@article{ART002855199},
author={Jeong-Jo Hong and Yong sun Oh},
title={Comparative Analysis of Solar Power Generation Prediction AI Model DNN-RNN},
journal={Journal of Internet of Things and Convergence},
issn={2466-0078},
year={2022},
volume={8},
number={3},
pages={55-61},
doi={10.20465/KIOTS.2022.8.3.055}
TY - JOUR
AU - Jeong-Jo Hong
AU - Yong sun Oh
TI - Comparative Analysis of Solar Power Generation Prediction AI Model DNN-RNN
JO - Journal of Internet of Things and Convergence
PY - 2022
VL - 8
IS - 3
PB - The Korea Internet of Things Society
SP - 55
EP - 61
SN - 2466-0078
AB - In order to reduce greenhouse gases, the main culprit of global warming, the United Nations signed the Climate Change Convention in 1992. Korea is also pursuing a policy to expand the supply of renewable energy to reduce greenhouse gas emissions. The expansion of renewable energy development using solar power led to the expansion of wind power and solar power generation. The expansion of renewable energy development, which is greatly affected by weather conditions, is creating difficulties in managing the supply and demand of the power system. To solve this problem, the power brokerage market was introduced. Therefore, in order to participate in the power brokerage market, it is necessary to predict the amount of power generation. In this paper, the prediction system was used to analyze the Yonchuk solar power plant. As a result of applying solar insolation from on-site (Model 1) and the Korea Meteorological Administration (Model 2), it was confirmed that accuracy of Model 2 was 3% higher. As a result of comparative analysis of the DNN and RNN models, it was confirmed that the prediction accuracy of the DNN model improved by 1.72%.
KW - Power generation forecast;Solar power;AI model;Data quality management;Insolation
DO - 10.20465/KIOTS.2022.8.3.055
ER -
Jeong-Jo Hong and Yong sun Oh. (2022). Comparative Analysis of Solar Power Generation Prediction AI Model DNN-RNN. Journal of Internet of Things and Convergence, 8(3), 55-61.
Jeong-Jo Hong and Yong sun Oh. 2022, "Comparative Analysis of Solar Power Generation Prediction AI Model DNN-RNN", Journal of Internet of Things and Convergence, vol.8, no.3 pp.55-61. Available from: doi:10.20465/KIOTS.2022.8.3.055
Jeong-Jo Hong, Yong sun Oh "Comparative Analysis of Solar Power Generation Prediction AI Model DNN-RNN" Journal of Internet of Things and Convergence 8.3 pp.55-61 (2022) : 55.
Jeong-Jo Hong, Yong sun Oh. Comparative Analysis of Solar Power Generation Prediction AI Model DNN-RNN. 2022; 8(3), 55-61. Available from: doi:10.20465/KIOTS.2022.8.3.055
Jeong-Jo Hong and Yong sun Oh. "Comparative Analysis of Solar Power Generation Prediction AI Model DNN-RNN" Journal of Internet of Things and Convergence 8, no.3 (2022) : 55-61.doi: 10.20465/KIOTS.2022.8.3.055
Jeong-Jo Hong; Yong sun Oh. Comparative Analysis of Solar Power Generation Prediction AI Model DNN-RNN. Journal of Internet of Things and Convergence, 8(3), 55-61. doi: 10.20465/KIOTS.2022.8.3.055
Jeong-Jo Hong; Yong sun Oh. Comparative Analysis of Solar Power Generation Prediction AI Model DNN-RNN. Journal of Internet of Things and Convergence. 2022; 8(3) 55-61. doi: 10.20465/KIOTS.2022.8.3.055
Jeong-Jo Hong, Yong sun Oh. Comparative Analysis of Solar Power Generation Prediction AI Model DNN-RNN. 2022; 8(3), 55-61. Available from: doi:10.20465/KIOTS.2022.8.3.055
Jeong-Jo Hong and Yong sun Oh. "Comparative Analysis of Solar Power Generation Prediction AI Model DNN-RNN" Journal of Internet of Things and Convergence 8, no.3 (2022) : 55-61.doi: 10.20465/KIOTS.2022.8.3.055
