TY - GEN
T1 - Photovoltaic Power Forecasting Using Cascade Forward Neural Network Based on Levenberg-Marquardt Algorithm
AU - Mahmudah, Norma
AU - Priyadi, Ardyono
AU - Setya Budi, Avian Lukman
AU - Budiharto Putri, Vita Lystianingrum
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/3/8
Y1 - 2021/3/8
N2 - Renewable energy is useful energy collected from renewable resources, and renewable resources are naturally replenished within human time. Photovoltaic (PV) is a generator that converts solar energy into electrical energy. Depending on the weather, photovoltaic output is an occasional output. Therefore, this study will use the Cascade Forward Neural Network (CFNN) method with the Levenberg-Marquardt algorithm to predict photovoltaic power plants one day in advance. The measure of accuracy error from the simulation result in this study is calculated using Mean Square Error (MSE). From the simulation results, it is obtained that The Cascade Forward Neural Network (CFNN) method with the Levenberg-Marquardt could give the better MSE at the learning rate of 0.1 by mean MSE of 0,308% while the learning rate of 0.05 by mean MSE of 0,326% and learning rate of 0.01 by mean MSE of 0,322%. It is also obtained that Cascade Forward Neural Network (CFNN) method also eligible for solving photovoltaic power forecasting problem due to its accuracy and should be eligible for another renewable energy electricity source power forecasting.
AB - Renewable energy is useful energy collected from renewable resources, and renewable resources are naturally replenished within human time. Photovoltaic (PV) is a generator that converts solar energy into electrical energy. Depending on the weather, photovoltaic output is an occasional output. Therefore, this study will use the Cascade Forward Neural Network (CFNN) method with the Levenberg-Marquardt algorithm to predict photovoltaic power plants one day in advance. The measure of accuracy error from the simulation result in this study is calculated using Mean Square Error (MSE). From the simulation results, it is obtained that The Cascade Forward Neural Network (CFNN) method with the Levenberg-Marquardt could give the better MSE at the learning rate of 0.1 by mean MSE of 0,308% while the learning rate of 0.05 by mean MSE of 0,326% and learning rate of 0.01 by mean MSE of 0,322%. It is also obtained that Cascade Forward Neural Network (CFNN) method also eligible for solving photovoltaic power forecasting problem due to its accuracy and should be eligible for another renewable energy electricity source power forecasting.
KW - Cascade Forward Neural Network
KW - Levenberg-Marquardt Algorithm
KW - Mean Square Error
KW - Photovoltaic
KW - Power Forecasting
UR - http://www.scopus.com/inward/record.url?scp=85106396781&partnerID=8YFLogxK
U2 - 10.1109/ICPEA51500.2021.9417842
DO - 10.1109/ICPEA51500.2021.9417842
M3 - Conference contribution
AN - SCOPUS:85106396781
T3 - ICPEA 2021 - 2021 IEEE International Conference in Power Engineering Application
SP - 115
EP - 120
BT - ICPEA 2021 - 2021 IEEE International Conference in Power Engineering Application
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference in Power Engineering Application, ICPEA 2021
Y2 - 8 March 2021 through 9 March 2021
ER -