scholarly journals Regression Model to Predict Global Solar Irradiance in Malaysia

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Hairuniza Ahmed Kutty ◽  
Muhammad Hazim Masral ◽  
Parvathy Rajendran
Keyword(s):  
Regression Model ◽  
Solar Irradiance ◽  
Cloud Cover ◽  
Prediction Models ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Proposed Model ◽  
Multiple Parameter ◽  
Gust Speed

A novel regression model is developed to estimate the monthly global solar irradiance in Malaysia. The model is developed based on different available meteorological parameters, including temperature, cloud cover, rain precipitate, relative humidity, wind speed, pressure, and gust speed, by implementing regression analysis. This paper reports on the details of the analysis of the effect of each prediction parameter to identify the parameters that are relevant to estimating global solar irradiance. In addition, the proposed model is compared in terms of the root mean square error (RMSE), mean bias error (MBE), and the coefficient of determination (R2) with other models available from literature studies. Seven models based on single parameters (PM1 to PM7) and five multiple-parameter models (PM7 to PM12) are proposed. The new models perform well, with RMSE ranging from 0.429% to 1.774%,R2ranging from 0.942 to 0.992, and MBE ranging from −0.1571% to 0.6025%. In general, cloud cover significantly affects the estimation of global solar irradiance. However, cloud cover in Malaysia lacks sufficient influence when included into multiple-parameter models although it performs fairly well in single-parameter prediction models.

scholarly journals 3D-VAR Data Assimilation of SEVIRI Radiances for the Prediction of Solar Irradiance in Italy Using WRF Solar Mesoscale Model—Preliminary Results

2020 ◽  
Vol 12 (6) ◽  
pp. 920 ◽  
Author(s):  
Sabrina Gentile ◽  
Francesco Di Paola ◽  
Domenico Cimini ◽  
Donatello Gallucci ◽  
Edoardo Geraldi ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Solar Irradiance ◽  
Cloud Cover ◽  
Weather Prediction ◽  
National Research Council ◽  
Mean Bias Error ◽  
Bias Error ◽  
Clear Sky ◽  
Sky Conditions ◽  
The Impact

Solar power generation is highly fluctuating due to its dependence on atmospheric conditions. The integration of this variable resource into the energy supply system requires reliable predictions of the expected power production as a basis for management and operation strategies. This is one of the goals of the Solar Cloud project, funded by the Italian Ministry of Economic Development (MISE)—to provide detailed forecasts of solar irradiance variables to operators and organizations operating in the solar energy industry. The Institute of Methodologies for Environmental Analysis of the National Research Council (IMAA-CNR), participating to the project, implemented an operational chain that provides forecasts of all the solar irradiance variables at high temporal and horizontal resolution using the numerical weather prediction Advanced Research Weather Research and Forecasting (WRF-ARW) Solar version 3.8.1 released by the National Center for Atmospheric Research (NCAR) in August 2016. With the aim of improving the forecast of solar irradiance, the three-dimensional (3D-Var) data assimilation was tested to assimilate radiances from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard the Meteosat Second Generation (MSG) geostationary satellite into WRF Solar. To quantify the impact, the model output is compared against observational data. Hourly Global Horizontal Irradiance (GHI) is compared with ground-based observations from Regional Agency for the Protection of the Environment (ARPA) and with MSG Shortwave Solar Irradiance estimations, while WRF Solar cloud coverage is compared with Cloud Mask by MSG. A preliminary test has been performed in clear sky conditions to assess the capability of the model to reproduce the diurnal cycle of the solar irradiance. The statistical scores for clear sky conditions show a positive performance of the model with values comparable to the instrument uncertainty and a correlation of 0.995. For cloudy sky, the solar irradiance and the cloud cover are better simulated when the SEVIRI radiances are assimilated, especially in the short range of the simulation. For the cloud cover, the Mean Bias Error one hour after the assimilation time is reduced from 41.62 to 20.29 W/m2 when the assimilation is activated. Although only two case studies are considered here, the results indicate that the assimilation of SEVIRI radiance improves the performance of WRF Solar especially in the first 3 hour forecast.

