Linear Regression Model in Estimating Solar Radiation in Perlis

Ibrahim, Siti Zuraidah; Ismail, Daut; Irwan, Y.M.; Irwanto, M.; Gomesh, N.; Farhana, Z.

doi:10.1016/j.egypro.2012.05.156

Cited by 65 publications

(37 citation statements)

References 7 publications

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“…One of the assumptions of analysis of variance is that error term is constant. This means, in the residual versus fitted plot, the residual errors, e i , as defined in Equation , are expected to have constant variance when scattered randomly around zero. A non‐constant error or funnel‐like plot would mean the hypothesis in the prediction is incorrect.…”

Section: Resultsmentioning

confidence: 99%

“…The estimated R fluctuates between −1 and +1, showing a substantial correlation between the response parameters and predictors. If R = 0, it can be said that there is no existence of any kind of correlation relationship between the two variables .

R = \frac{\sum_{i = 1}^{n} X_{i} Y_{i} - n \overset{X}{true} \overset{Y}{true}}{\sum_{i = 1}^{n} ((), X_{i}^{2} - n {\overset{false¯}{X}}^{2}) ((), Y_{i}^{2} - n {\overset{false¯}{Y}}^{2})}

where, X and Y are random variables, n represents the number of variables,

\overset{X}{true}

and

\overset{Y}{true}

are arithmetic means of the variables. The multiple regression model is given by Equation .

y = β_{0} + β_{1} X_{1} + β_{2} X_{2} + β_{3} X_{3} + \dots + β_{n} X_{n} + ɛ

where, β 0 is the intercept and ∈ is the error value.…”

Section: Methodsmentioning

confidence: 99%

Section: Mathematical Backgroundmentioning

confidence: 99%

See 2 more Smart Citations

Predictive analysis of photovoltaic plants specific yield with the implementation of multiple linear regression tool

Babatunde

Abbasoğlu

2018

Env Prog and Sustain Energy

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The objective of this study is to develop a model that can predict the specific yield of PV plant and statistically examine the significance of independent variables during the process. The methods presented will help investors make quick and reliable decisions about installing PV plant at a particular site. To determine the best‐fit model for energy prediction, data from all variables were collected and a relevance check was conducted by examining the relationship between independent variables and dependent variable. The forecasting method makes use of Multiple Linear Regression model. Seven models were developed and two models with the best accuracy are proposed in this article. Variance Inflation Factor, which assesses potential multicollinearity among independent variables, was used to optimize the models that have similar characteristics. The first proposed model has coefficient of multiple determination of goodness of fit (R2) of 96.8%. The second model has R2 of 96%. The models were applied to data series selected from same site; prediction accuracies of 97% and 95% were obtained, respectively. They were also validated with Middle East Technical University North Cyprus Campus PV plant, yielding prediction accuracies of PV specific yield of 92% and 90%, respectively. It could be concluded that the results presented in this study will guide the energy experts about the reliability of the methods used to analyze the technical feasibility of medium or large scale PV projects. © 2018 American Institute of Chemical Engineers Environ Prog, 38:e13098, 2019

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Section: Resultsmentioning

confidence: 99%

R = \frac{\sum_{i = 1}^{n} X_{i} Y_{i} - n \overset{X}{true} \overset{Y}{true}}{\sum_{i = 1}^{n} ((), X_{i}^{2} - n {\overset{false¯}{X}}^{2}) ((), Y_{i}^{2} - n {\overset{false¯}{Y}}^{2})}

where, X and Y are random variables, n represents the number of variables,

\overset{X}{true}

and

\overset{Y}{true}

are arithmetic means of the variables. The multiple regression model is given by Equation .

y = β_{0} + β_{1} X_{1} + β_{2} X_{2} + β_{3} X_{3} + \dots + β_{n} X_{n} + ɛ

where, β 0 is the intercept and ∈ is the error value.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Predictive analysis of photovoltaic plants specific yield with the implementation of multiple linear regression tool

