scholarly journals Forecasting the Demand of Oil in Ghana: A Statistical Approach

2021 ◽  
Vol 1 (1) ◽  
pp. 29-43
Author(s):  
Valentina Boamah
Keyword(s):  
Linear Regression ◽  
Statistical Approach ◽  
Exponential Smoothing ◽  
Vital Role ◽  
Energy Sources ◽  
Motor Gasoline ◽  
Exponential Regression ◽  
Oil Demand ◽  
Oil Components ◽  
Distillate Fuel

Oil plays a vital role in the economic growth and sustainability of industries and their corporations. The current study sought to forecast oil demand in Ghana for the next decade. The variables analyzed in this study were Petroleum and other liquids, motor gasoline, distillate fuel, and liquefied petroleum gases (LPG). The study utilized three univariate models; thus, linear regression, exponential regression, and exponential smoothing for forecasting various oil components. The linear regression model was deemed a better fit for the analysis of most of the variables. Furthermore, the findings revealed that the LPG growth rate is faster and requires less time to double in numbers than the other energy sources. Also, the exponential smoothing model was ineffective and inefficient. Overall, the demand for oil components analyzed will follow an increasing pattern from 2017 to 2027.  

scholarly journals CO<sub>2</sub> flux determination by closed-chamber methods can be seriously biased by inappropriate application of linear regression

2007 ◽  
Vol 4 (6) ◽  
pp. 1005-1025 ◽  
Author(s):  
L. Kutzbach ◽  
J. Schneider ◽  
T. Sachs ◽  
M. Giebels ◽  
H. Nykänen ◽  
...  
Keyword(s):  
Linear Regression ◽  
Nonlinear Regression ◽  
Co2 Flux ◽  
Exponential Model ◽  
Co2 Uptake ◽  
Co2 Fluxes ◽  
Closed Chamber ◽  
Exponential Regression ◽  
Closure Time ◽  
Over Time

Abstract. Closed (non-steady state) chambers are widely used for quantifying carbon dioxide (CO2) fluxes between soils or low-stature canopies and the atmosphere. It is well recognised that covering a soil or vegetation by a closed chamber inherently disturbs the natural CO2 fluxes by altering the concentration gradients between the soil, the vegetation and the overlying air. Thus, the driving factors of CO2 fluxes are not constant during the closed chamber experiment, and no linear increase or decrease of CO2 concentration over time within the chamber headspace can be expected. Nevertheless, linear regression has been applied for calculating CO2 fluxes in many recent, partly influential, studies. This approach has been justified by keeping the closure time short and assuming the concentration change over time to be in the linear range. Here, we test if the application of linear regression is really appropriate for estimating CO2 fluxes using closed chambers over short closure times and if the application of nonlinear regression is necessary. We developed a nonlinear exponential regression model from diffusion and photosynthesis theory. This exponential model was tested with four different datasets of CO2 flux measurements (total number: 1764) conducted at three peatlands sites in Finland and a tundra site in Siberia. Thorough analyses of residuals demonstrated that linear regression was frequently not appropriate for the determination of CO2 fluxes by closed-chamber methods, even if closure times were kept short. The developed exponential model was well suited for nonlinear regression of the concentration over time c(t) evolution in the chamber headspace and estimation of the initial CO2 fluxes at closure time for the majority of experiments. However, a rather large percentage of the exponential regression functions showed curvatures not consistent with the theoretical model which is considered to be caused by violations of the underlying model assumptions. Especially the effects of turbulence and pressure disturbances by the chamber deployment are suspected to have caused unexplainable curvatures. CO2 flux estimates by linear regression can be as low as 40% of the flux estimates of exponential regression for closure times of only two minutes. The degree of underestimation increased with increasing CO2 flux strength and was dependent on soil and vegetation conditions which can disturb not only the quantitative but also the qualitative evaluation of CO2 flux dynamics. The underestimation effect by linear regression was observed to be different for CO2 uptake and release situations which can lead to stronger bias in the daily, seasonal and annual CO2 balances than in the individual fluxes. To avoid serious bias of CO2 flux estimates based on closed chamber experiments, we suggest further tests using published datasets and recommend the use of nonlinear regression models for future closed chamber studies.

scholarly journals Hubungan Kecepatan Pendinginan Air dengan Kecepatan Tiupan Udara

2017 ◽  
Vol 2 ◽  
pp. 241
Author(s):  
Taat Guswantoro ◽  
Manogari Sianturi ◽  
Nurafni Prapitasari ◽  
Areli Elona
Keyword(s):  
Regression Analysis ◽  
Wind Speed ◽  
Linear Regression ◽  
Water Temperature ◽  
Rate Of Change ◽  
Hot Water ◽  
Water Cooling ◽  
Exponential Regression ◽  
The Relationship

