Global Solar Radiation Estimation and Climatic Variability Analysis Using Extreme Learning Machine Based Predictive Model
Document identifier: oai:DiVA.org:ltu-77489
Access full text here:
10.1109/ACCESS.2020.2965303Keyword: Engineering and Technology,
Civil Engineering,
Geotechnical Engineering,
Teknik och teknologier,
Samhällsbyggnadsteknik,
Geoteknik,
Energy feasibility studies,
Extreme learning machine,
Solar energy estimation,
Multivariate,
Soil MechanicsPublication year: 2020Relevant Sustainable Development Goals (SDGs):
The SDG label(s) above have been assigned by OSDG.aiAbstract: Sustainable utilization of the freely available solar radiation as renewable energy source requires accurate predictive models to quantitatively evaluate future energy potentials. In this research, an evaluation of the preciseness of extreme learning machine (ELM) model as a fast and efficient framework for estimating global incident solar radiation (G) is undertaken. Daily meteorological datasets suitable for G estimation belongs to the northern parts of the Cheliff Basin in Northwest Algeria, is used to construct the estimation model. Cross-correlation functions are applied between the inputs and the target variable (i.e., G) where several climatological information’s are used as the predictors for surface level G estimation. The most significant model inputs are determined in accordance with highest cross-correlations considering the covariance of the predictors with the G dataset. Subsequently, seven ELM models with unique neuronal architectures in terms of their input-hidden-output neurons are developed with appropriate input combinations. The prescribed ELM model’s estimation performance over the testing phase is evaluated against multiple linear regressions (MLR), autoregressive integrated moving average (ARIMA) models and several well-established literature studies. This is done in accordance with several statistical score metrics. In quantitative terms, the root mean square error (RMSE) and mean absolute error (MAE) are dramatically lower for the optimal ELM model with RMSE and MAE = 3.28 and 2.32 Wm −2 compared to 4.24 and 3.24 Wm −2 (MLR) and 8.33 and 5.37 Wm −2 (ARIMA).
Authors
Tao Hai
Computer Science Department, Baoji University of Arts and Sciences, Baoji, China
Other publications
>>
Ahmad Sharafati
Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
Other publications
>>
Achite Mohammed
Faculty of Nature and Life Sciences, Laboratory of Water and Environment, University Hassiba Benbouali Chlef, Hay Es-Salem Chlef, Algeria
Other publications
>>
Sinan Q. Salih
Institute of Research and Development, Duy Tan University, Da Nang, Vietnam
Other publications
>>
Ravinesh C. Deo
School of Agricultural, Computational and Environmental Sciences, Centre for Applied Climate Sciences, Institute of Life Sciences and Environment, University of Southern Queensland, Springfield, QLD, Australia
Other publications
>>
Nadhir Al-Ansari
Luleå tekniska universitet; Geoteknologi
Other publications
>>
Zaher Mundher Yaseen
Sustainable Developments in Civil Engineering Research Group, Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam
Other publications
>>
Documents attached
|
Click on thumbnail to read
|
Record metadata
Click to view metadata
header:
identifier: oai:DiVA.org:ltu-77489
datestamp: 2021-04-19T12:50:17Z
setSpec: SwePub-ltu
metadata:
mods:
@attributes:
version: 3.7
recordInfo:
recordContentSource: ltu
recordCreationDate: 2020-01-23
identifier:
http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-77489
10.1109/ACCESS.2020.2965303
2-s2.0-85078811182
titleInfo:
@attributes:
lang: eng
title: Global Solar Radiation Estimation and Climatic Variability Analysis Using Extreme Learning Machine Based Predictive Model
abstract: Sustainable utilization of the freely available solar radiation as renewable energy source requires accurate predictive models to quantitatively evaluate future energy potentials. In this research an evaluation of the preciseness of extreme learning machine (ELM) model as a fast and efficient framework for estimating global incident solar radiation (G) is undertaken. Daily meteorological datasets suitable for G estimation belongs to the northern parts of the Cheliff Basin in Northwest Algeria is used to construct the estimation model. Cross-correlation functions are applied between the inputs and the target variable (i.e. G) where several climatological information’s are used as the predictors for surface level G estimation. The most significant model inputs are determined in accordance with highest cross-correlations considering the covariance of the predictors with the G dataset. Subsequently seven ELM models with unique neuronal architectures in terms of their input-hidden-output neurons are developed with appropriate input combinations. The prescribed ELM model’s estimation performance over the testing phase is evaluated against multiple linear regressions (MLR) autoregressive integrated moving average (ARIMA) models and several well-established literature studies. This is done in accordance with several statistical score metrics. In quantitative terms the root mean square error (RMSE) and mean absolute error (MAE) are dramatically lower for the optimal ELM model with RMSE and MAE = 3.28 and 2.32 Wm −2 compared to 4.24 and 3.24 Wm −2 (MLR) and 8.33 and 5.37 Wm −2 (ARIMA).
