scholarly journals Empirical Models for the Evaluation of Global Solar Radiation in the Different Sites of Nepal

2019 ◽  
Vol 5 (1) ◽  
pp. 67-73
Author(s):  
B. P. Pant ◽  
K. N. Poudyal ◽  
B. Acharya ◽  
B. Budha
Keyword(s):  
Solar Radiation ◽  
Alternative Energy ◽  
Meteorological Parameters ◽  
Global Solar Radiation ◽  
Vital Role ◽  
Empirical Models ◽  
Model Order ◽  
The Earth ◽  
Good Possibility ◽  
Better Than

To operate many phenomenon’s on the earth surface such as physical, chemical and biological process solar radiation plays vital role. A common practice is to estimate average daily global solar radiation (GSR) using appropriate empirical models for the areas lacking the actual measured values. In this context several single and multiple meteorological parameters were selected to estimate the GSR for Jumla, Nepalgunj and Kathmandu. In order to validate the selected models various statistical test were employed. The selected models were compared on the basis of statistical errors. In the statistical analysis the value of root mean square error (RMSE) and coefficient o determination R2 is found to 0.15.0.23, 0.26 and 0.98, 0.96, 0.96 respectively for Jumla, Nepalgunj and Kathmandu in Samuel model. These values were comparatively better than other models. It is concluded that Samuel model (order three) is the best among the used models. The established result uncover that there is a good possibility of solar energy as a alternative energy source in Nepal.

scholarly journals Influence of Sunshine Hour and Relative Humidity on Total Solar Radiation at Nepalganj, Nepal

Patan Pragya ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. 97-104
Author(s):  
Usha Joshi ◽  
P. M. Shrestha ◽  
I. B. Karki ◽  
N. P. Chapagain ◽  
K. N. Poudyal
Keyword(s):  
Relative Humidity ◽  
Solar Radiation ◽  
Meteorological Parameters ◽  
Clean Energy ◽  
Empirical Models ◽  
Energy Resources ◽  
Total Solar Radiation ◽  
Sunshine Hour ◽  
Empirical Constants ◽  
Better Than

The solar energy is the abundantly available free and clean energy resources in Nepal. There are more than 300 sunny days because of Nepal lies in solar zone in a global map. The total solar radiation was measured by using CMP6 pyranometer at Nepalgunj (lat.:28.10oN, long.: 81.67oEand Alt. 165.0masl). The main objective of this study is to select the better empirical model and its empirical constants for the prediction of TSR for the year come. In this research, six different empirical models and meteorological parameters are utilized in the presence of regression technique for the years 2011 and 2012. Finally the different empirical constants are found. After the error analysis, the Swarthman-Oguniade model is found to perform better than others models. So the empirical constants of this model is utilized to predict the TSR of similar geographical sites of Nepal.

scholarly journals Prediction of daily global solar radiation using different empirical models on the basis of meteorological parameters at Trans Himalaya Region, Nepal

BIBECHANA ◽  
2021 ◽  
Vol 18 (1) ◽  
pp. 159-169
Author(s):  
Usha Joshi ◽  
I B Karki ◽  
N P Chapagain ◽  
K N Poudyal
Keyword(s):  
Relative Humidity ◽  
Solar Radiation ◽  
Meteorological Parameters ◽  
Energy Resource ◽  
Global Solar Radiation ◽  
Empirical Models ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Sunshine Hour ◽  
Empirical Constants

Global Solar Radiation (GSR) is the cleanest and freely available energy resource on the earth.  GSR  was measured for six years (2010 -2015) at the horizontal surface using calibrated first-class CMP6 pyranometer at Kathmandu (Lat. 27.70o N, Long. 85.5oE and Alt. 1350m). This paper explains the daily, monthly, and seasonal variations of GSR and also compares with sunshine hour, ambient temperature, relative humidity, and precipitation to GSR. The annual average global solar radiation is about 4.16 kWh/m2/day which is a significant amount to promote solar active and passive energy technologies at the Trans-Himalaya region. In this study, the meteorological parameters are utilized in the regression technique for four different empirical models and finally, the empirical constants are found. Thus obtained coefficients are utilized to predict the GSR using meteorological parameters for the years to come. In addition, the predicted GSR is found to be closer to the measured value of GSR. The values are justified by using statistical tools such as coefficient of determination (R2), root mean square error (RMSE), mean percentage error (MPE), and mean bias error (MBE). Finally, the values of R2, RMSE, MPE, and MBE are found to be 0.792, 1.405, -1.014, and 0.011, respectively for the model (D), which are based on sunshine hour, temperature and relative humidity. In this model, the empirical constants, a = 0.155, b = 0.134, c = 0.014 and d = 0.0007 are determined which can be utilized at the similar geographical locations of Nepal. BIBECHANA 18 (2021) 159-169

scholarly journals Evaluation of Temperature-Based Empirical Models and Machine Learning Techniques to Estimate Daily Global Solar Radiation at Biratnagar Airport, Nepal

