scholarly journals Evaluation and Recommendations for Improving the Accuracy of an Inexpensive Water Temperature Logger

2013 ◽  
Vol 30 (7) ◽  
pp. 1576-1582 ◽  
Author(s):  
S. J. Lentz ◽  
J. H. Churchill ◽  
C. Marquette ◽  
J. Smith
Keyword(s):  
Surface Layer ◽  
Solar Radiation ◽  
Water Temperature ◽  
Solar Heating ◽  
Bias Error ◽  
Mean Square ◽  
Temperature Logger ◽  
Accurate Temperature ◽  
Rms Error ◽  
Rms Errors

Abstract Onset's HOBO U22 Water Temp Pros are small, reliable, relatively inexpensive, self-contained temperature loggers that are widely used in studies of oceans, lakes, and streams. An in-house temperature bath calibration of 158 Temp Pros indicated root-mean-square (RMS) errors ranging from 0.01° to 0.14°C, with one value of 0.23°C, consistent with the factory specifications. Application of a quadratic calibration correction substantially reduced the RMS error to less than 0.009°C in all cases. The primary correction was a bias error typically between −0.1° and 0.15°C. Comparison of water temperature measurements from Temp Pros and more accurate temperature loggers during two oceanographic studies indicates that calibrated Temp Pros have an RMS error of ~0.02°C throughout the water column at night and beneath the surface layer influenced by penetrating solar radiation during the day. Larger RMS errors (up to 0.08°C) are observed near the surface during the day due to solar heating of the black Temp Pro housing. Errors due to solar heating are significantly reduced by wrapping the housing with white electrical tape.

scholarly journals Estimation of Global Solar Radiation using Modifed Angstrom Empirical formula on the basis of Meterological parameters in Himalaya Region Pokhara, Nepal

2016 ◽  
Vol 11 (1) ◽  
pp. 158-164
Author(s):  
Khem N. Poudyal
Keyword(s):  
Solar Radiation ◽  
Meteorological Parameters ◽  
Research Work ◽  
Global Solar Radiation ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Mean Square ◽  
Performance Parameters ◽  
Sunshine Hour

This research work proposes the coefficient equation of modified Angstrom   model using sunshine hour and meteorological parameters for the estimation of global solar radiation in Himalaya Region Pokhara (28.22° N, 83.32° E),  Nepal . This site is about 800.0 m above from the sea level lying just 20.0 km south of the Machhaputre Himalayas.  The model coefficients a and b obtained in this research are 0.43 and 0.23 respectively. The performance parameters of the model are: Root Mean Square Error RMSE = 0.13 MJ/m2 /day, Mean Bias Error MBE= 0.02 MJ/m /day Mean Percentage MPE= 5 percent and coefficient of determination R2 = 0.70. Journal of the Institute of Engineering, 2015, 11(1): 158-164

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 The Analysis of Dynamic O/D Adjustment for Bicycle Traffic Demand Estimation with AIMSUN Simulation Model: A Case Study of Nakhon Sawan Municipality in Thailand

2018 ◽  
Vol 12 (1) ◽  
pp. 352-365 ◽  
Author(s):  
Karn Chalermwongphan ◽  
Prapatpong Upala
Keyword(s):  
Simulation Model ◽  
Root Mean Square ◽  
Goodness Of Fit ◽  
Simulation Software ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Mean Square ◽  
Goodness Of Fit Test ◽  
Traffic Demand

Aim: This research aimed to present the process of estimating bicycle traffic demand in order to design bike routes that meet the daily transportation needs of the people in Nakhon Sawan Municipality. Methods: The primary and secondary traffic data were collected to develop a virtual traffic simulation model with the use of the AIMSUN simulation software. The model validation method was carried out to adjust the origin and destination survey data (O/D matrix) by running dynamic O/D adjustment. The 99 replication scenarios were statistically examined and assessed using the goodness-of-fit test. The 9 measures, which were examined, included: 1) Root Mean Square Error (RMSE), 2) Root Mean Square Percentage Error (RMSPE%), 3) Mean Absolute Deviation (MAD), 4) Mean Bias Error (MBE), 5) Mean Percentage Error (MPE%), 6) Mean Absolute Percentage Error (MAPE%), 7) Coefficient of Determination (R2), 8) GEH Statistic (GEH), and 9) Thiel’s U Statistic (Theil’s U). Results: The resulting statistical values were used to determine the acceptable ranges according to the acceptable indicators of each factor. Conclusion: It was found that there were only 8 scenarios that met the evaluation criteria. The selection and ranking process was consequently carried out using the multi-factor scoring method, which could eliminate errors that might arise from applying only one goodness-of-fit test measure.

scholarly journals A 16-year dataset (2000–2015) of high-resolution (3 h, 10 km) global surface solar radiation