subject:
@attributes:
lang: eng
authority: uka.se
topic:
Engineering and Technology
Civil Engineering
Geotechnical Engineering
@attributes:
lang: swe
authority: uka.se
topic:
Teknik och teknologier
Samhällsbyggnadsteknik
Geoteknik
@attributes:
lang: eng
topic: Energy feasibility studies
@attributes:
lang: eng
topic: extreme learning machine
@attributes:
lang: eng
topic: solar energy estimation
@attributes:
lang: eng
topic: multivariate
@attributes:
lang: swe
authority: ltu
topic: Geoteknik
genre: Research subject
@attributes:
lang: eng
authority: ltu
topic: Soil Mechanics
genre: Research subject
language:
languageTerm: eng
genre:
publication/journal-article
ref
note:
Published
7
Validerad;2020;Nivå 2;2020-01-23 (johcin)
name:
@attributes:
type: personal
namePart:
Hai
Tao
role:
roleTerm: aut
affiliation: Computer Science Department Baoji University of Arts and Sciences Baoji China
@attributes:
type: personal
namePart:
Sharafati
Ahmad
role:
roleTerm: aut
affiliation: Department of Civil Engineering Science and Research Branch Islamic Azad University Tehran Iran
@attributes:
type: personal
namePart:
Mohammed
Achite
role:
roleTerm: aut
affiliation: Faculty of Nature and Life Sciences Laboratory of Water and Environment University Hassiba Benbouali Chlef Hay Es-Salem Chlef Algeria
@attributes:
type: personal
namePart:
Salih
Sinan Q.
role:
roleTerm: aut
affiliation: Institute of Research and Development Duy Tan University Da Nang Vietnam
@attributes:
type: personal
namePart:
Deo
Ravinesh C.
role:
roleTerm: aut
affiliation: School of Agricultural Computational and Environmental Sciences Centre for Applied Climate Sciences Institute of Life Sciences and Environment University of Southern Queensland Springfield QLD Australia
@attributes:
type: personal
authority: ltu
namePart:
Al-Ansari
Nadhir
1947-
role:
roleTerm: aut
affiliation:
Luleå tekniska universitet
Geoteknologi
nameIdentifier:
nadhir
0000-0002-6790-2653
@attributes:
type: personal
namePart:
Yaseen
Zaher Mundher
role:
roleTerm: aut
affiliation: Sustainable Developments in Civil Engineering Research Group Faculty of Civil Engineering Ton Duc Thang University Ho Chi Minh City Vietnam
originInfo:
dateIssued: 2020
publisher: IEEE
place:
placeTerm: USA
relatedItem:
@attributes:
type: host
titleInfo:
title: IEEE Access
identifier: 2169-3536
part:
detail:
@attributes:
type: volume
number: 8
extent:
start: 12026
end: 12042
location:
url: http://ltu.diva-portal.org/smash/get/diva2:1387928/FULLTEXT01.pdf
accessCondition: gratis
physicalDescription:
form: electronic
typeOfResource: text