scholarly journals Performance Analysis of Clear Sky Global Horizontal Irradiance Models: Simple Models Adapted for Local Conditions

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Nicholas Kwarikunda ◽  
Zivayi Chiguvare
Keyword(s):  
Solar Energy ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Measured Data ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Mean Square ◽  
Clear Sky

Evaluation of the maximum solar energy potential of a given area for possible deployment of solar energy technologies requires assessment of clear sky solar irradiance for the region under consideration. Such localized assessment is critical for optimal sizing of the technology to be deployed in order to realize the anticipated output. As the measurements are not always available where they are needed, models may be used to estimate them. In this study, three different models were adapted for the geographical location of the area under study and used to estimate clear sky global horizontal irradiance (GHI) at three locations in the subtropical desert climate of Namibia. The three models, selected on the basis of input requirements, were used to compute clear sky GHI at Kokerboom, Arandis, and Auas. The models were validated and evaluated for performance using irradiance data measured at each of the sites for a period of three years by computing statistical parameters such as mean bias error (MBE), root mean square error (RMSE), and the coefficient of determination (R2), normalized MBE, and normalized RMSE. Comparative results between modelled and measured data showed that the models fit well the measured data, with normalized root mean square error values in the range 4–8%, while the R2 value was above 98% for the three models. The adapted models can thus be used to compute clear sky GHI at these study areas as well as in other regions with similar climatic conditions.

scholarly journals Solar radiation forecasting by using deep neural networks in Eski̇şehi̇r

2021 ◽  
pp. 159-169
Author(s):  
Mohammed QASEM
Keyword(s):  
Neural Network ◽  
Solar Radiation ◽  
Solar Energy ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Clean Energy ◽  
Mean Bias Error ◽  
Bias Error ◽  
Mean Square

According to the World Economic Outlook (WEO), the global demand for energy is presum- ably going to be increased due to growing the world’s population up during the upcoming two decades. As a result of that, apprehensions about environmental effects, which appear as a re- sult of greenhouse gases are grown and cleaner energy technologies are developed. This clearly shows that extended growth of the worldwide market share of clean energy. Solar energy is considered as one of the fundamental types of renewable energy. For this reason, the need for a predictive model that effectively observes solar energy conversion with high performance becomes urgent. In this paper, classic empirical, artificial neural network (ANN), deep neural network (DNN), and time series models are applied, and their results are compared to each other to find the most accurate model for daily global solar radiation (DGSR) estimation. In addition, four regression models have been developed and applied for DGSR estimation. The obtained results are evaluated and compared by the root mean square error (RMSE), rela- tive root mean square error (rRMSE), mean absolute error (MAE), mean bias error (MBE), t-statistic, and coefficient of determination (R2). Finally, simulation results provided that the best result is found by the DNN model.

scholarly journals Using supervised learning to develop BaRAD, a 40-year monthly bias-adjusted global gridded radiation dataset

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
T. C. Chakraborty ◽  
Xuhui Lee
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Diffuse Radiation ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Shortwave Radiation ◽  
Mean Bias Error ◽  
Bias Error ◽  
Mean Square

AbstractDiffuse solar radiation is an important, but understudied, component of the Earth’s surface radiation budget, with most global climate models not archiving this variable and a dearth of ground-based observations. Here, we describe the development of a global 40-year (1980–2019) monthly database of total shortwave radiation, including its diffuse and direct beam components, called BaRAD (Bias-adjusted RADiation dataset). The dataset is based on a random forest algorithm trained using Global Energy Balance Archive (GEBA) observations and applied to the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) dataset at the native MERRA-2 resolution (0.5° by 0.625°). The dataset preserves seasonal, latitudinal, and long-term trends in the MERRA-2 data, but with reduced biases than MERRA-2. The mean bias error is close to 0 (root mean square error = 10.1 W m−2) for diffuse radiation and −0.2 W m−2 (root mean square error = 19.2 W m−2) for the total incoming shortwave radiation at the surface. Studies on atmosphere-biosphere interactions, especially those on the diffuse radiation fertilization effect, can benefit from this dataset.

