IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v5y2012i11p4711-4725d21544.html
   My bibliography  Save this article

Day-Ahead Electricity Price Forecasting Using a Hybrid Principal Component Analysis Network

Author

Listed:
  • Ying-Yi Hong

    (Department of Electrical Engineering, Chung Yuan Christian University, 200 Chung Pei Road, Chung Li 32023, Taiwan)

  • Ching-Ping Wu

    (Department of Electrical Engineering, Chung Yuan Christian University, 200 Chung Pei Road, Chung Li 32023, Taiwan)

Abstract

Bidding competition is one of the main transaction approaches in a deregulated electricity market. Locational marginal prices (LMPs) resulting from bidding competition and system operation conditions indicate electricity values at a node or in an area. The LMP reveals important information for market participants in developing their bidding strategies. Moreover, LMP is also a vital indicator for the Security Coordinator to perform market redispatch for congestion management. This paper presents a method using a principal component analysis (PCA) network cascaded with a multi-layer feedforward (MLF) network for forecasting LMPs in a day-ahead market. The PCA network extracts essential features from periodic information in the market. These features serve as inputs to the MLF network for forecasting LMPs. The historical LMPs in the PJM market are employed to test the proposed method. It is found that the proposed method is capable of forecasting day-ahead LMP values efficiently.