scholarly journals Hourly Ground-Level PM2.5 Estimation Using Geostationary Satellite and Reanalysis Data via Deep Learning

2021 ◽  
Vol 13 (11) ◽  
pp. 2121
Author(s):  
Changsuk Lee ◽  
Kyunghwa Lee ◽  
Sangmin Kim ◽  
Jinhyeok Yu ◽  
Seungtaek Jeong ◽  
...  
Keyword(s):  
Cross Validation ◽  
Ground Level ◽  
Reanalysis Data ◽  
Training Dataset ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Air Quality Model ◽  
Quality Model ◽  
Near Surface

This study proposes an improved approach for monitoring the spatial concentrations of hourly particulate matter less than 2.5 μm in diameter (PM2.5) via a deep neural network (DNN) using geostationary ocean color imager (GOCI) images and unified model (UM) reanalysis data over the Korean Peninsula. The DNN performance was optimized to determine the appropriate training model structures, incorporating hyperparameter tuning, regularization, early stopping, and input and output variable normalization to prevent training dataset overfitting. Near-surface atmospheric information from the UM was also used as an input variable to spatially generalize the DNN model. The retrieved PM2.5 from the DNN was compared with estimates from random forest, multiple linear regression, and the Community Multiscale Air Quality model. The DNN demonstrated the highest accuracy compared to that of the conventional methods for the hold-out validation (root mean square error (RMSE) = 7.042 μg/m3, mean bias error (MBE) = −0.340 μg/m3, and coefficient of determination (R2) = 0.698) and the cross-validation (RMSE = 9.166 μg/m3, MBE = 0.293 μg/m3, and R2 = 0.49). Although the R2 was low due to underestimated high PM2.5 concentration patterns, the RMSE and MBE demonstrated reliable accuracy values (<10 μg/m3 and 1 μg/m3, respectively) for the hold-out validation and cross-validation.

scholarly journals Evaluation of NASA POWER Reanalysis Products to Estimate Daily Weather Variables in a Hot Summer Mediterranean Climate

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1207
Author(s):  
Gonçalo C. Rodrigues ◽  
Ricardo P. Braga
Keyword(s):  
Wind Speed ◽  
Bias Correction ◽  
Goodness Of Fit ◽  
Mediterranean Climate ◽  
Weather Data ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Weather Variables ◽  
Weather Stations

This study aims to evaluate NASA POWER reanalysis products for daily surface maximum (Tmax) and minimum (Tmin) temperatures, solar radiation (Rs), relative humidity (RH) and wind speed (Ws) when compared with observed data from 14 distributed weather stations across Alentejo Region, Southern Portugal, with a hot summer Mediterranean climate. Results showed that there is good agreement between NASA POWER reanalysis and observed data for all parameters, except for wind speed, with coefficient of determination (R2) higher than 0.82, with normalized root mean square error (NRMSE) varying, from 8 to 20%, and a normalized mean bias error (NMBE) ranging from –9 to 26%, for those variables. Based on these results, and in order to improve the accuracy of the NASA POWER dataset, two bias corrections were performed to all weather variables: one for the Alentejo Region as a whole; another, for each location individually. Results improved significantly, especially when a local bias correction is performed, with Tmax and Tmin presenting an improvement of the mean NRMSE of 6.6 °C (from 8.0 °C) and 16.1 °C (from 20.5 °C), respectively, while a mean NMBE decreased from 10.65 to 0.2%. Rs results also show a very high goodness of fit with a mean NRMSE of 11.2% and mean NMBE equal to 0.1%. Additionally, bias corrected RH data performed acceptably with an NRMSE lower than 12.1% and an NMBE below 2.1%. However, even when a bias correction is performed, Ws lacks the performance showed by the remaining weather variables, with an NRMSE never lower than 19.6%. Results show that NASA POWER can be useful for the generation of weather data sets where ground weather stations data is of missing or unavailable.

scholarly journals Space-Time Sea Surface pCO2 Estimation in the North Atlantic Based on CatBoost