scholarly journals A High-Resolution Topographic Correction Method for Clear-Sky Solar Irradiance Derived with a Numerical Weather Prediction Model

2011 ◽  
Vol 50 (12) ◽  
pp. 2460-2472 ◽  
Author(s):  
José A. Ruiz-Arias ◽  
David Pozo-Vázquez ◽  
Vicente Lara-Fanego ◽  
Francisco J. Santos-Alamillos ◽  
J. Tovar-Pescador
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Numerical Weather Prediction ◽  
Solar Irradiance ◽  
Weather Prediction ◽  
Mean Bias Error ◽  
Bias Error ◽  
Mean Square ◽  
Numerical Weather

AbstractRugged terrain is a source of variability in the incoming solar radiation field, but the influence of terrain is still not properly included by most current numerical weather prediction (NWP) models. In this work, a downscaling postprocessing method for NWP-model solar irradiance through terrain effects is presented. It allows one to decrease the estimation bias caused by terrain shading and sky-view reduction, and to account for elevation variability, surface orientation, and surface albedo. The method has been applied to a case study in southern Spain using the Weather Research and Forecasting (WRF) mesoscale model with a spatial resolution of 30 arc s, resulting in disaggregated maps of 3 arc s. The validation was based on a radiometric network made of eight stations located in the Natural Park of Sierra Mágina over an area of roughly 30 × 35 km2 and 12 carefully selected cloudless days during a year. Three of the stations were equipped with tilted pyranometers. Their inclination and aspect were visually adjusted to the inclination and aspect of the local terrain and then carefully measured. For horizontal surface, the downscaled irradiance has proven to reduce the root-mean-square error of the WRF model by 20% to about 25 W m−2 in winter and autumn and 60 W m−2 in spring and summer. For tilted surface, downscaling to different spatial resolutions resulted in the best performance for 9 arc s, with root-mean-square error of 45% (57 W m−2) and a mean bias error close to zero.

scholarly journals Authentication of Patin Fish Oil (Pangasius micronemus) using FTIR Spectroscopy Combined with Chemometrics

2020 ◽  
pp. 23
Author(s):  
Anggita Rosiana Putri ◽  
Abdul Rohman ◽  
Sugeng Riyanto ◽  
Widiastuti Setyaningsih
Keyword(s):  
Root Mean Square Error ◽  
Fish Oil ◽  
Ftir Spectroscopy ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Coefficient Of Determination ◽  
Mean Square

Authentication of Patin fish oil (MIP) is essential to prevent adulteration practice, to ensure quality, nutritional value, and product safety. The purpose of this study is to apply the FTIR spectroscopy combined with chemometrics for MIP authentication. The chemometrics method consists of principal component regression (PCR) and partial least square regression (PLSR). PCR and PLSR were used for multivariate calibration, while for grouping the samples using discriminant analysis (DA) method. In this study, corn oil (MJ) was used as an adulterate. Twenty-one mixed samples of MIP and MJ were prepared with the adulterate concentration range of 0-50%. The best authentication model was obtained using the PLSR technique using the first derivative of FTIR spectra at a wavelength of 650-3432 cm-1. The coefficient of determination (R2) for calibration and validation was obtained 0.9995 and 1.0000, respectively. The value of root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were 0.397 and 0.189. This study found that the DA method can group the samples with an accuracy of 99.92%.

