method is applied to solar radiation measurements performed in Guadeloupe (16°2N, 61W) where important fluctuations can be observed even within a period of a few minutes. The results exhibit four distinct classes of distributions corresponding to different types of days. The sequence of such classes can be of interest for future weather prediction.     

A comparative study to estimate daily diffuse solar radiation over India

A detailed study of monthly average daily diffuse solar radiation for selected Indian locations have been performed using five years (2001–2005) measured data. The data of four prominent locations (Jodhpur, Calcutta, Bombay and Pune), representing varying weather conditions of the entire country, have been taken for the present study. The correlations between the diffuse fraction (H_d/H) and the sunshine fraction (S/S₀) have been developed using regression analysis method for each selected location as well as for all Indian locations, we call it All India Correlation (AIC). The results obtained from present AIC are well compared with the measured data along with the estimates of Liu and Jordan, Gopinathan and Iqbal for different locations. The comparisons between various data conclude that AIC can be used to estimate diffuse fraction for any location in India. For further validation and to show the accuracy of present correlations, statistical tests of root mean square error (RMSE), mean bias error (MBE) and mean percentage error (MPE) are also performed.     

An adaptive wavelet-network model for forecasting daily total solar-radiation

The combination of wavelet theory and neural networks has lead to the development of wavelet networks. Wavelet-networks are feed-forward networks using wavelets as activation functions. Wavelet-networks have been used successfully in various engineering applications such as classification, identification and control problems. In this paper, the use of adaptive wavelet-network architecture in finding a suitable forecasting model for predicting the daily total solar-radiation is investigated. Total solar-radiation is considered as the most important parameter in the performance prediction of renewable energy systems, particularly in sizing photovoltaic (PV) power systems. For this purpose, daily total solar-radiation data have been recorded during the period extending from 1981 to 2001, by a meteorological station in Algeria. The wavelet-network model has been trained by using either the 19 years of data or one year of the data. In both cases the total solar radiation data corresponding to year 2001 was used for testing the model. The network was trained to accept and handle a number of unusual cases. Results indicate that the model predicts daily total solar-radiation values with a good accuracy of approximately 97% and the mean absolute percentage error is not more than 6%. In addition, the performance of the model was compared with different neural network structures and classical models. Training algorithms for wavelet-networks require smaller numbers of iterations when compared with other neural networks. The model can be used to fill missing data in weather databases. Additionally, the proposed model can be generalized and used in different locations and for other weather data, such as sunshine duration and ambient temperature. Finally, an application using the model for sizing a PV-power system is presented in order to confirm the validity of this model.     

Artificial neural network model for the estimation of direct solar radiation in the Indian zone

This paper presents a self-consistent model for the estimation of direct solar radiation in the Indian zone. It takes into account the atmospheric transmittance modified in accordance with the climate zone and calculates solar radiation at normal incidence using Hottel's clear day model. The regional weather phenomena are taken into account with the help of variables such as relative humidity, mean duration of sunshine per hour and the rainfall, and a composite parameter referred to as sky clearness index (CI) is determined using artificial neural network analysis. The CI is finally applied to the modified Hottel's clear day model to predict the grey day solar irradiance. The model predictions for the Indian region are found to be in good agreement with the measurements. The variability of sky CI is represented by the contours of constant value in Indian region, which in turn would enable the present model to be used in a self-consistent manner.     

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.     

A bi-variable probability density function for the daily clearness index

The assumption that probability density functions of daily clearness indices are unimodal was tested. Bimodal behaviour was observed in almost 60% of 600 monthly data sets and was shown to be the most usual shape at 50 locations between latitudes 18.43°N and 64.81°N and at between 2 and 2297 m above sea level. A bi-exponential probability density function was proposed that fits the observed behaviour of daily clearness indices. The proposed function uses the mean monthly clearness index and the mean monthly solar height at noon as inputs. The bi-exponential method for predicting daily distributions is shown to reduce the RMS error by over 20% compared with earlier methods. Parametric representations of monthly maximum and minimum clearness index using and are combined with the bi-exponential function to obtain a bi-variable probability density function and a cumulative distribution function , analogous to those of Liu and Jordan.     