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Sandeep Dhakal ◽  
Yogesh Gautam ◽  
Aayush Bhattarai
Keyword(s):  
Machine Learning ◽  
Relative Humidity ◽  
Linear Regression ◽  
Solar Radiation ◽  
Ambient Temperature ◽  
Sunshine Duration ◽  
Global Solar Radiation ◽  
Empirical Models ◽  
Stepwise Linear Regression ◽  
Better Than

Global solar radiation (GSR) is a critical variable for designing photovoltaic cells, solar furnaces, solar collectors, and other passive solar applications. In Nepal, the high initial cost and subsequent maintenance cost required for the instrument to measure GSR have restricted its applicability all over the country. The current study compares six different temperature-based empirical models, artificial neural network (ANN), and other five different machine learning (ML) models for estimating daily GSR utilizing readily available meteorological data at Biratnagar Airport. Amongst the temperature-based models, the model developed by Fan et al. performs better than the rest with an R2 of 0.7498 and RMSE of 2.0162 MJm−2d−1. Feed-forward multilayer perceptron (MLP) is utilized to model daily GSR utilizing extraterrestrial solar radiation, sunshine duration, maximum and minimum ambient temperature, precipitation, and relative humidity as inputs. ANN3 performs better than other ANN models with an R2 of 0.8446 and RMSE of 1.4595 MJm−2d−1. Likewise, stepwise linear regression performs better than other ML models with an R2 of 0.8870 and RMSE of 1.5143 MJm−2d−1. Thus, the model developed by Fan et al. is recommended to estimate daily GSR in the region where only ambient temperature data are available. Similarly, a more robust ANN3 and stepwise linear regression models are recommended to estimate daily GSR in the region where data about sunshine duration, maximum and minimum ambient temperature, precipitation, and relative humidity are available.

Review of empirical solar radiation models for estimating global solar radiation of various climate zones of China

2019 ◽  
Vol 44 (2) ◽  
pp. 168-188
Author(s):  
Shaban G Gouda ◽  
Zakia Hussein ◽  
Shuai Luo ◽  
Qiaoxia Yuan
Keyword(s):  
Solar Radiation ◽  
Solar Energy ◽  
Computational Cost ◽  
Energy Systems ◽  
Global Solar Radiation ◽  
Empirical Models ◽  
Dew Point ◽  
Machine Learning Techniques ◽  
Accurate Information ◽  
Input Parameters

Utilizing solar energy requires accurate information about global solar radiation (GSR), which is critical for designers and manufacturers of solar energy systems and equipment. This study aims to examine the literature gaps by evaluating recent predictive models and categorizing them into various groups depending on the input parameters, and comprehensively collect the methods for classifying China into solar zones. The selected groups of models include those that use sunshine duration, temperature, dew-point temperature, precipitation, fog, cloud cover, day of the year, and different meteorological parameters (complex models). 220 empirical models are analyzed for estimating the GSR on a horizontal surface in China. Additionally, the most accurate models from the literature are summarized for 115 locations in China and are distributed into the above categories with the corresponding solar zone; the ideal models from each category and each solar zone are identified. Comments on two important temperature-based models that are presented in this work can help the researchers and readers to be unconfused when reading the literature of these models and cite them in a correct method in future studies. Machine learning techniques exhibit performance GSR estimation better than empirical models; however, the computational cost and complexity should be considered at choosing and applying these techniques. The models and model categories in this study, according to the key input parameters at the corresponding location and solar zone, are helpful to researchers as well as to designers and engineers of solar energy systems and equipment.

scholarly journals Parametrization of Models and Use of Estimated Global Solar Radiation Data in the Irrigated Rice Yield Simulation