2021 ◽  
Vol 13 (14) ◽  
pp. 2805
Author(s):  
Hongwei Sun ◽  
Junyu He ◽  
Yihui Chen ◽  
Boyu Zhao
Keyword(s):  
North Atlantic ◽  
Function Analysis ◽  
Biological Activities ◽  
Mixed Layer Depth ◽  
Sea Surface ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
The North ◽  
The North Atlantic

Sea surface partial pressure of CO2 (pCO2) is a critical parameter in the quantification of air–sea CO2 flux, which plays an important role in calculating the global carbon budget and ocean acidification. In this study, we used chlorophyll-a concentration (Chla), sea surface temperature (SST), dissolved and particulate detrital matter absorption coefficient (Adg), the diffuse attenuation coefficient of downwelling irradiance at 490 nm (Kd) and mixed layer depth (MLD) as input data for retrieving the sea surface pCO2 in the North Atlantic based on a remote sensing empirical approach with the Categorical Boosting (CatBoost) algorithm. The results showed that the root mean square error (RMSE) is 8.25 μatm, the mean bias error (MAE) is 4.92 μatm and the coefficient of determination (R2) can reach 0.946 in the validation set. Subsequently, the proposed algorithm was applied to the sea surface pCO2 in the North Atlantic Ocean during 2003–2020. It can be found that the North Atlantic sea surface pCO2 has a clear trend with latitude variations and have strong seasonal changes. Furthermore, through variance analysis and EOF (empirical orthogonal function) analysis, the sea surface pCO2 in this area is mainly affected by sea temperature and salinity, while it can also be influenced by biological activities in some sub-regions.

scholarly journals Comparative Study of Ground Measured, Satellite-Derived, and Estimated Global Solar Radiation Data in Nigeria

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Boluwaji M. Olomiyesan ◽  
Onyedi D. Oyedum
Keyword(s):  
Solar Radiation ◽  
Comparative Study ◽  
Global Radiation ◽  
Statistical Error ◽  
Global Solar Radiation ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Radiation Data ◽  
Proposed Model

In this study, the performance of three global solar radiation models and the accuracy of global solar radiation data derived from three sources were compared. Twenty-two years (1984–2005) of surface meteorological data consisting of monthly mean daily sunshine duration, minimum and maximum temperatures, and global solar radiation collected from the Nigerian Meteorological (NIMET) Agency, Oshodi, Lagos, and the National Aeronautics Space Agency (NASA) for three locations in North-Western region of Nigeria were used. A new model incorporating Garcia model into Angstrom-Prescott model was proposed for estimating global radiation in Nigeria. The performances of the models used were determined by using mean bias error (MBE), mean percentage error (MPE), root mean square error (RMSE), and coefficient of determination (R2). Based on the statistical error indices, the proposed model was found to have the best accuracy with the least RMSE values (0.376 for Sokoto, 0.463 for Kaduna, and 0.449 for Kano) and highest coefficient of determination, R2 values of 0.922, 0.938, and 0.961 for Sokoto, Kano, and Kaduna, respectively. Also, the comparative study result indicates that the estimated global radiation from the proposed model has a better error range and fits the ground measured data better than the satellite-derived data.

scholarly journals Sorption isotherms and their fitted equations for dried chips of cassava roots (Manihot Esculenta Crantz; Tme-7 variety) and the resulting isosteric heats of sorption

Food Research ◽  
2020 ◽  
Vol 4 (3) ◽  
pp. 703-711
Author(s):  
A.S. Ajala ◽  
P.O. Ngoddy ◽  
J.O. Olajide
Keyword(s):  
Relative Humidity ◽  
Shelf Life ◽  
Gravimetric Method ◽  
Sorption Isotherms ◽  
Isosteric Heat ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Isotherm Models ◽  
Bias Error ◽  
Manihot Esculenta Crantz

