TY - GEN
T1 - Effect Verification of Training Period for Prediction of Photovoltaic Power Generation using ML
AU - Furusawa, Haruto
AU - Horita, Yuukou
N1 - Publisher Copyright:
© 2023 IEEE.
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
UR - http://www.scopus.com/inward/record.url?scp=85179757446&partnerID=8YFLogxK
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 -
