IDEAS home Printed from https://ideas.repec.org/a/eee/intfor/v30y2014i2p375-381.html
   My bibliography  Save this article

GEFCom2012: Electric load forecasting and backcasting with semi-parametric models

Author

Listed:
  • Nedellec, Raphael
  • Cugliari, Jairo
  • Goude, Yannig

Abstract

We sum up the methodology of the team tololo for the Global Energy Forecasting Competition 2012: Load Forecasting. Our strategy consisted of a temporal multi-scale model that combines three components. The first component was a long term trend estimated by means of non-parametric smoothing. The second was a medium term component describing the sensitivity of the electricity demand to the temperature at each time step. We use a generalized additive model to fit this component, using calendar information as well. Finally, a short term component models local behaviours. As the factors that drive this component are unknown, we use a random forest model to estimate it.

Suggested Citation

  • Nedellec, Raphael & Cugliari, Jairo & Goude, Yannig, 2014. "GEFCom2012: Electric load forecasting and backcasting with semi-parametric models," International Journal of Forecasting, Elsevier, vol. 30(2), pages 375-381.
    • Handle: RePEc:eee:intfor:v:30:y:2014:i:2:p:375-381
    DOI: 10.1016/j.ijforecast.2013.07.004
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0169207013000800
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijforecast.2013.07.004?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Shu Fan & Rob Hyndman, 2010. "Short-term load forecasting based on a semi-parametric additive model," Monash Econometrics and Business Statistics Working Papers 17/10, Monash University, Department of Econometrics and Business Statistics.
    2. Haeran Cho & Yannig Goude & Xavier Brossat & Qiwei Yao, 2013. "Modeling and Forecasting Daily Electricity Load Curves: A Hybrid Approach," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 7-21, March.
    3. Simon N. Wood, 2011. "Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(1), pages 3-36, January.
    4. Simon N. Wood, 2004. "Stable and Efficient Multiple Smoothing Parameter Estimation for Generalized Additive Models," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 673-686, January.
    5. Cho, Haeran & Goude, Yannig & Brossat, Xavier & Yao, Qiwei, 2013. "Modeling and forecasting daily electricity load curves: a hybrid approach," LSE Research Online Documents on Economics 49634, London School of Economics and Political Science, LSE Library.
    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. Ren'e Aid & Andrea Cosso & Huy^en Pham, 2020. "Equilibrium price in intraday electricity markets," Papers 2010.09285, arXiv.org.
    2. Tang, Ling & Wu, Yao & Yu, Lean, 2018. "A randomized-algorithm-based decomposition-ensemble learning methodology for energy price forecasting," Energy, Elsevier, vol. 157(C), pages 526-538.
    3. Monika Zimmermann & Florian Ziel, 2024. "Efficient mid-term forecasting of hourly electricity load using generalized additive models," Papers 2405.17070, arXiv.org.
    4. Tartakovsky, Alexandre M. & Ma, Tong & Barajas-Solano, David A. & Tipireddy, Ramakrishna, 2023. "Physics-informed Gaussian process regression for states estimation and forecasting in power grids," International Journal of Forecasting, Elsevier, vol. 39(2), pages 967-980.
    5. Yan Hong Chen & Wei-Chiang Hong & Wen Shen & Ning Ning Huang, 2016. "Electric Load Forecasting Based on a Least Squares Support Vector Machine with Fuzzy Time Series and Global Harmony Search Algorithm," Energies, MDPI, vol. 9(2), pages 1-13, January.