Cassava roots are susceptible to deterioration with 24 hrs of harvest; it needs processing into a more stable material such as dried cassava chips to extend its shelf life for long storage. However, improper knowledge of the effect of atmospheric relative humidity on these dried chips during storage makes it mouldy and unacceptable. This work aimed at studying the effect of sorption isotherms on the dried cassava chips. In this study, adsorption and desorption isotherm were carried out using static gravimetric method and data for equilibrium moisture content (EMC) were generated at five (5) temperatures (53, 60, 70, 80, 86oC). These were fitted into four (4) isotherm-models [Oswin, Peleg, the Modified Oswin and GAB]. The statistical criteria to test the models were coefficient of determination (R2 ), reduced chi-square (χ 2 ), root mean square error (RMSE) and mean bias error (MBE). The values of EMC ranged from 7.21-12.44% wb. The values of R2 ranged from 0.95-0.99; χ 2 ranged from 0.008-0.14; RMSE values ranged from 0.06-0.254 while MBE values ranged from -0.0004-1.1E-5. The values of isosteric heat of sorption calculated from the isosteres recorded a range from 6.579 to 67.829 kJ/mole. The Pelegmodel gave the best fit in the relative humidity range of 10 to 80%. The values of EMC show that the chips can have a stable shelf life without spoilage.

scholarly journals Using digital image processing to characterize the Campbell–Stokes sunshine recorder and to derive high-temporal resolution direct solar irradiance

2015 ◽  
Vol 8 (1) ◽  
pp. 183-194 ◽  
Author(s):  
A. Sanchez-Romero ◽  
J. A. González ◽  
J. Calbó ◽  
A. Sanchez-Lorenzo
Keyword(s):  
Image Processing ◽  
Temporal Resolution ◽  
Solar Irradiance ◽  
Atmospheric Aerosol ◽  
Mean Squared Error ◽  
Sunshine Duration ◽  
Strong Relationship ◽  
Mean Bias Error ◽  
Bias Error ◽  
High Temporal Resolution

Abstract. The Campbell–Stokes sunshine recorder (CSSR) has been one of the most commonly used instruments for measuring sunshine duration (SD) through the burn length of a given CSSR card. Many authors have used SD to obtain information about cloudiness and solar radiation (by using Ångström–Prescott type formulas), but the burn width has not been used systematically. In principle, the burn width increases for increasing direct beam irradiance. The aim of this research is to show the relationship between burn width and direct solar irradiance (DSI) and to prove whether this relationship depends on the type of CSSR and burning card. A method of analysis based on image processing of digital scanned images of burned cards is used. With this method, the temporal evolution of the burn width with 1 min resolution can be obtained. From this, SD is easily calculated and compared with the traditional (i.e., visual) determination. The method tends to slightly overestimate SD, but the thresholds that are used in the image processing could be adjusted to obtain an improved estimation. Regarding the burn width, experimental results show that there is a high correlation between two different models of CSSRs, as well as a strong relationship between burn widths and DSI at a high-temporal resolution. Thus, for example, hourly DSI may be estimated from the burn width with higher accuracy than based on burn length (for one of the CSSR, relative root mean squared error is 24 and 30%, respectively; mean bias error is −0.6 and −30.0 W m−2, respectively). The method offers a practical way to exploit long-term sets of CSSR cards to create long time series of DSI. Since DSI is affected by atmospheric aerosol content, CSSR records may also become a proxy measurement for turbidity and atmospheric aerosol loading.

scholarly journals Estimating daily evapotranspiration based on a model of evaporative fraction (EF) for mixed pixels

2019 ◽  
Vol 23 (2) ◽  
pp. 949-969
Author(s):  
Fugen Li ◽  
Xiaozhou Xin ◽  
Zhiqing Peng ◽  
Qinhuo Liu
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Heihe River ◽  
Natural Ecosystems ◽  
Evaporative Fraction ◽  
Mixed Pixel ◽  
Mixed Pixels

