TY - GEN
T1 - Daily Peak Load Demand Forecast Considering Weather Conditions
AU - Sasaki, Hideaki
AU - Urano, Shoichi
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The authors have proposed a prediction method that combines multiple regression analysis, which is a statistical method, and random forest, which is a machine learning method. The proposed method has applied to the prediction of daily peak load demand. In this paper, we compare the meteorological data using not only the past temperature, humidity, and solar radiation but also wind direction/volume, weather, etc
AB - The authors have proposed a prediction method that combines multiple regression analysis, which is a statistical method, and random forest, which is a machine learning method. The proposed method has applied to the prediction of daily peak load demand. In this paper, we compare the meteorological data using not only the past temperature, humidity, and solar radiation but also wind direction/volume, weather, etc
KW - load demand forecast
KW - Multiple regression model
KW - Random forest
UR - http://www.scopus.com/inward/record.url?scp=85127972735&partnerID=8YFLogxK
U2 - 10.1109/CPEEE54404.2022.9738671
DO - 10.1109/CPEEE54404.2022.9738671
M3 - Conference contribution
AN - SCOPUS:85127972735
T3 - Proceedings of 2022 12th International Conference on Power, Energy and Electrical Engineering, CPEEE 2022
SP - 195
EP - 200
BT - Proceedings of 2022 12th International Conference on Power, Energy and Electrical Engineering, CPEEE 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th International Conference on Power, Energy and Electrical Engineering, CPEEE 2022
Y2 - 25 February 2022 through 27 February 2022
ER -