Simple model for the generation of daily global solar-radiation data in Turkey

Modelling, performance analysis, and designing of solar energy systems depend on solar radiation data. In this study, a simple model for estimating the daily global radiation is developed. The model is based on a trigonometric function, which has only one independent parameter, namely the day of the year. The model is tested for 68 locations in Turkey using the data measured during at least 10 years. It is seen that predictions from the model agree well with the long-term measured data. The predictions are also compared with the data available in literature for Turkey. It is expected that the model developed for daily global solar radiation will be useful to the designers of energy-related systems as well as to those who need to estimates of yearly variation of global solar-radiation for any specific location in Turkey.     

A multivariate qualitative model for the prediction of daily global radiation from three hourly global radiation values

A new model for the prediction of daily global radiation using three hourly radiation values is proposed. This model is obtained by multivariate regression analysis. The hourly clearness index and various qualitative variables are used as independent variables. The hourly values are obtained from net ground measures of hourly global radiation corresponding to the hours in which Meteosat secondary images are available over Europe. The qualitative variables allow us to include additional non-numerical information, specifically, the season of the year. The proposed model is the same for all the locations analysed. This model can be used for the prediction of daily global radiation based on hourly global radiation data obtained from satellite images.     

Time series analysis of daily horizontal solar radiation

An analysis of the stationary and sequential properties of daily global horizontal solar radiation, on a discrete monthly basis, is presented for a number of locations of widely varying climatic conditions. Such information is essential as input to analytic models for the long-term performance of solar energy systems and for the generation of synthetic daily radiation sequences that can serve as input to numerical simulations that model solar systems. The new aspects of our study include (1) analysis of a solar radiation database that is much larger than those considered heretofore and includes tropical low-latitude, as well as temperate middle-latitude, climates, (2) documentation of the magnitudes and correlations of generalized stationary and sequential radiation statistics for a wide range of climatic stations, (3) proposal of a simple functional form for the probability density function for daily radiation and comparison with actual data, (4) explicit consideration of confidence limits in predicting stationary radiation statistics from a limited number of years of data, (5) evidence that, contrary to the claims of most related studies, there do not seem to be universal values or universal correlations for either the persistence strengths or the persistence times of daily radiation, and, (6) discussion of the practical value of statistical studies of this nature for the design of solar energy systems.     

Technical note a multiple linear correlation for diffuse radiation from global solar radiation and sunshine data over Egypt

Measurements of global solar radiation, diffuse radiation and sunshine duration data during the period from 1982 to 1988 at different locations over Egypt were used to establish empirical relationships that would connect the daily monthly average diffuse irradiation with both relative sunshine duration and clearness index separately and in combination. The selected locations were chosen to represent the different weather conditions of North, Middle and South Egypt. Our correlation equations were tested using measured data for the year 1992 at the same locations. The correlation connecting diffuse radiation with both clearness index and percentage possible sunshine is found to be applicable over Egypt.     

Solar radiation models—A review

Solar radiation models for predicting the average daily and hourly global radiation, beam radiation and diffuse radiation on horizontal surface are reviewed in this article. Estimations of monthly average hourly global radiation from daily summations are discussed. It was observed that Collares‐Pereira and Rabl model as modified by Gueymard (CPRG) yielded the best performance for estimating mean hourly global radiation incident on a horizontal surface for Indian regions. Estimations of monthly average hourly beam and diffuse radiation are discussed. It was observed that Singh‐Tiwari and Jamil‐Tiwari both models generally give better results for climatic conditions of Indian regions. Therefore, their use is recommended for composite climate of Indian regions. Empirical correlations developed to establish a relationship between the hourly diffuse fraction and the hourly clearness index using hourly global and diffuse irradiation measurements on a horizontal surface are discussed. Fifty models using the Angstrom–Prescott equation to predict the average daily global radiation with hours of sunshine are considered. It was reported that Ertekin and Yaldiz model showed the best performance against measured data of Konya, Turkey. Copyright © 2010 John Wiley & Sons, Ltd.     

Calculation of the repetition rate and the provision of daily amounts of solar radiation: Cadastre parameters

A calculation is presented of the repetition rate and the provision of daily amounts of solar radiation over the Apsheron Peninsula weather stations.     