Suggested Citation

  • Ying-Yi Hong & Ching-Ping Wu, 2012. "Day-Ahead Electricity Price Forecasting Using a Hybrid Principal Component Analysis Network," Energies, MDPI, vol. 5(11), pages 1-15, November.
    • Handle: RePEc:gam:jeners:v:5:y:2012:i:11:p:4711-4725:d:21544
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/5/11/4711/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/5/11/4711/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ming Meng & Dongxiao Niu & Wei Sun, 2011. "Forecasting Monthly Electric Energy Consumption Using Feature Extraction," Energies, MDPI, vol. 4(10), pages 1-13, September.
    2. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    3. Fei Wang & Zengqiang Mi & Shi Su & Hongshan Zhao, 2012. "Short-Term Solar Irradiance Forecasting Model Based on Artificial Neural Network Using Statistical Feature Parameters," Energies, MDPI, vol. 5(5), pages 1-16, May.
    4. Al-Agtash, Salem Y., 2010. "Supply curve bidding of electricity in constrained power networks," Energy, Elsevier, vol. 35(7), pages 2886-2892.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Umut Ugurlu & Oktay Tas & Aycan Kaya & Ilkay Oksuz, 2018. "The Financial Effect of the Electricity Price Forecasts’ Inaccuracy on a Hydro-Based Generation Company," Energies, MDPI, vol. 11(8), pages 1-19, August.
    2. Florian Ziel & Rick Steinert & Sven Husmann, 2015. "Forecasting day ahead electricity spot prices: The impact of the EXAA to other European electricity markets," Papers 1501.00818, arXiv.org, revised Dec 2015.
    3. Panapakidis, Ioannis P. & Dagoumas, Athanasios S., 2016. "Day-ahead electricity price forecasting via the application of artificial neural network based models," Applied Energy, Elsevier, vol. 172(C), pages 132-151.
    4. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    5. Lago, Jesus & De Ridder, Fjo & Vrancx, Peter & De Schutter, Bart, 2018. "Forecasting day-ahead electricity prices in Europe: The importance of considering market integration," Applied Energy, Elsevier, vol. 211(C), pages 890-903.
    6. George P. Papaioannou & Christos Dikaiakos & George Evangelidis & Panagiotis G. Papaioannou & Dionysios S. Georgiadis, 2015. "Co-Movement Analysis of Italian and Greek Electricity Market Wholesale Prices by Using a Wavelet Approach," Energies, MDPI, vol. 8(10), pages 1-30, October.
    7. Jesus Lago & Fjo De Ridder & Peter Vrancx & Bart De Schutter, 2017. "Forecasting day-ahead electricity prices in Europe: the importance of considering market integration," Papers 1708.07061, arXiv.org, revised Dec 2017.
    8. Rosano, Kim Jay R. & Nerves, Allan C., 2021. "Forecasting Locational Marginal Prices in Electricity Markets by Using Artificial Neural Networks," Journal of Economics, Management & Agricultural Development, Journal of Economics, Management & Agricultural Development (JEMAD), vol. 7(2), December.
    9. Haider Ali & Faheem Aslam & Paulo Ferreira, 2021. "Modeling Dynamic Multifractal Efficiency of US Electricity Market," Energies, MDPI, vol. 14(19), pages 1-16, September.
    10. Javier Pórtoles & Camino González & Javier M. Moguerza, 2018. "Electricity Price Forecasting with Dynamic Trees: A Benchmark Against the Random Forest Approach," Energies, MDPI, vol. 11(6), pages 1-21, June.
    11. Sergey Voronin & Jarmo Partanen, 2013. "Price Forecasting in the Day-Ahead Energy Market by an Iterative Method with Separate Normal Price and Price Spike Frameworks," Energies, MDPI, vol. 6(11), pages 1-24, November.
    12. Ping Jiang & Feng Liu & Yiliao Song, 2016. "A Hybrid Multi-Step Model for Forecasting Day-Ahead Electricity Price Based on Optimization, Fuzzy Logic and Model Selection," Energies, MDPI, vol. 9(8), pages 1-27, August.
    13. Claudio Monteiro & L. Alfredo Fernandez-Jimenez & Ignacio J. Ramirez-Rosado, 2015. "Explanatory Information Analysis for Day-Ahead Price Forecasting in the Iberian Electricity Market," Energies, MDPI, vol. 8(9), pages 1-23, September.
    14. Kun Li & Joseph D. Cursio & Yunchuan Sun, 2018. "Principal Component Analysis of Price Fluctuation in the Smart Grid Electricity Market," Sustainability, MDPI, vol. 10(11), pages 1-16, November.
    15. Bijay Neupane & Wei Lee Woon & Zeyar Aung, 2017. "Ensemble Prediction Model with Expert Selection for Electricity Price Forecasting," Energies, MDPI, vol. 10(1), pages 1-27, January.
    16. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    17. Umut Ugurlu & Ilkay Oksuz & Oktay Tas, 2018. "Electricity Price Forecasting Using Recurrent Neural Networks," Energies, MDPI, vol. 11(5), pages 1-23, May.
    18. Qunli Wu & Chenyang Peng, 2016. "Wind Power Generation Forecasting Using Least Squares Support Vector Machine Combined with Ensemble Empirical Mode Decomposition, Principal Component Analysis and a Bat Algorithm," Energies, MDPI, vol. 9(4), pages 1-19, April.
    19. Félix Iglesias & Wolfgang Kastner, 2013. "Analysis of Similarity Measures in Times Series Clustering for the Discovery of Building Energy Patterns," Energies, MDPI, vol. 6(2), pages 1-19, January.
