• 대한전기학회
Mobile QR Code QR CODE : The Transactions of the Korean Institute of Electrical Engineers
  • COPE
  • kcse
  • 한국과학기술단체총연합회
  • 한국학술지인용색인
  • Scopus
  • crossref
  • orcid

References

1 
Y.-G. Son, and S.-Y. Kim, “Optimal Planning for Integrated Energy System According to Special Act on the Promotion of Distributed Energy,” The Transactions of the Korean Institute of Electrical Engineers, vol. 73, no. 1, pp. 16–25, Jan. 2024.DOI:10.5370/KIEE.2024.73.1.16URL
2 
M. Gough et al., “Operation of a Technical Virtual Power Plant Considering Diverse Distributed Energy Resources,” IEEE Transactions on Industry Applications, vol. 58, no. 2, pp. 2547–2558, Mar.–Apr. 2022.DOI:10.1109/TIA.2022.3143479DOI
3 
Y. Zhang, Z. Chen, K. Ma, and F. Chen, “A Decentralized IoT Architecture of Distributed Energy Resources in Virtual Power Plant,” IEEE Internet of Things Journal, vol. 10, no. 10, pp. 9193–9205, May 2023.DOI:10.1109/JIOT.2022.3233134DOI
4 
E. Perez, H. Beltran, N. Aparicio, and P. Rodriguez, “Predictive Power Control for PV Plants With Energy Storage,” IEEE Transactions on Sustainable Energy, vol. 4, no. 2, pp. 482–490, Apr. 2013.DOI:10.1109/TSTE.2012.2210255DOI
5 
F. Wang, Z. Xuan, Z. Zhen, K. Li, T. Wang, and M. Shi, “A day-ahead PV power forecasting method based on LSTM-RNN model and time correlation modification under partial daily pattern prediction framework,” Energy Conversion and Management, vol. 212, pp. 112766, May 2020.DOI:10.1016/j.enconman.2020.112766DOI
6 
D. So, J. Oh, S. Leem, H. Ha, and J. Moon, “A Hybrid Ensemble Model for Solar Irradiance Forecasting: Advancing Digital Models for Smart Island Realization,” Electronics, vol. 12, pp. 2607, Jun. 2023.DOI:10.3390/electronics12122607DOI
7 
J. Moon, “A Multi-Step-Ahead Photovoltaic Power Forecasting Approach Using One-Dimensional Convolutional Neural Networks and Transformer,” Electronics, vol. 13, pp. 2007, May 2024.DOI:10.3390/electronics13112007DOI
8 
K. Wang, X. Qi, and H. Liu, “A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network,” Applied Energy, vol. 251, pp. 113315, Oct. 2019.DOI:10.1016/j.apenergy.2019.113315DOI
9 
H. Min, S. Hong, J. Song, B. Son, B. Noh, and J. Moon, “SolarFlux Predictor: A Novel Deep Learning Approach for Photovoltaic Power Forecasting in South Korea,” Electronics, vol. 13, pp. 2071, May 2024.DOI:10.3390/electronics13112071DOI
10 
M. AlShafeey, and C. Csáki, “Evaluating neural network and linear regression photovoltaic power forecasting models based on different input methods,” Energy Reports, vol. 7, pp. 7601–7614, Nov. 2021.DOI:10.1016/j.egyr.2021.10.125URL
11 
W. Zhao, H. Zhang, J. Zheng, Y. Dai, L. Huang, W. Shang, and Y. Liang, “A point prediction method based automatic machine learning for day-ahead power output of multi-region photovoltaic plants,” Energy, vol. 223, pp. 120026, May 2021. DOI:10.1016/j.energy.2021.120026DOI
12 
J.-Y. Oh, Y.-G. Lee, and G. Kim, “Improvement of Solar Power Forecasting Using Interpretation of Artificial Intelligence,” The Transactions of the Korean Institute of Electrical Engineers, vol. 69, no. 7, pp. 1111–1116, Jul. 2020.DOI:10.5370/KIEE.2020.69.7.1111URL
13 
B. Kang, and J. Yun, “A Study on the Prediction Model of Photovoltaic Power Generation using Deep Learning Algorithm,” Journal of The Institute of Electronics and Information Engineers, vol. 60, no. 2, pp. 119–125, Feb. 2023.DOI:10.5573/ieie.2023.60.2.119URL