<p class="AbstractEnglish"><strong>Abstract</strong>: In this study hot water was placed in two erlenmeyer scale 100 ml clogged and without plug, each filled with 150 ml hot water and allowed to cool in air. Measurement of water temperature using sensor connected to the interface and recorded using the pasco capstone 14.1. The wind is raised with the fan, to adjust the wind speed by adjusting the fan distance, the speed is measured using an anemometer. The water cooling constant is obtained by a decay exponential regression analysis of temperature vs time. The relationship between water colling coefficient with wind speed is used linear regression. From the research, the water cooling coefficient naturally for clogging erlenmeyer is 3,1 x 10<sup>-4</sup> s<sup>-1</sup> and for erlenmeyer without plug 3.8 x 10<sup>-4</sup> s<sup>-1</sup>, the rate of change of water cooling constant to wind speed is 1 , 4 x 10<sup>-4</sup> m<sup>-1</sup>.</p><p class="KeywordsEngish"> </p><p class="AbstrakIndonesia"><strong>Abstrak: </strong>Pada penelitian ini air panas ditempatkan dalam dua buah erlenmeyer berskala 100 ml bersumbat dan tanpa sumbat, masing-masing diisi air panas dengan volume 150 ml dan dibiarkan mendingin di udara. Pengukuran suhu air dengan menggunakan sensor panas yang dihubungkan ke interface dan dicatat menggunakan program pasco capstone 14.1. Angin dibangkitkan dengan kipas, untuk mengatur kecepatan angin dengan cara mengatur jarak kipas, kecepatan angin diukur menggunakan anemometer. Konstanta pendinginan air diperoleh dengan analisis regresi eksponensial meluruh dari data suhu dan waktu. Hubungan antara koefisien pendinginan air dengan kecepatan angin digunakan regresi linier. Dari penelitian diperoleh koefisien pendinginan air secara alami untuk erlenmeyer tersumbat sebesar 3,1 x 10<sup>-4</sup> s<sup>-1</sup> dan untuk erlenmeyer tanpa sumbat sebesar  3,8 x 10<sup>-4</sup> s<sup>-1</sup>, laju perubahan konstanta pendinginan air terhadap kecepatan angin adalah sebesar 1,4 x 10<sup>-4</sup> m<sup>-1</sup><sub>.</sub></p>

scholarly journals Central Jakarta Rainfall Intensity Forecast using Single Exponential Smoothing

2019 ◽  
Vol 8 (4) ◽  
pp. 2105-2108
Keyword(s):  
Rainfall Intensity ◽  
Forecast Error ◽  
Precipitation Amount ◽  
Exponential Smoothing ◽  
Vital Role ◽  
Capital City ◽  
Good Prediction ◽  
Intensity Prediction ◽  
Forecasting Method ◽  
Single Exponential

Rainfall is the precipitation amount that is falling from clouds. In extreme conditions, rainfall could arise many problems. It is the leading cause of landslides and flood disasters. In D.K.I. Jakarta, the capital city of Indonesia, rainfall intensity plays a very vital role since it could easily be puddled and caused floods in many areas. Therefore, in this study, we try to make a rainfall intensity prediction in Central Jakarta using a very popular forecasting method, i.e., the Single Exponential Smoothing (SES). Based on the experiments conducted using Phatsa, it can be concluded that the SES method has been successfully used to predict rainfall intensity. However, it cannot give a very good prediction result due to its high forecast error values.

Characterization of local regions for wavelet-based image denoising using a statistical approach

2020 ◽  
Vol 18 (03) ◽  
pp. 2050011
Author(s):  
Rajiv Verma ◽  
Rajoo Pandey
Keyword(s):  
Image Denoising ◽  
Statistical Approach ◽  
Wavelet Coefficient ◽  
Threshold Value ◽  
Vital Role ◽  
Wiener Filtering ◽  
Original Signal ◽  
Wavelet Coefficients ◽  
Local Window

The shape of local window plays a vital role in the estimation of original signal variance, which is used to shrink the noisy wavelet coefficients in wavelet-based image denoising algorithms. This paper presents an anisotropic-shaped region-based Wiener filtering (ASRWF) and BayesShrink (ASRBS) algorithms, which exploit the region characteristics to estimate the original signal variance using a statistical approach. The proposed approach divides the region centered on a noisy wavelet coefficient into various non-overlapping subregions. The Euclidean distance-based measure is considered to obtain the similarities between reference subregion and adjacent subregions. An appropriate threshold value is estimated by applying a statistical approach on these distances and the sets of similar and dissimilar subregions are obtained from a defined region. Thus, an anisotropic-shaped region is obtained by neglecting the dissimilar subregions in a defined region. The variance of every similar subregion is calculated and then averaged to estimate the original signal variance to shrink noisy wavelet coefficients effectively. Finally, the estimated signal variance is utilized in Wiener filtering and BayesShrink algorithms to improve the denoising performance. The performance of the proposed algorithms is analyzed qualitatively and quantitatively on standard images for different noise levels.

scholarly journals Enhancement of Visual Field Predictions with Pointwise Exponential Regression (PER) and Pointwise Linear Regression (PLR)

