그림 1. 전력거래량 시계열 데이터 Fig. 1 Time series of power trading amount
그림 2. 전력거래금액 시계열 데이터 Fig. 2 Time series of power trading amount of money
그림 3. 생산확산지수 시계열데이터 Fig. 3 Time series of production spread index
그림 4. 최종에너지 소비의 시계열 데이터 Fig. 4 Time series of final energy consumption
그림 5. 자동차용 경유의 시계열데이터 Fig. 5 Time series of vehicle diesel
그림 6. k-근접 이웃 회귀 알고리즘을 이용한 전력거래량 데이터의 학습 Fig. 6 Learning of power trading amount data using k-Neighbors Regression algorithms
그림 7. k-근접 이웃 회귀 알고리즘을 이용한 전력거래금액 데이터의 학습 Fig. 7 Learning of power trading amount of money data using k-Neighbors Regression algorithms
그림 8. k-근접 이웃 회귀 알고리즘을 이용한 생산확산지수 데이터의 학습 Fig. 8 Learning of production spread index data using k-Neighbors Regression algorithms
그림 9. k-근접 이웃 회귀 알고리즘을 이용한 최종에너지소비 데이터의 학습 Fig. 9 Learning of final energy consumption data using k-NeighborsRegressor algorithms
그림 10. k-근접 이웃 회귀 알고리즘을 이용한 자동차용 경유 데이터의 학습 Fig. 10 Learning of vehicle diesel data using k-Neighbors Regression algorithms
그림 11. 전력거래량의 f(x) Fig. 11 f(x) of power trading amount
그림 12. 전력거래금액의 f(x) Fig. 12 f(x) of power trading amount of money
그림 13. 생산확산지수의 f(x) Fig. 13 f(x) of production spread index
그림 14. 최종에너지소비의 f(x) Fig. 14 f(x) of final energy consumption
그림 15. 자동차용 경유의 f(x) Fig. 15 f(x) of vehicle diesel
그림 16. 전력거래량 데이터를 릿지 회귀 알고 리즘으로 학습하여 나타낸 f(x) Fig. 16 f(x) showing power trading amount data learned by Ridge algorithm
그림 17. 전력거래금액 데이터를 릿지 회귀 알고리즘으로 학습하여 나타낸 f(x) Fig. 17 f(x) showing power trading amount of money data learned by Ridge algorithm
그림 18. 생산확산지수 데이터를 릿지 회귀 알고리즘으로 학습하여 나타낸 f(x) Fig. 18 f(x) showing production spread index data learned by Ridge algorithm
그림 19. 최종에너지소비 데이터를 릿지 회귀 알고리즘으로 학습하여 나타낸 f(x) Fig. 19 f(x) showing final energy consumption data learned by Ridge algorithm
그림 20. 자동차용 경유 데이터를 릿지 회귀 알고리즘으로 학습하여 나타낸 f(x) Fig. 20 f(x) showing vehicle diesel data learned by Ridge algorithm
표 1. 각 알고리즘의 예측 정확도 결과 Table 1. Result of prediction accuracy for each algorithm
표 2. 각각의 예측 정확도를 평균 Table 2. Average of prediction accuracy for each algorithm
References
- H. Kim and H. Lee, "Fault detect and classification framework for semiconductor manufacturing processes using missing data estimation and generative adversary network," J. of Korean Institute of Intelligent Systems, vol. 28, no. 4, 2018, pp. 532-537.
- T. Tokisa, N. Miyake, S. Maeda, H. Kim, J. K. Tan, S. Ishikawa, S. Murakami, and T. Aoki, "Detection of Lung Nodule on Temporal Subtraction Images Based on Artificial Neural Network," Int. J. of Fuzzy Logic and Intelligent Systems, vol. 12, no. 2, 2012, pp. 137-142. https://doi.org/10.5391/IJFIS.2012.12.2.137
- F. Asghar, M. Talha, and S. Kim, "Comparative Study of Three Fault Diagnostic Methods for Three Phase Inverter with Induction Motor," Int. J. of Fuzzy Logic and Intelligent Systems, vol. 17, no. 4, 2017, pp. 245-256. https://doi.org/10.5391/IJFIS.2017.17.4.245
- Y. Jung and Y. Bae, "Analysis of Fault Diagnosis for Current and Vibration Signals in Pumps and Motors using a Reconstructed Phase Portrait," Int. J. of Fuzzy Logic and Intelligent Systems, vol. 15, no. 3, 2015, pp. 166-171. https://doi.org/10.5391/IJFIS.2015.15.3.166
- R. Casimir, E. Boutleux, G. Clerc, and A. Yahoui, "The use of features selection and nearest neighbors rule for faults diagnostic in induction motors," Engineering Applications of Artificial Intelligence, vol. 19, no. 2, 2006, pp. 169-177. https://doi.org/10.1016/j.engappai.2005.07.004
- J. Juez, G. I. Sainz, E. J. Moya, and J. R. Per'an. "Early Detection and Diagnosis of Faults in an AC Motor Using Neuro Fuzzy Techniques: FasArt + Fuzzy k Nearest Neighbors," in International Work-Conference on Artificial Neural Networks 2001, Lecture Notes in Computer Science, vol. 2085, 2001. pp. 571-578.
- J. M. Keller, M. R. Gray, and J. A. Givens, "A Fuzzy K-Nearest Neighbor Algorithm," IEEE Trans. Systems, Man, and Cybernetics, vol. SMC-15, no. 4, 1985, pp. 581-585.
- J. Tian, C. Morillo, M. H. Azarian, and M. Pecht, "Motor Bearing Fault Detection Using Spectral Kurtosis-Based Feature Extraction Coupled With K-Nearest Neighbor Distance Analysis," IEEE Trans. Industrial Electronics, vol. 63, no. 3, 2016, pp. 1793-1803. https://doi.org/10.1109/TIE.2015.2509913
- S. Bang, "Implementation of Image based Fire Detection System Using Convolution Neural Network," J. of the Korea Institute of Electronic Communication Science, vol. 12, no. 2, 2017, pp. 331-336. https://doi.org/10.13067/JKIECS.2017.12.2.331
- Y. Kim, S. Park, and D. Kim, "Research on Robust Face Recognition against Lighting Variation using CNN," J. of the Korea Institute of Electronic Communication Science, vol. 12, no. 2, 2017, pp. 325-330. https://doi.org/10.13067/JKIECS.2017.12.2.325
- C. Jung, R. Jang, D. Nyang, and K. Lee " A Study of User Behavior Recognition-Based PIN Entry Using Machine Learning Technique," Korea Information Processing Society review, computer and communication systems, vol. 7, no. 5, 2018, pp. 127-136.
- G. Lee, H. Ha, H. Hong, and H. Kim "Exploratory Research on Automating the Analysis of Scientific Argumentation Using Machine Learning," J. of the Korean Association for Science Education, vol. 38, no. 2, 2018, pp. 219-234. https://doi.org/10.14697/JKASE.2018.38.2.219
- N. Kim and Y. Bae, "Status Diagnosis of Pump and Motor Applying K-Nearest Neighbors," J. of the Korea Institute of Electronic Communication Science, vol. 13, no. 6, 2018, pp. 1249-1255. https://doi.org/10.13067/JKIECS.2018.13.6.1249