Babatunde

Abbasoğlu

2018

Env Prog and Sustain Energy

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“…Go green is an activity that needs to be done conceptually in handling environmental crises. In a broad sense go green is all effort to restore, maintain and improve the condition of land in order to produce and function optimally, either as water regulator or environmental protection [17]. Nazarudin (1994) in Pramono [18], states go green is the effort of environmental arrangement by using the plant as its main material, so that from the plant can be taken many benefits.…”

Section: Go Greenmentioning

confidence: 99%

Mutual Cooperation Based Go Green: New Concept of Defense Country

Dewantara¹,

Budimasyah²

2018

Proceedings of the Annual Civic Education Conference (ACEC 2018)

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Growing awareness of state defending in the field of environment is still a challenge for the Indonesian nation. The nation of Indonesia is currently experiencing a deficit of social capital and low implementation of community civilization values. This study aims to analyze and prove that the practice of mutual cooperation go green based community in Glintung village as the initial pioneer of the formation of state defending by promoting the development of village in protecting the environment sector. This qualitative research uses a grounded theory approach. Data were collected by observation techniques, interviews and documentation studies. The results showed that mutual cooperation based on go green in Glintung village which has a state defending contents resulted in the development of green infrastructure, water saving infrastructure, waste management infrastructure and the emergence of urban farming movement. The practical implications of mutual cooperation based on go green succeeded in building a sense of nationality of citizenship through the active participation of the community in every work activity of the village development, besides that mutual cooperation based go green can foster the responsibility of society as citizens who uphold the value of nationalism.

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“…Due to the overcast climate of Ireland, the total numbers of the days with high output and close to the maximum point are quite low (especially from November-March). Previously statistical tools were adopted (Arthur, et al, 1988;Lee, et al, 2012;Ibrahim, et al, 2012), such as curve fitting, regression model and probability density distributions, for analyzing the solar radiation distribution. The approach adopted in this study was essentially based on investigating the DC generation and determining the distribution of specified generation categories in MATLAB.…”

Section: Bess With DC Generationmentioning

confidence: 99%

The Battery Energy Storage System (bess) Design Option for On-Campus Photovoltaic Charging Station (PV-CS)

Esfandyari¹,

Norton²,

Conlon³

et al. 2016

Proceedings of the ISES Solar World Congress 2015

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As the application of Light weight Electric Vehicles (LEVs) increase in communities, Dublin Institute of Technology (DIT) uses these small vehicles for short distance journeys around its 78 acre campus of "Grangegorman" located in inner Dublin city, Ireland. This paper presents an introduction to the campus Photovoltaic Charging Station (PV-CS) that generates clean electricity from the sun and charges the LEVs batteries which can lead to reduction of CO2 emissions, along with fulfillment of national and international green sustainability targets. Based on the evaluation of possible options for PV-CS design, the optimal design configuration was chosen as a "Battery Energy Storage System (BESS)". The PV generated electricity that is stored in battery banks will serve as the primary source for charging the campus vehicles, with any surplus demand being met by the utility grid. Batteries have been included in the design due to intermittent nature of Irish sunshine and the charge time requirements of campus load. This paper concentrates on the detailed sizing of the BESS via two approaches: BESS with DC generation and BESS with LEV load demand. In the first approach the outputs were normalized and grouped into specified generation categories in MATLAB. To establish the accurate BESS capacity the load demand variations of the LEVs were monitored. Based on DC generation and demand profile, the optimal capacity of BESS was chosen to be in the range of 6-8 kWh, which can accommodate up to 6 LEVs.

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Linear Regression Model in Estimating Solar Radiation in Perlis

Cited by 65 publications

References 7 publications

Predictive analysis of photovoltaic plants specific yield with the implementation of multiple linear regression tool

Predictive analysis of photovoltaic plants specific yield with the implementation of multiple linear regression tool

Mutual Cooperation Based Go Green: New Concept of Defense Country

The Battery Energy Storage System (bess) Design Option for On-Campus Photovoltaic Charging Station (PV-CS)

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