IDEAS home Printed from https://ideas.repec.org/r/taf/jnlasa/v108y2013i501p7-21.html
   My bibliography  Save this item

Modeling and Forecasting Daily Electricity Load Curves: A Hybrid Approach

Citations

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


Cited by:

  1. 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.
  2. Brenda López Cabrera & Franziska Schulz, 2017. "Forecasting Generalized Quantiles of Electricity Demand: A Functional Data Approach," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 127-136, January.
  3. Amara-Ouali, Yvenn & Fasiolo, Matteo & Goude, Yannig & Yan, Hui, 2023. "Daily peak electrical load forecasting with a multi-resolution approach," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1272-1286.
  4. 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.
  5. Bessec, Marie & Fouquau, Julien, 2018. "Short-run electricity load forecasting with combinations of stationary wavelet transforms," European Journal of Operational Research, Elsevier, vol. 264(1), pages 149-164.
  6. Mingotti, Nicola & Lillo Rodríguez, Rosa Elvira & Romo, Juan, 2015. "A Random Walk Test for Functional Time Series," DES - Working Papers. Statistics and Econometrics. WS ws1506, Universidad Carlos III de Madrid. Departamento de Estadística.
  7. Pontus Netzell & Hussain Kazmi & Konstantinos Kyprianidis, 2024. "Deriving Input Variables through Applied Machine Learning for Short-Term Electric Load Forecasting in Eskilstuna, Sweden," Energies, MDPI, vol. 17(10), pages 1-19, May.
  8. Abdelmonaem Jornaz & V. A. Samaranayake, 2019. "A Multi-Step Approach to Modeling the 24-hour Daily Profiles of Electricity Load using Daily Splines," Energies, MDPI, vol. 12(21), pages 1-22, November.
  9. Xu, Xiuqin & Chen, Ying & Goude, Yannig & Yao, Qiwei, 2021. "Day-ahead probabilistic forecasting for French half-hourly electricity loads and quantiles for curve-to-curve regression," LSE Research Online Documents on Economics 120774, London School of Economics and Political Science, LSE Library.
  10. 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.
  11. Zhou, Kaile & Fu, Chao & Yang, Shanlin, 2016. "Big data driven smart energy management: From big data to big insights," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 215-225.
  12. Xu, Xiuqin & Chen, Ying & Goude, Yannig & Yao, Qiwei, 2021. "Day-ahead probabilistic forecasting for French half-hourly electricity loads and quantiles for curve-to-curve regression," Applied Energy, Elsevier, vol. 301(C).
  13. 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.
  14. Salahuddin Khan, 2023. "Short-Term Electricity Load Forecasting Using a New Intelligence-Based Application," Sustainability, MDPI, vol. 15(16), pages 1-12, August.
  15. Arora, Siddharth & Taylor, James W., 2018. "Rule-based autoregressive moving average models for forecasting load on special days: A case study for France," European Journal of Operational Research, Elsevier, vol. 266(1), pages 259-268.
  16. Cees Diks & Bram Wouters, 2023. "Noise reduction for functional time series," Papers 2307.02154, arXiv.org.
  17. Elías, Antonio & Jiménez, Raúl & Shang, Han Lin, 2022. "On projection methods for functional time series forecasting," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
  18. 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.
  19. 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).
  20. Jin-Li Hu & Nhi Ha Bao Bui, 2024. "The Future Design of Smart Energy Systems with Energy Flexumers: A Constructive Literature Review," Energies, MDPI, vol. 17(9), pages 1-32, April.
  21. Suqi Zhang & Ningjing Zhang & Ziqi Zhang & Ying Chen, 2022. "Electric Power Load Forecasting Method Based on a Support Vector Machine Optimized by the Improved Seagull Optimization Algorithm," Energies, MDPI, vol. 15(23), pages 1-17, December.
  22. Guo, Shaojun & Qiao, Xinghao, 2023. "On consistency and sparsity for high-dimensional functional time series with application to autoregressions," LSE Research Online Documents on Economics 114638, London School of Economics and Political Science, LSE Library.
  23. VandenHeuvel, Daniel & Wu, Jinran & Wang, You-Gan, 2023. "Robust regression for electricity demand forecasting against cyberattacks," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1573-1592.
  24. 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.
  25. 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.
  26. Ping-Huan Kuo & Chiou-Jye Huang, 2018. "A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting," Energies, MDPI, vol. 11(1), pages 1-13, January.
  27. Fang, Qin & Guo, Shaojun & Qiao, Xinghao, 2022. "Finite sample theory for high-dimensional functional/scalar time series with applications," LSE Research Online Documents on Economics 114637, London School of Economics and Political Science, LSE Library.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.