Abstract. Currently, applications of remote sensing evapotranspiration (ET) products are limited by the coarse resolution of satellite remote sensing data caused by land surface heterogeneities and the temporal-scale extrapolation of the instantaneous latent heat flux (LE) based on satellite overpass time. This study proposes a simple but efficient model (EFAF) for estimating the daily ET of remotely sensed mixed pixels using a model of the evaporative fraction (EF) and area fraction (AF) to increase the accuracy of ET estimate over heterogeneous land surfaces. To accomplish this goal, we derive an equation for calculating the EF of mixed pixels based on two key hypotheses. Hypothesis 1 states that the available energy (AE) of each sub-pixel is approximately equal to that of any other sub-pixels in the same mixed pixel within an acceptable margin of error and is equivalent to the AE of the mixed pixel. This approach simplifies the equation, and uncertainties and errors related to the estimated ET values are minor. Hypothesis 2 states that the EF of each sub-pixel is equal to that of the nearest pure pixel(s) of the same land cover type. This equation is designed to correct spatial-scale errors for the EF of mixed pixels; it can be used to calculate daily ET from daily AE data. The model was applied to an artificial oasis located in the midstream area of the Heihe River using HJ-1B satellite data with a 300 m resolution. The results generated before and after making corrections were compared and validated using site data from eddy covariance systems. The results show that the new model can significantly improve the accuracy of daily ET estimates relative to the lumped method; the coefficient of determination (R2) increased to 0.82 from 0.62, the root mean square error (RMSE) decreased to 1.60 from 2.47 MJ m−2(decreased approximately to 0.64 from 0.99 mm) and the mean bias error (MBE) decreased from 1.92 to 1.18 MJ m−2 (decreased from approximately 0.77 to 0.47 mm). It is concluded that EFAF can reproduce daily ET with reasonable accuracy; can be used to produce the ET product; and can be applied to hydrology research, precision agricultural management and monitoring natural ecosystems in the future.

scholarly journals Drying of MalaysianCapsicum annuumL. (Red Chili) Dried by Open and Solar Drying

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Ahmad Fudholi ◽  
Mohd Yusof Othman ◽  
Mohd Hafidz Ruslan ◽  
Kamaruzzaman Sopian
Keyword(s):  
Root Mean Square ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Solar Drying ◽  
Mean Square ◽  
Drying Time ◽  
Sun Drying ◽  
65 H ◽  
Mean Square Errors

This study evaluated the performance of solar drying in the Malaysian red chili (Capsicum annuumL.). Red chilies were dried down from approximately 80% (wb) to 10% (wb) moisture content within 33 h. The drying process was conducted during the day, and it was compared with 65 h of open sun drying. Solar drying yielded a 49% saving in drying time compared with open sun drying. At the average solar radiation of 420 W/m2and air flow rate of 0.07 kg/s, the collector, drying system, and pickup demonstrated efficiency rates of approximately 28%, 13%, and 45%, respectively. Evaporative capacity ranged from 0.13 to 2.36 kg/h, with an average of 0.97 kg/h. The specific moisture extraction rate (SMER) of 0.19 kg/kWh was obtained. Moreover, the drying kinetics ofC. annuumL. were investigated. A nonlinear regression procedure was used to fit three drying models. These models were compared with experimental data on red chilies dried by open sun drying and those dried by solar drying. The fit quality of the models was evaluated using their coefficient of determination (R2), mean bias error, and root-mean-square error values. The Page model resulted in the highestR2and the lowest mean bias and root-mean-square errors.

scholarly journals Empirical Model for Estimating Global Solar Radiation on Horizontal Surfaces for Selected Cities in the Six Geopolitical Zones in Nigeria

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
M. S. Okundamiya ◽  
A. N. Nzeako
Keyword(s):  
Solar Radiation ◽  
Global Solar Radiation ◽  
Mean Bias Error ◽  
Bias Error ◽  
Data Set ◽  
Ambient Temperatures ◽  
Performance Accuracy ◽  
Daily Data ◽  
Proposed Model ◽  
Regression Theory

This study proposes a temperature-based model of monthly mean daily global solar radiation on horizontal surfaces for selected cities, representing the six geopolitical zones in Nigeria. The modelling was based on linear regression theory and was computed using monthly mean daily data set for minimum and maximum ambient temperatures. The results of three statistical indicators: Mean Bias Error (MBE), Root Mean Square Error (RMSE), andt-statistic (TS), performed on the model along with practical comparison of the estimated and observed data, validate the excellent performance accuracy of the proposed model.

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