Estimating Center of Mass Kinematics During Perturbed Human Standing Using Accelerometers

2021 ◽  
pp. 1-10
Author(s):  
Sandra K. Hnat ◽  
Musa L. Audu ◽  
Ronald J. Triolo ◽  
Roger D. Quinn
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Mean Squared Error ◽  
Balance Control ◽  
Center Of Mass ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Cord Injury ◽  
Movement Type

Estimating center of mass (COM) through sensor measurements is done to maintain walking and standing stability with exoskeletons. The authors present a method for estimating COM kinematics through an artificial neural network, which was trained by minimizing the mean squared error between COM displacements measured by a gold-standard motion capture system and recorded acceleration signals from body-mounted accelerometers. A total of 5 able-bodied participants were destabilized during standing through: (1) unexpected perturbations caused by 4 linear actuators pulling on the waist and (2) volitionally moving weighted jars on a shelf. Each movement type was averaged across all participants. The algorithm’s performance was quantified by the root mean square error and coefficient of determination (R2) calculated from both the entire trial and during each perturbation type. Throughout the trials and movement types, the average coefficient of determination was 0.83, with 89% of the movements with R2 > .70, while the average root mean square error ranged between 7.3% and 22.0%, corresponding to 0.5- and 0.94-cm error in both the coronal and sagittal planes. COM can be estimated in real time for balance control of exoskeletons for individuals with a spinal cord injury, and the procedure can be generalized for other gait studies.

scholarly journals Estimating Maximum Daily Precipitation in the Upper Vistula Basin, Poland

Atmosphere ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 43 ◽  
Author(s):  
Dariusz Młyński ◽  
Andrzej Wałęga ◽  
Andrea Petroselli ◽  
Flavia Tauro ◽  
Marta Cebulska
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Goodness Of Fit ◽  
Daily Precipitation ◽  
Probability Distributions ◽  
Random Variables ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Probability Of Exceedance

The aim of this study was to determine the best probability distributions for calculating the maximum annual daily precipitation with the specific probability of exceedance (Pmaxp%). The novelty of this study lies in using the peak-weighted root mean square error (PWRMSE), the root mean square error (RMSE), and the coefficient of determination (R2) for assessing the fit of empirical and theoretical distributions. The input data included maximum daily precipitation records collected in the years 1971–2014 at 51 rainfall stations from the Upper Vistula Basin, Southern Poland. The value of Pmaxp% was determined based on the following probability distributions of random variables: Pearson’s type III (PIII), Weibull’s (W), log-normal, generalized extreme value (GEV), and Gumbel’s (G). Our outcomes showed a lack of significant trends in the observation series of the investigated random variables for a majority of the rainfall stations in the Upper Vistula Basin. We found that the peak-weighted root mean square error (PWRMSE) method, a commonly used metric for quality assessment of rainfall-runoff models, is useful for identifying the statistical distributions of the best fit. In fact, our findings demonstrated the consistency of this approach with the RMSE goodness-of-fit metrics. We also identified the GEV distribution as recommended for calculating the maximum daily precipitation with the specific probability of exceedance in the catchments of the Upper Vistula Basin.

scholarly journals Using Multi-Temporal MODIS NDVI Data to Monitor Tea Status and Forecast Yield: A Case Study at Tanuyen, Laichau, Vietnam

2020 ◽  
Vol 12 (11) ◽  
pp. 1814
Author(s):  
Phamchimai Phan ◽  
Nengcheng Chen ◽  
Lei Xu ◽  
Zeqiang Chen
Keyword(s):  
Remote Sensing ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Vegetation Index ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Mean Square ◽  
Multi Temporal

