Browse > Article
http://dx.doi.org/10.13067/JKIECS.2019.14.5.943

Performance Comparison of Machine Learning in the Prediction for Amount of Power Market  

Choi, Jeong-Gon (Chosun University, Electrical Engineering)
Publication Information
The Journal of the Korea institute of electronic communication sciences / v.14, no.5, 2019 , pp. 943-950 More about this Journal
Abstract
Machine learning can greatly improve the efficiency of work by replacing people. In particular, the importance of machine learning is increasing according to the requests of fourth industrial revolution. This paper predicts monthly power transactions using MLP, RNN, LSTM, and ANFIS of neural network algorithms. Also, this paper used monthly electricity transactions for mount and money, final energy consumption, and diesel fuel prices for vehicle provided by the National Statistical Office, from 2001 to 2017. This paper learns each algorithm, and then shows predicted result by using time series. Moreover, this paper proposed most excellent algorithm among them by using RMSE.
Keywords
Machine learning; Multi-perceptron; RNN; LSTM; ANFIS; Prediction; Power market;
Citations & Related Records
Times Cited By KSCI : 6  (Citation Analysis)
연도 인용수 순위
1 A. Graves, Supervised Sequence Labelling with Recurrent Neural Networks. Heidelberg New York: Springer, 2012.
2 S. Kim, Coding Chef's 3 Minute Deep Learning, Keras flavor. seoul Korea: HanbitMedia, 2018.
3 J. R. Jang, "ANFIS: Adaptive-Network-Based-Fuzzy Inference System," IEEE Transactions on systems, man, and cybernetics, vol. 23, 1993, pp. 665-685.   DOI
4 J. Yi and C. Choi, "Flood Forecasting and Warning Using Neuro-Fuzzy Inference Technique," J. of Korea Water Resources Association, vol. 41, no. 3, 2008, pp.341-351.   DOI
5 K. Lee, H. Lee, and K. Oh, "Using fuzzy-neural network to predict hedge fund survival," J. of the Korean Data & Information Science Society, vol. 26, no. 6, 2015, pp. 1189-1198.   DOI
6 G. Lee, Artificial Intelligence : from Turing test to Deep Learning. seoul Korea: saengneung, 2018.
7 B. Wang, "Short-term Electrical Load Forecasting Using Neuro-Fuzzy Model with Error Compensation," J. of Fuzzy Logic and Intelligent Systems, vol. 9, no. 4, Dec. 2009, pp. 327-332.   DOI
8 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. 2, 2018, pp. 127-136.
9 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.   DOI
10 Y. Bang, C. Lee, and H. Park, "Electricity Load Forecasting by using a Normalized Fuzzy System," J. of Fuzzy Logic and Intelligent Systems, vol. 28, no. 1, Feb. 2018, pp. 57-64.
11 Y. Bae and N. Kim, "Classification of Motor status using K-nearest neighbors," J. of the Korea Institute of Electronic Communication Sciences, vol. 13, no. 6, Dec. 2018, pp. 1249-1256.   DOI
12 Y. Kim, G. Mun, and S. Choi, "Future Trend Impact Analysis Based on Adaptive Neuro-Fuzzy Inference System," J. of The Korea Institute of Electronic Communication Sciences, vol. 10, no. 4, Apr. 2015, pp. 499-506.   DOI
13 H. Yoon, Y. Kim, K. Ha, and G. Kim "Application of groundwater-level prediction models using data-based learning algorithms to National Groundwater Monitoring Network data," J. of Engineering Geology, vol. 23, no. 2, June 2013, pp. 137-147.   DOI