Energy Production: A Comparison of Forecasting Methods using the Polynomial Curve Fitting and Linear Regression

Kafazi, Ismail El; Bannari, Rachid; Abouabdellah, Abdellah; Aboutafail, My Othman; Guerrero, Josep M.

doi:10.1109/irsec.2017.8477278

Cited by 21 publications

(12 citation statements)

References 5 publications

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“…Ismail El kafazi, [53] Proposed two ways to predict renewable energy. Wind and solar power integration and system improvement and availability assure continued output and ensure supply of necessary amount of energy.…”

Section: Iiia Review On (Linear Regression)mentioning

confidence: 99%

“…Color reading MLRM The fits of the regression model is better, the effects of the temperature and refractive index and concentration on the model are not taken into account. 82% [53], 2017…”

Section: Medical Data Polynomialmentioning

confidence: 99%

“…We may implement advanced machine learning algorithms, such as help vector machine and deep neural network, to solve problems. [53] Two methods were proposed for estimating energy output from renewables, using the R-square figure for the curve fitting of the polynomial method is considerably higher than the one of the linear regression method. However, the obtained regression equation will be used in the future to analyze and study the relationship and interactions between demand and energy production using machine learning in order to model electrical transport.…”

Section: 92%mentioning

confidence: 99%

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A Review on Linear Regression Comprehensive in Machine Learning

Maulud

Abdulazeez

2020

JASTT

562

145

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Perhaps one of the most common and comprehensive statistical and machine learning algorithms are linear regression. Linear regression is used to find a linear relationship between one or more predictors. The linear regression has two types: simple regression and multiple regression (MLR). This paper discusses various works by different researchers on linear regression and polynomial regression and compares their performance using the best approach to optimize prediction and precision. Almost all of the articles analyzed in this review is focused on datasets; in order to determine a model's efficiency, it must be correlated with the actual values obtained for the explanatory variables.

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Section: Iiia Review On (Linear Regression)mentioning

confidence: 99%

Section: Medical Data Polynomialmentioning

confidence: 99%

Section: 92%mentioning

confidence: 99%

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A Review on Linear Regression Comprehensive in Machine Learning

Maulud

Abdulazeez

2020

JASTT

562

145

View full text Add to dashboard Cite

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“…To select parameters potentially best suited to the parameters closely related to the hydrogen economy, a single-parameter regression model was built. A linear regression model has also been used many times in the energy area, for example, in [35,36,[80][81][82][83][84].…”

Section: The Linear Regression Modelsmentioning

confidence: 99%

The Impact of Economic, Energy, and Environmental Factors on the Development of the Hydrogen Economy

2021

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This article attempts to model interdependencies between socio-economic, energy, and environmental factors with selected data characterizing the development of the hydrogen economy. The study applies Spearman’s correlation and a linear regression model to estimate the influence of gross domestic product, population, final energy consumption, renewable energy, and CO2 emission on chosen hydrogen indicators—production, patents, energy technology research, development, and demonstration budgets. The study was conducted in nine countries selected for their actions towards a hydrogen economy based on analyses of national strategies, policies, research and development programs, and roadmaps. The results confirm the statistically significant impact of the chosen indicators, which are the drivers for the development of the hydrogen economy from 2008 to 2018. Moreover, the empirical results show that different characteristics in each country contribute to the development of the hydrogen economy vision.

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“…The use of a curve-fitting technique is a widely accepted estimation approach where a curve that fits most of the samples is employed for out-sample predictions. In this context, Kafazi et al [16] used the curve-fitting technique for energy forecasting, whereas Donmez et al [17] used a similar approach to forecast the electricity demand. In another work, Srikanth et al [18] compared the performance of polynomial curve-fitting, ARIMA, and ANFIS methods and concluded that the polynomial curve outperformed the other two methods.…”

Section: Curve Fittingmentioning

confidence: 99%