    6. Jingrui Xie & Tao Hong, 2017. "Wind Speed for Load Forecasting Models," Sustainability, MDPI, vol. 9(5), pages 1-12, May.
    7. Shao, Zhen & Chao, Fu & Yang, Shan-Lin & Zhou, Kai-Le, 2017. "A review of the decomposition methodology for extracting and identifying the fluctuation characteristics in electricity demand forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 123-136.
    8. Bidong Liu & Jakub Nowotarski & Tao Hong & Rafal Weron, 2015. "Probabilistic load forecasting via Quantile Regression Averaging on sister forecasts," HSC Research Reports HSC/15/01, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    9. Luo, Jian & Hong, Tao & Fang, Shu-Cherng, 2018. "Benchmarking robustness of load forecasting models under data integrity attacks," International Journal of Forecasting, Elsevier, vol. 34(1), pages 89-104.
    10. Hong, Tao & Wang, Pu & White, Laura, 2015. "Weather station selection for electric load forecasting," International Journal of Forecasting, Elsevier, vol. 31(2), pages 286-295.
    11. Moreno-Carbonell, Santiago & Sánchez-Úbeda, Eugenio F. & Muñoz, Antonio, 2020. "Rethinking weather station selection for electric load forecasting using genetic algorithms," International Journal of Forecasting, Elsevier, vol. 36(2), pages 695-712.
    12. Wang, Pu & Liu, Bidong & Hong, Tao, 2016. "Electric load forecasting with recency effect: A big data approach," International Journal of Forecasting, Elsevier, vol. 32(3), pages 585-597.
    13. Akouemo, Hermine N. & Povinelli, Richard J., 2016. "Probabilistic anomaly detection in natural gas time series data," International Journal of Forecasting, Elsevier, vol. 32(3), pages 948-956.
    14. René Aid & Andrea Cosso & Huyên Pham, 2022. "Equilibrium price in intraday electricity markets," Mathematical Finance, Wiley Blackwell, vol. 32(2), pages 517-554, April.
    15. Moral-Carcedo, Julián & Pérez-García, Julián, 2019. "Time of day effects of temperature and daylight on short term electricity load," Energy, Elsevier, vol. 174(C), pages 169-183.
    16. Ivana Kiprijanovska & Simon Stankoski & Igor Ilievski & Slobodan Jovanovski & Matjaž Gams & Hristijan Gjoreski, 2020. "HousEEC: Day-Ahead Household Electrical Energy Consumption Forecasting Using Deep Learning," Energies, MDPI, vol. 13(10), pages 1-29, May.
    17. Lin, Yu & Lu, Qin & Tan, Bin & Yu, Yuanyuan, 2022. "Forecasting energy prices using a novel hybrid model with variational mode decomposition," Energy, Elsevier, vol. 246(C).
    18. Seyedeh Narjes Fallah & Mehdi Ganjkhani & Shahaboddin Shamshirband & Kwok-wing Chau, 2019. "Computational Intelligence on Short-Term Load Forecasting: A Methodological Overview," Energies, MDPI, vol. 12(3), pages 1-21, January.
    19. Zhang, Wenjie & Quan, Hao & Srinivasan, Dipti, 2018. "Parallel and reliable probabilistic load forecasting via quantile regression forest and quantile determination," Energy, Elsevier, vol. 160(C), pages 810-819.
    20. Masoud Sobhani & Allison Campbell & Saurabh Sangamwar & Changlin Li & Tao Hong, 2019. "Combining Weather Stations for Electric Load Forecasting," Energies, MDPI, vol. 12(8), pages 1-11, April.
    21. Peña, Juan Ignacio & Rodriguez, Rosa, 2018. "Default supply auctions in electricity markets: Challenges and proposals," Energy Policy, Elsevier, vol. 122(C), pages 142-151.
    22. Hong, Tao & Fan, Shu, 2016. "Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, Elsevier, vol. 32(3), pages 914-938.
    23. Moral-Carcedo, Julián & Pérez-García, Julián, 2017. "Integrating long-term economic scenarios into peak load forecasting: An application to Spain," Energy, Elsevier, vol. 140(P1), pages 682-695.

