Effect Verification of Training Period for Prediction of Photovoltaic Power Generation using ML

Haruto Furusawa; Yuukou Horita

doi:10.1109/GCCE59613.2023.10315639

Effect Verification of Training Period for Prediction of Photovoltaic Power Generation using ML

Haruto Furusawa, Yuukou Horita^*

^*Corresponding author for this work

Civil Design and Engineering

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

Abstract

Among renewable energies, photovoltaic power generation, which can be introduced relatively easily in buildings and houses, is being used, and its further introduction is desired. Therefore, there is a need for technology to accurately predict the amount of electricity generated at potential sites for photovoltaic power generation facilities. In this study, we tried various machine learning methods for predicting the amount of electricity generated by photovoltaic power generation without using the information of the solar radiation meters, and examined the effect of the training period of machine learning on the accuracy of the prediction.

Original language	English
Title of host publication	GCCE 2023 - 2023 IEEE 12th Global Conference on Consumer Electronics
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	293-295
Number of pages	3
ISBN (Electronic)	9798350340181
DOIs	https://doi.org/10.1109/GCCE59613.2023.10315639
State	Published - 2023
Event	12th IEEE Global Conference on Consumer Electronics, GCCE 2023 - Nara, Japan Duration: 2023/10/10 → 2023/10/13

Publication series

Name	GCCE 2023 - 2023 IEEE 12th Global Conference on Consumer Electronics

Conference

Conference	12th IEEE Global Conference on Consumer Electronics, GCCE 2023
Country/Territory	Japan
City	Nara
Period	2023/10/10 → 2023/10/13

Keywords

Accuracy Verification
Machine learning
Photovoltaic power generation

ASJC Scopus subject areas

Artificial Intelligence
Energy Engineering and Power Technology
Electrical and Electronic Engineering
Safety, Risk, Reliability and Quality
Instrumentation
Atomic and Molecular Physics, and Optics

UN SDGs

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

Access to Document

10.1109/GCCE59613.2023.10315639

Cite this

Furusawa, H., & Horita, Y. (2023). Effect Verification of Training Period for Prediction of Photovoltaic Power Generation using ML. In GCCE 2023 - 2023 IEEE 12th Global Conference on Consumer Electronics (pp. 293-295). (GCCE 2023 - 2023 IEEE 12th Global Conference on Consumer Electronics). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GCCE59613.2023.10315639

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title = "Effect Verification of Training Period for Prediction of Photovoltaic Power Generation using ML",

abstract = "Among renewable energies, photovoltaic power generation, which can be introduced relatively easily in buildings and houses, is being used, and its further introduction is desired. Therefore, there is a need for technology to accurately predict the amount of electricity generated at potential sites for photovoltaic power generation facilities. In this study, we tried various machine learning methods for predicting the amount of electricity generated by photovoltaic power generation without using the information of the solar radiation meters, and examined the effect of the training period of machine learning on the accuracy of the prediction.",

keywords = "Accuracy Verification, Machine learning, Photovoltaic power generation",

author = "Haruto Furusawa and Yuukou Horita",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 12th IEEE Global Conference on Consumer Electronics, GCCE 2023 ; Conference date: 10-10-2023 Through 13-10-2023",

year = "2023",

doi = "10.1109/GCCE59613.2023.10315639",

language = "英語",

series = "GCCE 2023 - 2023 IEEE 12th Global Conference on Consumer Electronics",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "293--295",

booktitle = "GCCE 2023 - 2023 IEEE 12th Global Conference on Consumer Electronics",

}

Furusawa, H & Horita, Y 2023, Effect Verification of Training Period for Prediction of Photovoltaic Power Generation using ML. in GCCE 2023 - 2023 IEEE 12th Global Conference on Consumer Electronics. GCCE 2023 - 2023 IEEE 12th Global Conference on Consumer Electronics, Institute of Electrical and Electronics Engineers Inc., pp. 293-295, 12th IEEE Global Conference on Consumer Electronics, GCCE 2023, Nara, Japan, 2023/10/10. https://doi.org/10.1109/GCCE59613.2023.10315639

Effect Verification of Training Period for Prediction of Photovoltaic Power Generation using ML. / Furusawa, Haruto; Horita, Yuukou.
GCCE 2023 - 2023 IEEE 12th Global Conference on Consumer Electronics. Institute of Electrical and Electronics Engineers Inc., 2023. p. 293-295 (GCCE 2023 - 2023 IEEE 12th Global Conference on Consumer Electronics).

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

TY - GEN

T1 - Effect Verification of Training Period for Prediction of Photovoltaic Power Generation using ML

AU - Furusawa, Haruto

AU - Horita, Yuukou

PY - 2023

Y1 - 2023

N2 - Among renewable energies, photovoltaic power generation, which can be introduced relatively easily in buildings and houses, is being used, and its further introduction is desired. Therefore, there is a need for technology to accurately predict the amount of electricity generated at potential sites for photovoltaic power generation facilities. In this study, we tried various machine learning methods for predicting the amount of electricity generated by photovoltaic power generation without using the information of the solar radiation meters, and examined the effect of the training period of machine learning on the accuracy of the prediction.

AB - Among renewable energies, photovoltaic power generation, which can be introduced relatively easily in buildings and houses, is being used, and its further introduction is desired. Therefore, there is a need for technology to accurately predict the amount of electricity generated at potential sites for photovoltaic power generation facilities. In this study, we tried various machine learning methods for predicting the amount of electricity generated by photovoltaic power generation without using the information of the solar radiation meters, and examined the effect of the training period of machine learning on the accuracy of the prediction.

KW - Accuracy Verification

KW - Machine learning

KW - Photovoltaic power generation

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U2 - 10.1109/GCCE59613.2023.10315639

DO - 10.1109/GCCE59613.2023.10315639

M3 - 会議への寄与

AN - SCOPUS:85179757446

T3 - GCCE 2023 - 2023 IEEE 12th Global Conference on Consumer Electronics

SP - 293

EP - 295

BT - GCCE 2023 - 2023 IEEE 12th Global Conference on Consumer Electronics

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 12th IEEE Global Conference on Consumer Electronics, GCCE 2023

Y2 - 10 October 2023 through 13 October 2023

ER -

Furusawa H, Horita Y. Effect Verification of Training Period for Prediction of Photovoltaic Power Generation using ML. In GCCE 2023 - 2023 IEEE 12th Global Conference on Consumer Electronics. Institute of Electrical and Electronics Engineers Inc. 2023. p. 293-295. (GCCE 2023 - 2023 IEEE 12th Global Conference on Consumer Electronics). doi: 10.1109/GCCE59613.2023.10315639

Effect Verification of Training Period for Prediction of Photovoltaic Power Generation using ML

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

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Cite this