2016 ◽  
Vol 5 (2) ◽  
pp. 12 ◽  
Author(s):  
Esteban Morales ◽  
John Mark S. de Leon ◽  
Niloufar Abdollahi ◽  
Fei Yu ◽  
Kouros Nouri-Mahdavi ◽  
...  
Keyword(s):  
Visual Field ◽  
Linear Regression ◽  
Exponential Regression

Proton NMR Analysis of Octane Number for Motor Gasoline: Part IV

1992 ◽  
Vol 46 (8) ◽  
pp. 1294-1300 ◽  
Author(s):  
M. Ichikawa ◽  
N. Nonaka ◽  
H. Amano ◽  
I. Takada ◽  
S. Ishimori ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Aromatic Compounds ◽  
Octane Number ◽  
Proton Magnetic Resonance ◽  
Linear Regression Analysis ◽  
Regression Equations ◽  
Nmr Analysis ◽  
Motor Gasoline ◽  
The Relationship

Software (a program) for predicting the octane number of motor gasoline by proton magnetic resonance (PMR) spectrometry has been formulated. At the same time, a method has been studied to predict the composition of gasoline (in terms of the contents of paraffin, olefin, and aromatic compounds). The formulated program was evaluated by using it to predict the octane numbers of 31 samples of marketed summer gasoline (including 16 regular and 15 premium products), whose octane numbers and compositions were identified according to the ASTM standards. Also, the relationship between the PMR spectrum and gasoline composition was subjected to linear regression analysis by using the 31 samples whose octane numbers were calculated, and the appropriateness of the resultant regression equations was assessed. This report concerns the results of the study in which the octane numbers of the 31 samples were satisfactorily predicted by the formulated program and useful linear regression equations were obtained for the prediction of the composition of gasoline.

scholarly journals CH<sub>4</sub> exchange at the forest floor of a forestry-drained fen: low flux rates but high temporal variation

2016 ◽  
Author(s):  
Mika Korkiakoski ◽  
Markku Koskinen ◽  
Kari Minkkinen ◽  
Paavo Ojanen ◽  
Timo Penttilä ◽  
...  
Keyword(s):  
Stainless Steel ◽  
Linear Regression ◽  
Exchange Rates ◽  
Measurement System ◽  
Forest Floor ◽  
Steel Frames ◽  
Tree Stand ◽  
Ch4 Flux ◽  
Exponential Regression ◽  
Moss Layer

Abstract. We measured methane (CH4) exchange rates with automatic chambers at the forest floor of a nutrient-rich drained peatland in 2011–2013. The fen, located in southern Finland, was drained for forestry in the 1970s and the tree stand is now a mixture of Scots pine, Norway spruce and pubescent birch. Our measurement system consisted of six transparent polycarbonate chambers and stainless steel frames, positioned on a number of different field and moss layer types. Flux rates were calculated with both linear and exponential regression. The use of linear regression systematically underestimated CH4 flux rates by 20–50 % when compared to exponential regression. However, the use of exponential regression with small fluxes (

Theoretical Study on Adiabatic Electron Affinity of Fatty Acids

2021 ◽  
Author(s):  
Yaxin Yang ◽  
Wenrui Zheng ◽  
Hongyun Xie ◽  
Lufei Ren ◽  
Xiaofei Xu ◽  
...  
Keyword(s):  
Fatty Acids ◽  
Secondary Metabolites ◽  
Electron Affinity ◽  
Theoretical Study ◽  
Vital Role ◽  
Energy Sources ◽  
Signal Molecules ◽  
Human Metabolism ◽  
Adiabatic Electron Affinity

As nutrients, secondary metabolites, essential signal molecules and energy sources, fatty acids play a vital role in biomedicine, pharmacokinetics and human metabolism. The reduction of fatty acids is one of...

scholarly journals The use of Holt–Winters method for forecasting the amount of sewage inflowing into the wastewater treatment plant in Nowy Sącz

2016 ◽  
Vol 27 (2) ◽  
pp. 7-12
Author(s):  
Ewa Wąsik ◽  
Krzysztof Chmielowski
Keyword(s):  
Time Series ◽  
Wastewater Treatment ◽  
Linear Regression ◽  
Wastewater Treatment Plant ◽  
Treatment Plant ◽  
Exponential Smoothing ◽  
Model Fit ◽  
Daily Amount

Abstract The aim of the study was to determine changes of daily amount of sewage inflowing into a wastewater treatment plant in Nowy Sącz in the years 2008-2014. To this end, the data in the form of time series corresponding to the investigated multi-year period were analysed. Daily volume of sewage for annual periods was forecast using a seasonal method of Holt and Winters based on the exponential smoothing algorithms. The model fit to actual daily amount of sewage for 2014 was assessed using linear regression. The results of fit for the additive Holt-Winters model confirmed the usefulness of this tool for forecasting the amount of sewage inflowing into the wastewater treatment plant.

Sign in / Sign up

Export Citation Format

Share Document