Solar radiation prediction based on machine learning for istanbul in Turkey

Veysel Çoban; Sezi Çevik Onar

doi:10.1007/978-3-030-23756-1_25

Solar radiation prediction based on machine learning for istanbul in Turkey

Veysel Çoban^*, Sezi Çevik Onar

^*Corresponding author for this work

Department of Industrial Engineering

Bozok University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

Abstract

The correct installation of solar energy systems is important for the energy efficiency of the system. The total solar radiation values reaching the system have an important role in determining the energy production potential of the solar energy system. In this study, statistical and machine learning methods used in solar radiation estimation are discussed. Forecasting methods are evaluated with the application on Istanbul region. The variability of the data collected for the Istanbul region is examined and the inappropriate data in the data are extracted. The data that are checked and approved are applied to the forecasting models and the models are compared and evaluated according to their error values. Models are evaluated according to variability values and error values over temporal horizons. Variability has an important role in determining the most appropriate forecasting model.

Original language	English
Title of host publication	Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference
Editors	Cengiz Kahraman, Sezi Cevik Onar, Basar Oztaysi, Irem Ucal Sari, Selcuk Cebi, A.Cagri Tolga
Publisher	Springer Verlag
Pages	197-204
Number of pages	8
ISBN (Print)	9783030237554
DOIs	https://doi.org/10.1007/978-3-030-23756-1_25
Publication status	Published - 2020
Event	International Conference on Intelligent and Fuzzy Systems, INFUS 2019 - Istanbul, Turkey Duration: 23 Jul 2019 → 25 Jul 2019

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	1029
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	International Conference on Intelligent and Fuzzy Systems, INFUS 2019
Country/Territory	Turkey
City	Istanbul
Period	23/07/19 → 25/07/19

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Keywords

Forecasting methods
Machine learning
Solar irradiation

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/978-3-030-23756-1_25

Cite this

Çoban, V., & Onar, S. Ç. (2020). Solar radiation prediction based on machine learning for istanbul in Turkey. In C. Kahraman, S. Cevik Onar, B. Oztaysi, I. U. Sari, S. Cebi, & A. C. Tolga (Eds.), Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference (pp. 197-204). (Advances in Intelligent Systems and Computing; Vol. 1029). Springer Verlag. https://doi.org/10.1007/978-3-030-23756-1_25

Çoban, Veysel ; Onar, Sezi Çevik. / Solar radiation prediction based on machine learning for istanbul in Turkey. Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference. editor / Cengiz Kahraman ; Sezi Cevik Onar ; Basar Oztaysi ; Irem Ucal Sari ; Selcuk Cebi ; A.Cagri Tolga. Springer Verlag, 2020. pp. 197-204 (Advances in Intelligent Systems and Computing).

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title = "Solar radiation prediction based on machine learning for istanbul in Turkey",

abstract = "The correct installation of solar energy systems is important for the energy efficiency of the system. The total solar radiation values reaching the system have an important role in determining the energy production potential of the solar energy system. In this study, statistical and machine learning methods used in solar radiation estimation are discussed. Forecasting methods are evaluated with the application on Istanbul region. The variability of the data collected for the Istanbul region is examined and the inappropriate data in the data are extracted. The data that are checked and approved are applied to the forecasting models and the models are compared and evaluated according to their error values. Models are evaluated according to variability values and error values over temporal horizons. Variability has an important role in determining the most appropriate forecasting model.",

keywords = "Forecasting methods, Machine learning, Solar irradiation",

author = "Veysel {\c C}oban and Onar, {Sezi {\c C}evik}",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; International Conference on Intelligent and Fuzzy Systems, INFUS 2019 ; Conference date: 23-07-2019 Through 25-07-2019",

year = "2020",

doi = "10.1007/978-3-030-23756-1_25",

language = "English",

isbn = "9783030237554",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer Verlag",

pages = "197--204",

editor = "Cengiz Kahraman and {Cevik Onar}, Sezi and Basar Oztaysi and Sari, {Irem Ucal} and Selcuk Cebi and A.Cagri Tolga",

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Çoban, V & Onar, SÇ 2020, Solar radiation prediction based on machine learning for istanbul in Turkey. in C Kahraman, S Cevik Onar, B Oztaysi, IU Sari, S Cebi & AC Tolga (eds), Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference. Advances in Intelligent Systems and Computing, vol. 1029, Springer Verlag, pp. 197-204, International Conference on Intelligent and Fuzzy Systems, INFUS 2019, Istanbul, Turkey, 23/07/19. https://doi.org/10.1007/978-3-030-23756-1_25

Solar radiation prediction based on machine learning for istanbul in Turkey. / Çoban, Veysel; Onar, Sezi Çevik.
Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference. ed. / Cengiz Kahraman; Sezi Cevik Onar; Basar Oztaysi; Irem Ucal Sari; Selcuk Cebi; A.Cagri Tolga. Springer Verlag, 2020. p. 197-204 (Advances in Intelligent Systems and Computing; Vol. 1029).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Solar radiation prediction based on machine learning for istanbul in Turkey

AU - Çoban, Veysel

AU - Onar, Sezi Çevik

PY - 2020

Y1 - 2020

N2 - The correct installation of solar energy systems is important for the energy efficiency of the system. The total solar radiation values reaching the system have an important role in determining the energy production potential of the solar energy system. In this study, statistical and machine learning methods used in solar radiation estimation are discussed. Forecasting methods are evaluated with the application on Istanbul region. The variability of the data collected for the Istanbul region is examined and the inappropriate data in the data are extracted. The data that are checked and approved are applied to the forecasting models and the models are compared and evaluated according to their error values. Models are evaluated according to variability values and error values over temporal horizons. Variability has an important role in determining the most appropriate forecasting model.

AB - The correct installation of solar energy systems is important for the energy efficiency of the system. The total solar radiation values reaching the system have an important role in determining the energy production potential of the solar energy system. In this study, statistical and machine learning methods used in solar radiation estimation are discussed. Forecasting methods are evaluated with the application on Istanbul region. The variability of the data collected for the Istanbul region is examined and the inappropriate data in the data are extracted. The data that are checked and approved are applied to the forecasting models and the models are compared and evaluated according to their error values. Models are evaluated according to variability values and error values over temporal horizons. Variability has an important role in determining the most appropriate forecasting model.

KW - Forecasting methods

KW - Machine learning

KW - Solar irradiation

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DO - 10.1007/978-3-030-23756-1_25

M3 - Conference contribution

AN - SCOPUS:85069431909

SN - 9783030237554

T3 - Advances in Intelligent Systems and Computing

SP - 197

EP - 204

BT - Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference

A2 - Kahraman, Cengiz

A2 - Cevik Onar, Sezi

A2 - Oztaysi, Basar

A2 - Sari, Irem Ucal

A2 - Cebi, Selcuk

A2 - Tolga, A.Cagri

PB - Springer Verlag

T2 - International Conference on Intelligent and Fuzzy Systems, INFUS 2019

Y2 - 23 July 2019 through 25 July 2019

ER -

Çoban V, Onar SÇ. Solar radiation prediction based on machine learning for istanbul in Turkey. In Kahraman C, Cevik Onar S, Oztaysi B, Sari IU, Cebi S, Tolga AC, editors, Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference. Springer Verlag. 2020. p. 197-204. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-23756-1_25

Solar radiation prediction based on machine learning for istanbul in Turkey

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

Access to Document

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