@inproceedings{eac17497cd214e48bae6e66a33e9db21,
title = "PV Power Forecast Based on Artificial Neural Network at Indonesia Shopping Mall PV Rooftop",
abstract = "In this research, forecast of Photovoltaic (PV) rooftop power output has been carried out. Forecast is using machine learning Artificial Neural Network (ANN). ANN is precisely used for forecast by memorizing and learning data patterns that are used as training and targets. The input training data used are irradiance, PV temperature and time per 10 minutes and time per 1 hour for 6 days. While the target is the PV output power data for 6 days. The training algoritm that used is Levenberg-Marquardt (LM) Algorithm. LM trains the network faster and has high rate of convergence. The performance parameter used to determine a good ANN forecast is the Mean Square Error (MSE). After conducting the training data, the data is tested for ANN forecast using input data on the seventh days. The output is the forecasted PV output power. The forecast results will be compared with the PV output power measurement data.",
keywords = "ANN, Forecasting, Levenberg-Marquardt, MSE, PV",
author = "Karimatun Nisa' and Luki Mahendra and Hodi Eko and Farid, {Imam Wahyudi}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2021 ; Conference date: 08-12-2021 Through 09-12-2021",
year = "2021",
doi = "10.1109/ICAMIMIA54022.2021.9807694",
language = "English",
series = "2021 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2021 - Proceeding",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "335--338",
booktitle = "2021 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2021 - Proceeding",
address = "United States",
}