Fuzzy inference systems applied to the daily ultraviolet radiation evaluation (295–385 nm) from daily global radiation

This work presents the results of applying different automatic learning techniques to the calculation of daily ultraviolet radiation from daily global radiation on a horizontal surface. Using the data from three Spanish locations, a zonal study was made, which was finally combined in models for general application. Using the corresponding atmospheric transparency index, three models based on multivariate linear regression, non-linear regression and generation of fuzzy inference systems, respectively, were designed. The results obtained verify the good behavior of the fuzzy inference system calculated.     

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.     

Cumulative distribution curves of daily clearness index in a southern tropical climate

Most previous works, involving the analysis of the statistical properties of solar radiation, have considered places situated in the Northern Hemisphere, generally at latitudes above 30°. In this work, the cumulative distribution curves for 23 sites located in the Southern Hemisphere, in the tropical and inter-tropical regions of Brazil, between the Equator and 30° latitude were calculated, trying to enlarge the available information on the statistical properties of solar radiation towards locations in sub-equatorial regions of the world. Results are compared with four distinct models of daily cumulated distribution functions , proposed by Bendt et al., Hollands and Huget, Saunier et al., and Babu and Satyamurty. A first comparison shows that the function proposed by Saunier et al. is well adjusted to our experimental data for the greater part of the Brazilian locations, except for places with temperate climates, in which the Hollands and Huget model, with the maximum clearness index equal to 0.864, as proposed by these authors, is superior. By substituting the maximum clearness index 0.864 of the Hollands and Huget model for a local maximum, as obtained experimentally in this work, results improve considerably. This study ratifies the conclusions previously obtained by other authors such as Saunier et al., and Babu and Satyamurty on the non-universal character of the Liu and Jordan cumulative distribution functions (CDFs).     

Comparisons of the hourly and daily global irradiances of Turkey on non-horizontal surfaces

Global irradiances incident on a horizontal surface are estimated from the bright sunshine hour measurements over Turkey. Global radiation and its components on daily and hourly bases are computed for the surfaces with any inclination and orientation. Some demonstration results are presented for the six climatologically different regions. Numerous hourly and daily computer output tables of global direct and diffuse radiations are obtained for 50 locations, which have been used to prepare a solar radiation handbook of Turkey.     

Estimation of symmetric and asymmetric hourly global and diffuse radiation from daily values

The validity of the correlations[1–3] to estimate the hourly global and diffuse solar radiation components for an independent dataset of fourteen locations is examined in this article. The correlations for the diffuse component[2,3] are found to be in rather poor agreement with the data. An improved correlation for the diffuse component that includes a daily diffuse fraction as a parameter is developed. The influence of this improved correlation on estimating the beam radiation component is examined. A convenient form of describing asymmetry for these three components of solar radiation distribution is proposed and validated.     

A comparison of daily solar radiation estimates for the Northeastern United States using the Northeast Regional Climate Center and National Renewable Energy Laboratory models

Observed solar radiation data at three sites in the northeastern United States are compared with values estimated for nearby airport locations using the National Renewable Energy Laboratory (NREL) and Northeast Regional Climate Center (NRCC) models. A tendency toward considerable overestimation of relatively low values of observed solar radiation is evident in the NREL model. This bias is apparently regardless of season. A similar bias is not detected in the NRCC model. For moderate to high values of solar radiation both models produce estimates with similar accuracy for most practical applications. However, these models both tend to underestimate observed solar radiation on days when near maximum possible radiation levels are received. The tendency for the NREL model to overestimate low solar radiation values appears to be linked to the use of total sky cover, rather than the combination of cloud coverage and cloud base height information. Although total sky coverage data may be superior for estimates of moderate to high daily solar radiation values, it appears that information regarding the height of low overcast layers and the presence of obstructions to visibility, such as fog or haze, is required to accurately estimate low daily solar radiation totals.     

Use of radial basis functions for estimating monthly mean daily solar radiation

The present study utilizes the radial basis functions technique for the estimation of monthly mean daily values of solar radiation falling on horizontal surfaces and compares its performance with that of the multilayer perceptrons network and a classical regression model. In this work, we use solar radiation data from 41 stations that are spread over the Kingdom of Saudi Arabia. The solar radiation data from 31 locations are used for training the neural networks and the data from the remaining 10 locations are used for testing the estimated values. However, the testing data were not used in the modeling or training of the networks to give an indication of the performance of the system at unknown locations. Results indicate the viability of the radial basis for this kind of problem.