2018 ◽  
Vol 33 (2) ◽  
pp. 238-246
Author(s):  
João Rodrigo de Castro ◽  
Santiago Vianna Cuadra ◽  
Luciana Barros Pinto ◽  
João Marcelo Hoffmann de Souza ◽  
Marcos Paulo dos Santos ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Air Temperature ◽  
Rice Yield ◽  
Simulation Models ◽  
Global Solar Radiation ◽  
Empirical Models ◽  
Potential Yield ◽  
Irrigated Rice ◽  
Radiation Data ◽  
The Impact

Abstract The objective of this study was to evaluate the use of estimated global solar radiation data in the simulations of potential yield of irrigated rice. Global solar radiation was estimated by four empirical models, based on air temperature, and a meteorological satellite derivated. The empirical models were calibrated and validated for 10 sites, representative of the six rice regions of the State of Rio Grande do Sul - Brazil. To evaluate the impact of the radiation estimates on irrigated rice yield simulations, the CERES-Rice model, calibrated for four cultivars, was used. The estimates of global solar radiation of the empirical models based on the air temperature showed deviations, from the observed values, of 20 to 30% and the estimated by satellite deviations of more than 30%. The global solar radiation data estimated by the Hargreaves and Samani, Donatelli and Campbell and derived satellite (PowerNasa) type air temperature-based empirical models can be used as input data in simulation models of crop growth, development and productivity of irrigated rice.

scholarly journals Using the artificial neural networks for prediction and validating solar radiation

2019 ◽  
Vol 27 (1) ◽  
Author(s):  
Zahraa E. Mohamed
Keyword(s):  
Solar Radiation ◽  
Global Solar Radiation ◽  
Horizontal Surface ◽  
The Other ◽  
Rate Coefficients ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Statistical Indicators ◽  
Better Than

AbstractThe main objective of this paper is to employ the artificial neural network (ANN) models for validating and predicting global solar radiation (GSR) on a horizontal surface of three Egyptian cities. The feedforward backpropagation ANNs are utilized based on two algorithms which are the basic backpropagation (Bp) and the Bp with momentum and learning rate coefficients respectively. The statistical indicators are used to investigate the performance of ANN models. According to these indicators, the results of the second algorithm are better than the other. Also, model (6) in this method has the lowest RMSE values for all cities in this study. The study indicated that the second method is the most suitable for predicting GSR on a horizontal surface of all cities in this work. Moreover, ANN-based model is an efficient method which has higher precision.

Study of the Intensity of Global Solar Radiation Incident in Suan Sunandha Rajabhat University

2013 ◽  
Vol 770 ◽  
pp. 229-232
Author(s):  
A. Sansomboon ◽  
N. Luewarasirikul ◽  
A. Ittipongse ◽  
W. Phae-Ngam ◽  
S. Pattarapanitchai
Keyword(s):  
Solar Radiation ◽  
Solar Energy ◽  
Alternative Energy ◽  
Global Radiation ◽  
Global Solar Radiation ◽  
Digital Data ◽  
Data Logger ◽  
Language Version ◽  
Energy Applications ◽  
Yearly Average

Solar radiation is one of mains alternative energy, widely used in present day. Measure solar radiation accurately is an essential for planning in application of used. Universities are the places that have used significant of energy all year long. Therefore, long-term measured solar radiation data is important, for understand in both quantity and variation in time period, for application of the alternative energy in future. The main objective of this research is to investigate solar energy potentials of Suan Sunandha Rajabhat University, Bongkok, Thailand (Latitude 13.46°N, Longitude 100.31°E). A station for solar radiation was installed at Suan Sunandha Rajabhat University. The main equipment is composed of two parts: 1) a pyranometer from Kipp & Zonen Ltd., model CMP11, and 2) a digital data logger from Measurement Systems Ltd. model DX2000. The pyranometer is permanently installed on the top of a building. The data logger is keeping clean and safe inside the building. To analyze the values of the global solar radiations, the computer source code is written in Interactive Data Language version 6.1 (IDL6.1). The results show the variation of the average hourly global irradiance is about 800-900 W/m2 at 12:00 UTC. The maximum monthly average daily global radiation is 21.5 MJ/m2-day in April. The yearly average daily radiation at Suan Sunandha Rajabhat University is found to be 16.55 MJ/m2-day. The information from the monthly and yearly global radiation has relatively high solar energy potentials. Finally, the solar radiation database was also developed for use in solar energy applications in Suan Sunandha Rajabhat University and neighbor areas.

Sign in / Sign up

Export Citation Format

Share Document