Browse > Article
http://dx.doi.org/10.7232/JKIIE.2013.39.1.030

Electricity Price Prediction Based on Semi-Supervised Learning and Neural Network Algorithms  

Kim, Hang Seok (Department of Industrial Engineering, Ajou University)
Shin, Hyun Jung (Department of Industrial Engineering, Ajou University)
Publication Information
Journal of Korean Institute of Industrial Engineers / v.39, no.1, 2013 , pp. 30-45 More about this Journal
Abstract
Predicting monthly electricity price has been a significant factor of decision-making for plant resource management, fuel purchase plan, plans to plant, operating plan budget, and so on. In this paper, we propose a sophisticated prediction model in terms of the technique of modeling and the variety of the collected variables. The proposed model hybridizes the semi-supervised learning and the artificial neural network algorithms. The former is the most recent and a spotlighted algorithm in data mining and machine learning fields, and the latter is known as one of the well-established algorithms in the fields. Diverse economic/financial indexes such as the crude oil prices, LNG prices, exchange rates, composite indexes of representative global stock markets, etc. are collected and used for the semi-supervised learning which predicts the up-down movement of the price. Whereas various climatic indexes such as temperature, rainfall, sunlight, air pressure, etc, are used for the artificial neural network which predicts the real-values of the price. The resulting values are hybridized in the proposed model. The excellency of the model was empirically verified with the monthly data of electricity price provided by the Korea Energy Economics Institute.
Keywords
Electricity Price Forecasting; Semi-Supervised Learning; Artificial Neural Network; Hybrid Model;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Gareta, R. and Romeo, L. M. et al. (2006), Forecasting of electricity prices with neural networks, Energy Conversion and Management, 47, 1770-1778.   DOI   ScienceOn
2 Ghiassi, M., Zimbra, D. K. et al. (2006), Medium term system load forecasting with a dynamic artificial neural network model, Electric Power Systems Research, 76, 302-316.   DOI   ScienceOn
3 Gong, Y.-C. and Chen, C.-L. (2008), Semi-supervised Method for Gene Expression Data Classification with Gaussian Fields and Harmonic Functions, International Conference on Pattern Recognition, Tampa, FL.
4 Joachims, T. (1999), Transductive Inference for Text Classification using Support Vector Machines, International Conference on Machine Learning, San Francisco.
5 Kim, D.-Y. Lee, C.-J. et al. (2006), Development of System Marginal Price Forecasting Method Using ARIMA Model, Trans. KIEE, 55A, 85-93.
6 Lee, H. and Shin, H. (2011), Electricity Demand Forecasting based on Support Vector Regression, IE Interfaces, 24(4), 351-361.   DOI   ScienceOn
7 Lee, J.-K. Park, J.-B. et al. (2005), A System Marginal Price Forecasting Method Based on an Artificial Neural Network Using Time and Day Information, Trans. KIEE, 54A, 144-151.
8 Liu, R. and Zhou, J. et al. (2006), A Graph-based Semi-supervised Learning Algorithm for Web Page Classification, International Conference on Intelligent Systems Design and Applications, China.
9 Nigam, K. and Mccallum, A. K. et al. (1999), Text Classication from Labeled and Unlabeled Documents using EM, Machine Learning, 39, 1-34.
10 Nogales, F. J., Javier Contreras, M. IEEE et al. (2002), Forecasting Next- Day Electricity Prices by Time Series Models, IEEE TRANSACTIONS ON POWER SYSTEMS, 17, 342-348.
11 Vahidinasab, V. and Jadid, S. et al. (2008), Day-ahead price forecasting in restructured power systems using artificial neural networks, Electric Power Systems Research, 78, 1332-1342.   DOI   ScienceOn
12 Weron, R. and Misiorek, A. (2008), Forecasting spot electricity prices : A comparison of parametric and semiparametric time series models, International Journal of Forecasting, 24, 744-763.   DOI   ScienceOn
13 Yamin, H. Y. and Shahidehpour, S. M. et al. (2004), Adaptive shor-term electricity price forecasting using artificial neural networks in the restructured power markets, Electrical Power and Energy Systems, 26, 571-581.   DOI   ScienceOn
14 Yarowsky, D. (1995), Unsupervised word sense disambiguation rivaling supervised methods, ACL 1995 Proceedings of the 33rd annual meeting on Association for Computational Linguistics Stroudsburg, Association for Computational Linguistics Stroudsburg, PA, USA.
15 Zhou, D. and Bousquet, O. et al. (2004), Learning with local and global consistency, Advances in Neural Information Processing Systems, 16, 321-328.
16 Zhu, X. (2005), Semi-Supervised Learning with Graphs, Ph.D. dissertation, Pittsburgh, PA 15213, Carnegie Mellon.
17 Zhu, X. (2008), Semi-Supervised Learning Literature Survey, 1-60.
18 Zhu, X. and Ghahramani, Z. et al. (2003), Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions, International Conference on Machine Learning(ICML 2003), Washington DC.