2019 ◽  
Vol 11 (4) ◽  
pp. 1905-1915 ◽  
Author(s):  
Wenjun Tang ◽  
Kun Yang ◽  
Jun Qin ◽  
Xin Li ◽  
Xiaolei Niu
Keyword(s):  
High Resolution ◽  
Solar Radiation ◽  
Radiant Energy ◽  
Reanalysis Data ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Mean Bias Error ◽  
Bias Error ◽  
Surface Solar Radiation ◽  
Global Surface

Abstract. The recent release of the International Satellite Cloud Climatology Project (ISCCP) HXG cloud products and new ERA5 reanalysis data enabled us to produce a global surface solar radiation (SSR) dataset: a 16-year (2000–2015) high-resolution (3 h, 10 km) global SSR dataset using an improved physical parameterization scheme. The main inputs were cloud optical depth from ISCCP-HXG cloud products; the water vapor, surface pressure and ozone from ERA5 reanalysis data; and albedo and aerosol from Moderate Resolution Imaging Spectroradiometer (MODIS) products. The estimated SSR data were evaluated against surface observations measured at 42 stations of the Baseline Surface Radiation Network (BSRN) and 90 radiation stations of the China Meteorological Administration (CMA). Validation against the BSRN data indicated that the mean bias error (MBE), root mean square error (RMSE) and correlation coefficient (R) for the instantaneous SSR estimates at 10 km scale were −11.5 W m−2, 113.5 W m−2 and 0.92, respectively. When the estimated instantaneous SSR data were upscaled to 90 km, its error was clearly reduced, with RMSE decreasing to 93.4 W m−2 and R increasing to 0.95. For daily SSR estimates at 90 km scale, the MBE, RMSE and R at the BSRN were −5.8 W m−2, 33.1 W m−2 and 0.95, respectively. These error metrics at the CMA radiation stations were 2.1 W m−2, 26.9 W m−2 and 0.95, respectively. Comparisons with other global satellite radiation products indicated that our SSR estimates were generally better than those of the ISCCP flux dataset (ISCCP-FD), the global energy and water cycle experiment surface radiation budget (GEWEX-SRB), and the Earth's Radiant Energy System (CERES). Our SSR dataset will contribute to the land-surface process simulations and the photovoltaic applications in the future. The dataset is available at  https://doi.org/10.11888/Meteoro.tpdc.270112 (Tang, 2019).

scholarly journals Estimation of monthly global solar radiation in Buenos Aires: preliminary analysis

2014 ◽  
Vol 5 (1) ◽  
pp. 669-680
Author(s):  
Susan G. Lakkis ◽  
Mario Lavorato ◽  
Pablo O. Canziani
Keyword(s):  
Solar Radiation ◽  
Buenos Aires ◽  
Meteorological Data ◽  
Global Solar Radiation ◽  
Estimation Accuracy ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Third Order ◽  
Sunshine Hours

Six existing models and one proposed approach for estimating global solar radiation were tested in Buenos Aires using commonly measured meteorological data as temperature and sunshine hours covering the years 2010-2013. Statistical predictors as mean bias error, root mean square, mean percentage error, slope and regression coefficients were used as validation criteria. The variability explained (R2), slope and MPE indicated that the higher precision could be excepted when sunshine hours are used as predictor. The new proposed approach explained almost 99% of the RG variability with deviation of less than ± 0.1 MJm-2day-1 and with the MPE smallest value below 1 %. The well known Ångström-Prescott methods, first and third order, was also found to perform for the measured data with high accuracy (R2=0.97-0.99) but with slightly higher MBE values (0.17-0.18 MJm-2day-1). The results pointed out that the third order Ångström type correlation did not improve the estimation accuracy of solar radiation given the highest range of deviation and mean percentage error obtained.  Where the sunshine hours were not available, the formulae including temperature data might be considered as an alternative although the methods displayed larger deviation and tended to overestimate the solar radiation behavior.

scholarly journals Analysis of the potential use of Trombe walls in Brazil: design recommendations

2020 ◽  
Vol 5 ◽  
pp. 4
Author(s):  
Fernando Antonio de Melo Sá Cavalcanti ◽  
Rosana Maria Caram
Keyword(s):  
Solar Radiation ◽  
Computer Simulations ◽  
Thermal Performance ◽  
Natural Ventilation ◽  
Solar Heating ◽  
Heating And Cooling ◽  
Heated Air ◽  
Trombe Wall ◽  
Potential Use ◽  
Passive Strategy

In this paper, the thermal performance of a standard environment was evaluated based on the use of a Trombe wall with different configurations and types of use, as the potential for using this passive strategy is still little studied in Brazil. This device is capable of absorbing energy from solar radiation by heating the air in this greenhouse and this heated air can be directed to the interior or exterior of the building depending on the purpose. This air can be used to heat the room or cool it by means of natural ventilation. The analysis of this research was based on a series of computer simulations using the EnergyPlus software, version 7.0 in order to quantify and classify the thermal performance of a standard environment equipped with this component, under the various construction configurations. Both for heating and cooling environments. The use of Trombe walls improved the thermal comfort of users in buildings located in Brazil, depending on the climate where they are located, promoting natural ventilation and passive solar heating, allowing the potential of this device to be investigated in the most diverse Brazilian regions.