14 
P. Li, K. Zhou, X. Lu, and S. Yang, “A hybrid deep learning model for short-term PV power forecasting,” Applied Energy, vol. 259, pp. 114216, Feb. 2020.DOI:10.1016/j.apenergy.2019.114216DOI
15 
A. Agga, A. Abbou, M. Labbadi, Y. El Houm, and I. H. O. Ali, “CNN-LSTM: An efficient hybrid deep learning architecture for predicting short-term photovoltaic power production,” Electric Power Systems Research, vol. 208, pp. 107908, Jul. 2022.DOI:10.1016/j.epsr.2022.107908DOI
16 
S. Heslop, and I. MacGill, “Comparative analysis of the variability of fixed and tracking photovoltaic systems,” Solar Energy, vol. 107, pp. 351–364, Sep. 2014.DOI:10.1016/j.solener.2014.05.015DOI
17 
J. Oh, D. So, J. Jo, N. Kang, E. Hwang, and J. Moon, “Two-Stage Neural Network Optimization for Robust Solar Photovoltaic Forecasting,” Electronics, vol. 13, pp. 1659, Apr. 2024.DOI:10.3390/electronics13091659DOI
18 
J. Moon, S. Park, S. Rho, and E. Hwang, “A comparative analysis of artificial neural network architectures for building energy consumption forecasting,” International Journal of Distributed Sensor Networks, vol. 15, no. 9, pp. 1550147719877616, Sep. 2019.DOI:10.1177/1550147719877616DOI
19 
Y. Yu, X. Si, C. Hu, and J. Zhang, “A review of recurrent neural networks: LSTM cells and network architectures,” Neural Computation, vol. 31, no. 7, pp. 1235–1270, Jul. 2019.DOI:10.1162/neco_a_01199DOI
20 
A. Mellit, A. M. Pavan, and V. Lughi, “Deep learning neural networks for short-term photovoltaic power forecasting,” Renewable Energy, vol. 172, pp. 276–288, Jul. 2021.DOI:10.1016/j.renene.2021.02.166DOI
21 
M. Khan, H. Wang, A. Riaz, A. Elfatyany, and S. Karim, “Bidirectional LSTM-RNN-based hybrid deep learning frameworks for univariate time series classification,” The Journal of Supercomputing, vol. 77, pp. 7021–7045, Jul. 2021.DOI:10.1007/s11227-020-03560-zDOI
22 
D. So, J. Oh, I. Jeon, J. Moon, M. Lee, and S. Rho, “BiGTA-Net: A hybrid deep learning-based electrical energy forecasting model for building energy management systems,” Systems, vol. 11, no. 9, pp. 456, Sep. 2023.DOI:10.3390/systems11090456DOI
23 
J. Liang, and W. Tang, “Ultra-Short-Term Spatiotemporal Forecasting of Renewable Resources: An Attention Temporal Convolutional Network-Based Approach,” IEEE Transactions on Smart Grid, vol. 13, no. 5, pp. 3798–3812, Sep. 2022.DOI:10.1109/TSG.2022.3175451DOI
24 
T. Limouni, R. Yaagoubi, K. Bouziane, K. Guissi, and E. H. Baali, “Accurate one step and multistep forecasting of very short-term PV power using LSTM-TCN model,” Renewable Energy, vol. 205, pp. 1010–1024, Mar. 2023.DOI:10.1016/j.renene.2023.01.118DOI
25 
J. Song, W. Li, S. Zhu, C. Zhou, G. Xue, and X. Wu, “Predicting hourly heating load in district heating system based on the hybrid Bi-directional long short-term memory and temporal convolutional network model,” Journal of Cleaner Production, vol. 463, pp. 142769, Jul. 2024.DOI:10.1016/j.jclepro.2024.142769DOI
26 
X. Pu, H. Xiao, J. Wang, W. Pei, J. Yang, and J. Zhang, “A novel GRU-TCN network based Interactive Behavior Learning of multi-energy Microgrid under incomplete information,” Energy Reports, vol. 9, pp. 608–616, Sep. 2023.DOI:10.1016/j.egyr.2023.04.128DOI