Deterministic Modelling of Solar Radiation for Natural Resource Management in Udaipur Region (original) (raw)
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IRJET- Deterministic Modelling of Solar Radiation for Natural Resource Management in Udaipur Region
IRJET, 2020
Increasing use of various kinds of energy sources in rural as well as in urban areas highlights the necessity of using the more solar energy for sustainable use of precious natural resources and their management. The major electrical energy utilization in the region of Udaipur are for domestic lighting but a significant amount of energy is also utilized for pumping of water for irrigation and other on-farm activities. Knowledge of solar radiation over time plays very important role in planning, design, installation, operation and maintenance of solar energy harvesting units/ systems. In Udaipur region, where the solar energy is in abundance throughout the year, installation of such systems may prove to be very cost effective and energy efficient, if designed, with the help of precise and accurately fitted models predicting the values of solar radiations over time. In the present study the past data of thirty-two years (1983-2014) were used. The data of maximum, minimum and average values of total global and diffused solar radiation over time were assumed as the time series consisting of finite numbers of equally spaced data points. The periodic variations in the radiations were assumed to be the superposition of several sinusoidal harmonics. In this context, the Fourier Series Transformation has been fitted to the data of solar radiations available in Udaipur region. The models, thus developed, have been tested for the variance test analysis. The predicted values were compared with the historical values obtained from subtraction of respective predicted values of historical data series and were found random around zero mean. The models developed may well be adopted for prediction of solar radiations in Udaipur region. Similar models can also be made for different regions for utilization in generation of data and the spatial comparisons. The developed Fourier transformation models can also be employed for the prediction of such values which could be efficiently used for the optimisation of design parameters and thus, the cost effectiveness while designing solar radiation harnessing units such as solar photovoltaic array for successful electric generation in the region, solar cookers, dryers, solar ponds etc.
International Conference On. Solar Energy Materials, Solar Cells & Solar Energy Applications, 2018
A number of parameters such as solar irradiance, temperature, wind speed, wind direction and soiling are influencing the solar energy harvesting. It is essential to develop deeper understanding of the factors influencing the solar energy production in a particular region and have reliable models to forecast energy production. For this research study, Killinochi district was chosen as it has a lot of potential for solar PV, and solar thermal compared to other districts. In this paper, initially it was analysed how the weather data and solar irradiance vary on a daily and yearly basis. Subsequently, the correlation between individual weather parameters, solar irradiance and silicon voltage are studied. Pearson correlation estimation was used for correlation studies. Based on correlation studies, it was found that the solar parameters are influencing the solar power generation. After that, temperature variations were modelled using ARIMA modelling and the model were used to forecast the next hour data. Similarly, the diffused horizontal irradiance (DHI) and global horizontal irradiance (GHI) data can be forecasted using ARIMA modelling, and the next hour data can be predicted. Future study will include modelling of correlation between solar irradiance and temperature or humidity using support vector regression methods; and DHI and GHI will be forecasted based on weather data. These prediction models are useful for power generation entities and households. The effects of soiling on PV modules which vary with soil type, location and weather patterns will also be considered.
Comparative Study of Solar Radiation Models for the
Comparative Study of Solar Radiation Models for the Estimation of Solar Radiation Using Short-Term Meteorological Data in Lawra, Ghana, 2022
Monthly average daily global solar radiation data are essential for the design and study of solar energy systems. The performance and accuracy of eleven models for the estimation of monthly average global solar radiation were compared in this study. Nineteen months (Nov 2020 – May 2022) ground measurement data consisting of monthly mean daily sunshine duration, relative humidity, minimum and maximum temperatures, and global solar radiation collected from the Lawra Solar Plant were used. The models were compared using statistical indices. According to the indices, most of the models were in reasonably good agreement with the measured data. Two model equations, however, were found to have the highest accuracy and can thus be used to estimate monthly average global solar radiation in Lawra and other places with similar climatic conditions where radiation data is unavailable.