Tea is a cash crop that improves the quality of life for people in the Tanuyen District of Laichau Province, Vietnam. Tea yield, however, has stagnated in recent years, due to changes in temperature, precipitation, the age of the tea bushes, and diseases. Developing an approach for monitoring tea bushes by remote sensing and Geographic Information Systems (GIS) might be a way to alleviate this problem. Using multi-temporal remote sensing data, the paper details an investigation of the changes in tea health and yield forecasting through the normalized difference vegetation index (NDVI). In this study, we used NDVI as a support tool to demonstrate the temporal and spatial changes in NDVI through the extract tea NDVI value and calculate the mean NDVI value. The results of the study showed that the minimum NDVI value was 0.42 during January 2013 and February 2015 and 2016. The maximum NDVI value was in August 2015 and June 2017. We indicate that the linear relationship between NDVI value and mean temperature was strong with R 2 = 0.79 Our results confirm that the combination of meteorological data and NDVI data can achieve a high performance of yield prediction. Three models to predict tea yield were conducted: support vector machine (SVM), random forest (RF), and the traditional linear regression model (TLRM). For period 2009 to 2018, the prediction tea yield by the RF model was the best with a R 2 = 0.73 , by SVM it was 0.66, and 0.57 with the TLRM. Three evaluation indicators were used to consider accuracy: the coefficient of determination ( R 2 ), root-mean-square error (RMSE), and percentage error of tea yield (PETY). The highest accuracy for the three models was in 2015 with a R 2 ≥ 0.87, RMSE < 50 kg/ha, and PETY less 3% error. In the other years, the prediction accuracy was higher in the SVM and RF models. Meanwhile, the RF algorithm was better than PETY (≤10%) and the root mean square error for this algorithm was significantly less (≤80 kg/ha). RMSE and PETY showed relatively good values in the TLRM model with a RMSE from 80 to 100 kg/ha and a PETY from 8 to 15%.

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 Applicability of Near Infrared Reflectance Spectroscopy to Predict Amylose Contents of Single-Grain Maize

Agronomy ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2463
Author(s):  
Qing Dong ◽  
Qianqian Xu ◽  
Jiandong Wu ◽  
Beijiu Cheng ◽  
Haiyang Jiang
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Near Infrared Reflectance ◽  
Single Grain ◽  
Single Seeds

Near infrared reflectance spectroscopy (NIRS) and reference data were used to determine the amylose contents of single maize seeds to enable rapid, effective selection of individual seeds with desired traits. To predict the amylose contents of a single seed, a total of 1069 (865 as calibration set, 204 as validation set) single seeds representing 120 maize varieties were analyzed using chemical methods and performed calibration and external validation of the 150 single seeds set in parallel. Compared to various spectral pretreatments, the regression of partial least squares (PLS) with mathematical treatment of Harmonization showed the final optimization. The single-seed amylose contents showed the root mean square error of calibration (RMSEC) of 2.899, coefficient of determination for calibration (R2) of 0.902, and root mean square error of validation (RMSEV) of 2.948. In external validations, the coefficient of determination in cross-validation (r2), root mean square error of the prediction (RMSEP) and ratio of the standard deviation to SEP (RPD) were 0.892, 2.975 and 3.086 in the range of 20–30%, respectively. Therefore, NIRS will be helpful to breeders for determining the amylose contents of single-grain maize.

DRYING KINETICS AND MOISTURE DIFFUSIVITY OF FOUR VARIETIES OF BAMBARA BEANS

2021 ◽  
Vol 6 (1) ◽  
pp. 30-33
Author(s):  
E.O. Awotona ◽  
A.O. Alade ◽  
S.A. Adebanjo ◽  
O. Duduyemi ◽  
T.J. Afolabi
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Goodness Of Fit ◽  
Moisture Diffusivity ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Fit Statistics ◽  
Drying Behavior ◽  
Maximum Coefficient

Drying of bambara beans was studied at 40oC at every 30 minutes in a Laboratory oven. Effective moisture diffusivity ranges between 5.886 x 10-10 m2/s – 4.354 x 10-10 m2/s respectively. The statistical criteria used in evaluation of the model were maximum coefficient of determination R2 and minimum root mean square error [RMSE]. Determination for goodness of fit statistics for drying of the beans was carried out. Midilli model was used to predict the drying curve. The Midili model was found to produce accurate predictions for all the four varieties of bambara beans and the model was shown to be an excellent model for predicting drying behavior of TVSU-47 and the R2 value was 0.9971 and the value of root mean square error was 0.0149 respectively.

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