KIEE - The Transactions of the Korean Institute of Electrical Engineers

1

Ministry of Trade, Industry and Energy (MOTIE), “2020 Energy Survey Results,” 2022. Available online : http://english.motie.go.kr/www/main.do.

2

C. Y. Lim, “Design and Implementation of IEC 62541-based Industry Internet of Things Simulator for Meta-Factory,” The Journal of the Convergence on Culture Technology (JCCT), vol. 9, no. 3, pp. 789–795, 2023. DOI : 10.17703/JCCT.2023.9.3.789

3

C. E. Yeo, “Energy Consumption Analysis of Batch Type Heating Process for Energy Savings in Food Processing Plants,” The Journal of the Convergence on Culture Technology (JCCT), vol. 9, no. 3, pp. 817–823, 2023. DOI : 10.17703/JCCT.2023.9.3.817

4

I. G. Yeo, “Pay Attention FEMS Strengthened Regulations Greenhouse Gas Energy,” Korea Heating Air-Conditioning Refrigeration & Renewable Energy News (KHARN), Seoul, Republic of Korea, 2020.

5

M. M. Comesaña, “Prediction of building’s thermal performance using LSTM and MLP neural networks,” Energies, vol. 10, no. 7439, 2020.

6

C. Y. Lim, “Design and Performance Evaluation of Digital Twin Prototype Based on Biomass Plant,” The Journal of the Convergence on Culture Technology (JCCT), vol. 9, no. 5, pp. 935–940, 2023. DOI : 10.17703/JCCT.2023.9.5.935.

7

C. Lee, “Prediction performance analysis of artificial neural network model by input variable combination for residential heating loads,” Energies, vol. 14, no. 756, 2021.

8

J. Runge, “Forecasting energy use in buildings using artificial neural networks: A review,” Energies, vol. 12, no. 3254, 2019.

9

J. S. Choi, “LSTM-based power load prediction system design for store energy saving,” Journal of Korea Information and Electronics Communication Technology, vol. 14, pp. 307–313, 2021.

10

C. Y. Lim, “Artificial Neural Network-based Thermal Environment Prediction Model for Energy Saving of Data Center Cooling Systems,” The Journal of the Convergence on Culture Technology (JCCT), vol. 9, no. 6, pp. 883–888, 2023. DOI : 10.17703/JCCT.2023.9.6.889

11

W. S. Jeon, “GIS-Based Prediction of electricity consumption for apartment complex by using machine learning,” Journal of the Korean Institute of Communication and Information Sciences, vol. 1, pp. 407–408, 2022.

12

J. Qunlong, “A study on energy consumption prediction from building energy management system data with missing values using SSIM and VLSW algorithms,” Journal of the Korean Institute of Electrical Engineers, vol. 70, pp. 1–540, 2021.

13

M. Ito, “Textbooks of Machine Learning with Python,” K. -S. Park, Translator, Hanvit Media, Seoul, Republic of Korea, 2020.

14

C. Y. Lim, “Designing Digital Twin Concept Model for High-Speed Synchronization,” The Journal of the Convergence on Culture Technology (JCCT), vol. 9, no. 6, pp. 245–250, 2023. DOI : 10.17703/JCCT.2023.9.6.245

15

B. R. Park, “Performance tests on the ANN model prediction accuracy for cooling load of buildings during the setback period,” Journal of Korea Institute of Ecological Architecture and Environment, vol. 17, pp. 83–88, 2017.

16

A. Sadeghi, “An intelligent model to predict energy performances of residential buildings based on deep neural networks,” Energies, vol. 13, no. 571, 2020.

17

W. Choi, “Multi-objective optimization of flexible wing using multidisciplinary design optimization system of aero-nonlinear structure interaction based on support vector regression,” Journal of the Korean Society for Aeronautical and Space Sciences, vol. 43, pp. 601–608, 2015.

18

S. Oh, “Comparison of a response surface method and artificial neural network in predicting the aerodynamic performance of a wind turbine airfoil and its optimization,” Applied Sciences, vol. 10, no. 6277, 2020.

19

C. Sudheer, “A hybrid SVM-PSO model for forecasting monthly streamflow,” Neural Computing and Applications, vol. 24, pp. 1381–1389, 2014.

20

J. B. Kim, “Comparison of MLR and SVR based linear and nonlinear regressions Compensation for wind speed prediction,” Journal of the Korean Institute of Electrical Engineers, vol. 65, pp. 851–856, 2016.

21

B. C. Oh, “Development of SVR-based short-term load forecasting algorithm,” Journal of the Korean Institute of Electrical Engineers, vol. 68, pp. 95–99, 2019.

22

E. Ceperic, “A strategy for short-term load forecasting by support vector regression machines,” IEEE Transactions on Power Systems, vol. 28, pp. 4356–4364, 2013.

KIEEThe Transactions of
the Korean Institute of Electrical Engineers

The Transactions of the Korean Institute of Electrical Engineers

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KIEEThe Transactions ofthe Korean Institute of Electrical Engineers