Heliyon, 2024
alarming; about 35 % underperformance in solar power generation is observed. The goal of the current study is to minimize this disparity by improving the design models. Considering only cell temperature in the power generation model is responsible for the observed difference in design and operational solar power generated, the present study used a thermocouple to directly measure cell temperature, an anemometer to measure wind speed, and a solar power meter to measure irradiance. These extrinsic factors were used to modify the power generation model based only on cell temperature through the direct correlation of cell temperature, wind speed, and irradiance with solar power generation. Thus, the absence of extrinsic factors (wind speed and irradiance) in the design models is responsible for the colossal drop in solar power generated. Empirically, the missing extrinsic factors were used to transform the implicit solar power model into an explicit model. The development of a solar power generation model, multiple differential models, simulation and experimentation with a pilot solar rig served as alternate model for the prediction of solar power generation. The second-order differential model validated well with empirical solar power generated in Busitema, Mayuge, Soroti, and Tororo study areas based on RMSEs (0.6437, 0.6692, 0.2008, 0.1804, respectively), thus, narrowing the gap between the designed and operational solar power generated. Mayuge and Soroti recorded the highest solar power generation of 9.028 MW compared to Busitema (8.622 MW) and Tororo (8.345 MW), suggesting that it has a conducive site for installing future solar plants. The above results support the use of empirical explicit (triple) and second-order differential models for the design and operation of power plants
Forecasting the Diffuse Solar Radiation by using a New Modeling over Saudi Arabia
American Journal of Applied Sciences, 2021
Within the arranging and execution assessment of solar energy projects, particularly within the planning and estimation of solar photovoltaic sources as elective energy sources within the future, exact evaluation of Diffuse Solar Radiation (DSR) is one of the essential and basic issues. The target of this article is to use the new model to predict DSR in four different locations in Saudi Arabia. The author explored the available meteorological and radiation data. The data covers the 25 years from 1990 to 2014 and were measured at Al-Baha, Abha, Jeddah and Taif locations in Saudi Arabia. Through detailed statistical evaluation and analysis, 19 empirical models were tested to construct the best empirical model to estimate the monthly average daily DSR of Saudi Arabia. Using widely used statistical errors, namely MBE, MPE, RMSE, U95, R, t-test and GPI the proposed correlation model was compared with 19 models provided in the literature. Through this analysis, the cubic empirical equation model is selected as the good model. Compared with these existing models, this model reveals accurate results with minimal statistical errors. Based on these results, Model 22 gives the highest GPI value. The conclusion is the cubic equation model of the diffusion fraction (i.e., D/G = 0.215+2.123 (S/So)-3.547(S/So) 2 +5.142 (S/So) 3 is the best agreement model that has been mentioned in the previous discussion. This model is a generalized equation in Saudi Arabia, which can forecast the monthly average daily diffuse radiation on the horizontal plane at any site under similar climatic conditions without the measured climatic conditions. Experts or architects can use this model to perform site selection and technical and financial evaluation of solar energy applications and photovoltaic technology.
2012
This study was undertaken to analyze solar radiation abundance to ascertain the potential of solar energy as an electrical energy resource. Local weather forecasting for predicting solar radiation is performed using a meteorological model MM5. The prediction results are compared with observed results obtained from the Japan Meteorological Agency for verification of the data accuracy. Results show that local weather forecasting has high accuracy. Prediction of solar radiation is similar with observation results. Monthly average values of solar radiation are sufficiently good during March– September. Electrical energy generated by photovoltaic cells is almost proportional to the solar radiation amount. Effects of clouds on solar radiation can be removed by monthly averaging. The balance between supply and demand of electricity can be estimated using a standard curve obtained from the temporal average. When the amount of solar radiation every hour with average of more than 100 km radiu...
An analytical study on daily solar radiation data
2013
Solar energy being an unlimited source of energy is rapidly increasing in importance amongst the various energy sources. For the effective and efficient utilization of any solar energy system, the site-specific data on solar radiation are the prime need. In the present communication, we have conducted an analytical study on available solar radiation data at Solar Energy Centre, Gwal Pahari, India. The study is carried out for daily global radiation on horizontal and tilted surface and for direct normal irradiance (DNI). An attempt is also made to analyse the clearness index as it is an important factor for the installation of any solar energy system. Additionally, three statistical parameters – standard deviation, kurtosis and skewness are worked out for these solar radiation parameters. Furthermore, the sunshine hours are computed exploiting the data of DNI. A comparison is also made between the ground measurements and satellitederived data – global and DNI.
The paper examines the effects of radiation and temperature on photovoltaic systems in environmental conditions of Shiraz City. In this regard, first the Circuit model of photo voltaic array is evaluated. After reviewing the case and obtaining climate data using the RET Screen software, and simulating the results in MATLAB software, we concluded that with increase of radiation, the maximum power increases in the area. Then, temperature reducing effect on power, as well as radiation increasing effect on power was surveyed. The findings of the study can be useful to design a solar power plant in the area.
Estimation of Solar Energy Using Different Empirical Models at Mid Hill, Nepal
Journal of Nepal Physical Society
Accurate knowledge of global solar radiation distribution is essential for designing, sizing, and performing an evaluation of solar energy system in any part of the world. However, it is not available in many sites of Nepal due to the high expense of the technical process. This study is focused on the performance of different models based on daily global solar radiation, sunshine hour, temperature, and relative humidity at mid-hill region Lumle, (lat. 28.29650N, long. 83.8179oE, and Alt. 1740.0 m.a.s.l.). This study is carried for the year 2018 to 2020. The performance of different models based on sunshine hour, temperature, and relative humidity were analyzed using the regression technique and statistical tools such as Root Mean Square Error (RMSE), Mean Bias Error (MBE), Mean Percentage Error (MPE), and Coefficient of determination (R2). After the analysis, the modified Angstrom model (M-9) based on temperature difference and relative humidity was found to be the best in terms of ...