scholarly journals A Comparison of Models for Estimating Solar Radiation from Sunshine Duration in Croatia

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Tihomir Betti ◽  
Ivana Zulim ◽  
Slavica Brkić ◽  
Blanka Tuka
Keyword(s):  
Mean Absolute Error ◽  
Sunshine Duration ◽  
Model Performance ◽  
Absolute Error ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Mean Square ◽  
Using Data ◽  
Statistical Indicators

The performance of seventeen sunshine-duration-based models has been assessed using data from seven meteorological stations in Croatia. Conventional statistical indicators are used as numerical indicators of the model performance: mean absolute percentage error (MAPE), mean bias error (MBE), mean absolute error (MAE), and root-mean-square error (RMSE). The ranking of the models was done using the combination of all these parameters, all having equal weights. The Rietveld model was found to perform the best overall, followed by Soler and Dogniaux-Lemoine monthly dependent models. For three best-performing models, new adjusted coefficients are calculated, and they are validated using separate dataset. Only the Dogniaux-Lemoine model performed better with adjusted coefficients, but across all analysed locations, the adjusted models showed improvement in reduced maximum percentage error.

Prévision des températures de l'eau en rivières à l'aide d'un modèle conceptuel : le cas de la rivière Moisie

1994 ◽  
Vol 21 (1) ◽  
pp. 63-75 ◽  
Author(s):  
Guy Morin ◽  
Tonino-Joseph Nzakimuena ◽  
Wanda Sochanski
Keyword(s):  
Solar Radiation ◽  
Water Temperature ◽  
Energy Budget ◽  
Production Capacity ◽  
Short Wave ◽  
Wave Radiation ◽  
Temperature Model ◽  
Salmon River ◽  
Daily Data ◽  
Temperature Impacts

Hydro-Québec is projecting to increase the hydroelectric production capacity of the St. Marguerite River by diversion of the tributaries Pékans and Carheil rivers of the Moisie River, the most productive salmon river of the whole Quebec. Along with substantial changes in hydrological regimes, this hydroelectric development is most likely to affect some physical environment factors such as the water temperature, which is of prime importance for the biotope and, in particular, for the salmon productivity. The objective of the present study is to simulate, over a long period of time, the river water temperatures under natural conditions as compare to those after the impoundment, to assess the consequences of the tributary diversion. We used the hydrological CEQUEAU model coupled with a temperature model.The temperature model developed is applicable to the ice-free period and calculates daily water temperatures in rivers by computing an energy budget to each element of the watershed. The energy budget considers the short-wave solar radiation, long-wave radiation, evaporation, and convection in the air as well as the advective heat of various inflows from surface runoff, interflow, and groundwaters. The estimation of the atmospheric thermal exchanges is based on the equations usually found in literature. The volumes of the various inflows are given by the hydrological model. The temperature model uses daily data for air temperature and monthly data for solar radiation, cloudiness, wind speed, and vapour pressure.The model has been applied to the Moisie River (Québec), using the measured values for the calibration. Both observed and calculated values show good agreement. The model was also used to simulate, over the whole watershed, the water temperatures for the 1961–1989 period and after the diversion. The results show that the tributary diversion contributed to increase the water temperature of the Moisie River and that this increase is gradually attenuated as we progress downstream. Key words: temperature, impacts, model, Moisie, Québec, diversion, hydrology.

scholarly journals The thermal structure of the anoxic trough in Lake Untersee, Antarctica

2018 ◽  
Vol 30 (6) ◽  
pp. 333-344 ◽  
Author(s):  
James Bevington ◽  
Christopher P. McKay ◽  
Alfonso Davila ◽  
Ian Hawes ◽  
Yukiko Tanabe ◽  
...  
Keyword(s):  
Stability Analysis ◽  
Solar Radiation ◽  
Water Temperature ◽  
Energy Input ◽  
Thermal Structure ◽  
Field Measurements ◽  
Maximum Depth ◽  
Antarctic Lake ◽  
Thermal Maximum ◽  
Anoxic Basin

AbstractLake Untersee is a perennially ice-covered Antarctic lake that consists of two basins. The deepest basin, next to the Anuchin Glacier is aerobic to its maximum depth of 160 m. The shallower basin has a maximum depth of 100 m, is anoxic below 80 m, and is shielded from convective currents. The thermal profile in the anoxic basin is unusual in that the water temperature below 50 m is constant at 4°C but rises to 5°C between 70 m and 80 m depth, then drops to 3.7°C at the bottom. Field measurements were used to conduct a thermal and stability analysis of the anoxic basin. The shape of the thermal maximum implies two discrete locations of energy input, one of 0.11 W m-2 at 71 m depth and one of 0.06 W m-2 at 80 m depth. Heat from microbial activity cannot account for the required amount of energy at either depth. Instead, absorption of solar radiation due to an increase in water opacity at these depths can account for the required energy input. Hence, while microbial metabolism is not an important source of heat, biomass increases opacity in the water column resulting in greater absorption of sunlight.

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