    20. George P. Papaioannou & Christos Dikaiakos & Anargyros Dramountanis & Panagiotis G. Papaioannou, 2016. "Analysis and Modeling for Short- to Medium-Term Load Forecasting Using a Hybrid Manifold Learning Principal Component Model and Comparison with Classical Statistical Models (SARIMAX, Exponential Smoot," Energies, MDPI, vol. 9(8), pages 1-40, August.
    21. Yongxiu He & Yangyang Liu & Tian Xia & Min Du & Hongzhen Guo, 2014. "The Optimal Price Ratio of Typical Energy Sources in Beijing Based on the Computable General Equilibrium Model," Energies, MDPI, vol. 7(5), pages 1-24, April.
    22. Francisco Martínez-Álvarez & Alicia Troncoso & Gualberto Asencio-Cortés & José C. Riquelme, 2015. "A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting," Energies, MDPI, vol. 8(11), pages 1-32, November.
    23. Ziel, Florian & Steinert, Rick & Husmann, Sven, 2015. "Forecasting day ahead electricity spot prices: The impact of the EXAA to other European electricity markets," Energy Economics, Elsevier, vol. 51(C), pages 430-444.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ismail Shah & Hasnain Iftikhar & Sajid Ali & Depeng Wang, 2019. "Short-Term Electricity Demand Forecasting Using Components Estimation Technique," Energies, MDPI, vol. 12(13), pages 1-17, July.
    2. Anatoly A. Peresetsky & Ruslan I. Yakubov, 2017. "Autocorrelation in an unobservable global trend: does it help to forecast market returns?," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 7(1/2), pages 152-169.
    3. Kelly Burns & Imad Moosa, 2017. "Demystifying the Meese–Rogoff puzzle: structural breaks or measures of forecasting accuracy?," Applied Economics, Taylor & Francis Journals, vol. 49(48), pages 4897-4910, October.
    4. João C. Claudio & Katja Heinisch & Oliver Holtemöller, 2020. "Nowcasting East German GDP growth: a MIDAS approach," Empirical Economics, Springer, vol. 58(1), pages 29-54, January.
    5. Antonio Rubia & Trino-Manuel Ñíguez, 2006. "Forecasting the conditional covariance matrix of a portfolio under long-run temporal dependence," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(6), pages 439-458.
    6. Athanasopoulos, George & de Carvalho Guillén, Osmani Teixeira & Issler, João Victor & Vahid, Farshid, 2011. "Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions," Journal of Econometrics, Elsevier, vol. 164(1), pages 116-129, September.
    7. Hao Chen & Qiulan Wan & Yurong Wang, 2014. "Refined Diebold-Mariano Test Methods for the Evaluation of Wind Power Forecasting Models," Energies, MDPI, vol. 7(7), pages 1-14, July.
    8. Antonello D’Agostino & Kieran Mcquinn & Karl Whelan, 2012. "Are Some Forecasters Really Better Than Others?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(4), pages 715-732, June.
    9. Cavit Pakel & Neil Shephard & Kevin Sheppard, 2009. "Nuisance parameters, composite likelihoods and a panel of GARCH models," Economics Papers 2009-W12, Economics Group, Nuffield College, University of Oxford.
    10. Christophe Chorro & Florian Ielpo & Benoît Sévi, 2017. "The contribution of jumps to forecasting the density of returns," Post-Print halshs-01442618, HAL.
    11. Carlo Altavilla & Paul De Grauwe, 2010. "Forecasting and combining competing models of exchange rate determination," Applied Economics, Taylor & Francis Journals, vol. 42(27), pages 3455-3480.
    12. Matsumura, Marco & Moreira, Ajax & Vicente, José, 2011. "Forecasting the yield curve with linear factor models," International Review of Financial Analysis, Elsevier, vol. 20(5), pages 237-243.
    13. Carlo Altavilla & Raffaella Giacomini & Giuseppe Ragusa, 2017. "Anchoring the yield curve using survey expectations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(6), pages 1055-1068, September.
    14. Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2020. "Forecasting realized oil-price volatility: The role of financial stress and asymmetric loss," Journal of International Money and Finance, Elsevier, vol. 104(C).
    15. Castro, Luciano de & Galvao, Antonio F. & Kim, Jeong Yeol & Montes-Rojas, Gabriel & Olmo, Jose, 2022. "Experiments on portfolio selection: A comparison between quantile preferences and expected utility decision models," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 97(C).
    16. Vitek, Francis, 2006. "Measuring the Stance of Monetary Policy in a Small Open Economy: A Dynamic Stochastic General Equilibrium Approach," MPRA Paper 802, University Library of Munich, Germany.
    17. Grace Lee Ching Yap, 2020. "Optimal Filter Approximations for Latent Long Memory Stochastic Volatility," Computational Economics, Springer;Society for Computational Economics, vol. 56(2), pages 547-568, August.
    18. Xiaojie Xu, 2017. "The rolling causal structure between the Chinese stock index and futures," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 31(4), pages 491-509, November.
    19. Xilong Chen & Eric Ghysels, 2011. "News--Good or Bad--and Its Impact on Volatility Predictions over Multiple Horizons," The Review of Financial Studies, Society for Financial Studies, vol. 24(1), pages 46-81, October.
    20. Faria, Gonçalo & Verona, Fabio, 2023. "Forecast combination in the frequency domain," Bank of Finland Research Discussion Papers 1/2023, Bank of Finland.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:5:y:2012:i:11:p:4711-4725:d:21544. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.