19 Pao, H.-T. (2007), Forecasting electricity market pricing using artificial neural networks, Energy Conversion and Management, 48, 907-912.   DOI   ScienceOn
20 Pino, R. and Parreno, J. et al. (2008), Forecasting next-day price of electricity in Spanish energy market using artificial neural networks, Engineering Applications of Artificial Intelligence, 21, 53-62.   DOI   ScienceOn
21 Reich, Y. and Barai, S. V. (2000), A methodology for building neural networks models from empirical engineering data, Engineering Applications of Artificial Intelligence, 13, 685-694.   DOI   ScienceOn
22 Shin, H. and Hill, N. J. et al. (2010), Graph sharpening, Expert Systems with Applications, 37(12), 7870-7879.   DOI   ScienceOn
23 Shin, H. and Lisewski, A. M. et al. (2007), Graph sharpening plus graph integration : a synergy that improves protein functional classification, Bioinformatics, 23, 3217-3224.   DOI   ScienceOn
24 Shin, H. and Tsuda, K. (2006), Prediction of Protein Function from Networks, Semi-Supervised Learning, O. Chapelle, B. Scholkopf and A. Zien, MIT press, 339-352.
25 Subramanya, A. and Bilmes, J. (2008), Soft-Supervised Learning for Text Classification, EMNLP 2008 Proceedings of the Conference on Empirical Methods in Natural Language Processing Honolulu, Hawaii, Association for Computational Linguistics Stroudsburg, PA, USA.
26 Tan, Z. and Zhang, J. et al. (2010), Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models, Applied Energy, 87, 3606-3610.   DOI   ScienceOn
27 Torghaban, S. S. and Zareipour, H. et al. (2010), Medium-Term Electricity Market Price Forecasting : A Data-driven Approach, North American Power Symposium (NAPS), Arlington, TX, 1-7.
28 Aggarwal, S. K. and Saini, L. M. et al. (2009), Electricity price forecasting in deregulated markets : A review and evaluation, Electrical Power and Energy Systems, 31, 13-22.   DOI   ScienceOn
29 Amjady, N. and Daraeepour, A. (2009), Design of input vector for dayahead price forecasting of electricity markets, Expert Systems with Applications, 36, 12281-12294.   DOI   ScienceOn
30 Amjady, N. and Keynia, F. (2008), Day ahead price forecasting of electricity markets by a mixed data model and hybrid forecast method, Electrical Power and Energy Systems, 30, 533-546.   DOI   ScienceOn
31 Amjady, N. and Keynia, F. (2009), Day-ahead price forecasting of electricity markets by a new feature selection algorithm and cascaded neural network technique, Energy Conversion and Management, 50 (2976-2982), 2976-2982.   DOI   ScienceOn
32 Ando, R. K. and Zhang, T. (2005), A High-Performance Semi-Supervised Learning Method for Text Chunking, Annual Meeting on Association for Computational Linguistics, Ann Arbor, Michigan.
33 Azadeh, A. and Ghaderi, S. F. et al. (2008), Annual electricity consumption forecasting by neural network in high energy consuming industrial sectors, Energy Conversion and Management, 49, 2272- 2278.   DOI   ScienceOn
34 Bair, E. and Tibshirani, R. (2004), Semi-Supervised Methods to Predict Patient Survival from Gene Expression Data, PLoS Biology, 2, 511- 522.
35 Bishop and Christopher (1995), Neural Networks for Pattern Recognition, Oxford.
36 Blum, A. and Chawla, S. (2001), Learning from Labeled and Unlabeled Data using Graph Mincuts, ICML 2001 Proceedings of the Eighteenth International Conference on Machine Learning San Francisco, Morgan Kaufmann Publishers Inc. San Francisco, CA, USA.
37 Blum, A. and Mitchell, T. (1998), Combining labeled and unlabeled data with co-training, COLT 1998 Proceedings of the eleventh annual conference on Computational learning theory New York, ACM New York, NY, USA.
38 Catalao, J. P. S. and Mariano, S. J. P. S. et al. (2007), Short-term electricity prices forecasting in a competitive market : A neural network approach, Electric Power Systems Research, 77, 1297-1304.   DOI   ScienceOn
39 Chan, K. F. and Gray, P. et al. (2008), A new approach to characterizing and forecasting electricity price volatility, International Journal of Forecasting, 24, 728-743.   DOI   ScienceOn
40 Chapelle, O. and Scholkopf, B. et al. (2006), Semi-Supervised Learning, Cambridge, England, MIT Press.
41 Deb, R. and Albert, R. et al. (2000), How to Incorporate Volatility and Risk in Electricity Price Forecasting, The Electricity Journal, 13, 65-75.
42 Ercan, P. and Soto, J. (2011), A model for long term electricity price forecasting for France, Master Thesis, KTH Royal Institute of Technology.