    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. Cho, Haeran & Goude, Yannig & Brossat, Xavier & Yao, Qiwei, 2013. "Modeling and forecasting daily electricity load curves: a hybrid approach," LSE Research Online Documents on Economics 49634, London School of Economics and Political Science, LSE Library.
    2. Souhaib Ben Taieb & Raphael Huser & Rob J. Hyndman & Marc G. Genton, 2015. "Probabilistic time series forecasting with boosted additive models: an application to smart meter data," Monash Econometrics and Business Statistics Working Papers 12/15, Monash University, Department of Econometrics and Business Statistics.
    3. repec:cte:wsrepe:ws1506 is not listed on IDEAS
    4. Longhi, Christian & Musolesi, Antonio & Baumont, Catherine, 2014. "Modeling structural change in the European metropolitan areas during the process of economic integration," Economic Modelling, Elsevier, vol. 37(C), pages 395-407.
    5. E. Zanini & E. Eastoe & M. J. Jones & D. Randell & P. Jonathan, 2020. "Flexible covariate representations for extremes," Environmetrics, John Wiley & Sons, Ltd., vol. 31(5), August.
    6. Daniel Melser & Robert J. Hill, 2019. "Residential Real Estate, Risk, Return and Diversification: Some Empirical Evidence," The Journal of Real Estate Finance and Economics, Springer, vol. 59(1), pages 111-146, July.
    7. repec:hum:wpaper:sfb649dp2014-030 is not listed on IDEAS
    8. Maciej Berȩsewicz & Dagmara Nikulin, 2021. "Estimation of the size of informal employment based on administrative records with non‐ignorable selection mechanism," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(3), pages 667-690, June.
    9. Wang, Bo & Deng, Nana & Li, Haoxiang & Zhao, Wenhui & Liu, Jie & Wang, Zhaohua, 2021. "Effect and mechanism of monetary incentives and moral suasion on residential peak-hour electricity usage," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    10. Adam R. Pines & Bart Larsen & Zaixu Cui & Valerie J. Sydnor & Maxwell A. Bertolero & Azeez Adebimpe & Aaron F. Alexander-Bloch & Christos Davatzikos & Damien A. Fair & Ruben C. Gur & Raquel E. Gur & H, 2022. "Dissociable multi-scale patterns of development in personalized brain networks," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    11. Daniel Melser, 2017. "Residential Real Estate, Risk, Return and Home Characteristics: Evidence from Sydney 2002-14," ERES eres2017_296, European Real Estate Society (ERES).
    12. Jean Chung & Guanchao Tong & Jiayou Chao & Wei Zhu, 2021. "Path Analysis of Sea-Level Rise and Its Impact," Stats, MDPI, vol. 5(1), pages 1-14, December.
    13. Lozinskaia, Agata & Redkina, Anastasiia & Shenkman, Evgeniia, 2020. "Electricity consumption forecasting for integrated power system with seasonal patterns," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 60, pages 5-25.
    14. Cees Diks & Bram Wouters, 2023. "Noise reduction for functional time series," Papers 2307.02154, arXiv.org.
    15. Moral-Carcedo, Julián & Pérez-García, Julián, 2017. "Integrating long-term economic scenarios into peak load forecasting: An application to Spain," Energy, Elsevier, vol. 140(P1), pages 682-695.
    16. Jahanpour, Ehsan & Ko, Hoo Sang & Nof, Shimon Y., 2016. "Collaboration protocols for sustainable wind energy distribution networks," International Journal of Production Economics, Elsevier, vol. 182(C), pages 496-507.
    17. Phung Khanh Lam & Dong Thi Hoai Tam & Nguyen Minh Dung & Nguyen Thi Hanh Tien & Nguyen Tan Thanh Kieu & Cameron Simmons & Jeremy Farrar & Bridget Wills & Marcel Wolbers, 2015. "A Prognostic Model for Development of Profound Shock among Children Presenting with Dengue Shock Syndrome," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-13, May.
    18. Øystein Sørensen & Anders M. Fjell & Kristine B. Walhovd, 2023. "Longitudinal Modeling of Age-Dependent Latent Traits with Generalized Additive Latent and Mixed Models," Psychometrika, Springer;The Psychometric Society, vol. 88(2), pages 456-486, June.
    19. Gerhard Tutz & Jan Gertheiss, 2014. "Rating Scales as Predictors—The Old Question of Scale Level and Some Answers," Psychometrika, Springer;The Psychometric Society, vol. 79(3), pages 357-376, July.
    20. Sandra Bilek-Steindl & Christian Glocker & Serguei Kaniovski & Thomas Url, 2016. "Austria 2025 – The Effect of Human Capital Accumulation on Output Growth," WIFO Studies, WIFO, number 59175.
    21. Hübler, Michael & Bukin, Eduard & Xi, Yuting, 2020. "The effects of international trade on structural change and CO₂ emissions," Kiel Working Papers 2174, Kiel Institute for the World Economy (IfW Kiel).
    22. Musolesi Antonio & Mazzanti Massimiliano, 2014. "Nonlinearity, heterogeneity and unobserved effects in the carbon dioxide emissions-economic development relation for advanced countries," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 18(5), pages 521-541, December.

    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:eee:intfor:v:30:y:2014:i:2:p:375-381. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijforecast .

    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.