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Analysis and long term forecasting of electricity demand trough a decomposition model: A case study for Spain

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Cited by:

  1. Yu, Miao & Zhao, Xintong & Gao, Yuning, 2019. "Factor decomposition of China’s industrial electricity consumption using structural decomposition analysis," Structural Change and Economic Dynamics, Elsevier, vol. 51(C), pages 67-76.
  2. Deguo Su & Beibei Tan & Anbing Zhang & Yikai Hou, 2023. "Analysis of the Influencing Factors of Power Demand in Beijing Based on the LMDI Model," Sustainability, MDPI, vol. 15(10), pages 1-16, May.
  3. Alexopoulos, Thomas A., 2017. "The growing importance of natural gas as a predictor for retail electricity prices in US," Energy, Elsevier, vol. 137(C), pages 219-233.
  4. David I. Okorie, 2021. "A network analysis of electricity demand and the cryptocurrency markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 3093-3108, April.
  5. Nunes, Juliana Barbosa & Mahmoudi, Nadali & Saha, Tapan Kumar & Chattopadhyay, Debabrata, 2018. "A stochastic integrated planning of electricity and natural gas networks for Queensland, Australia considering high renewable penetration," Energy, Elsevier, vol. 153(C), pages 539-553.
  6. Yi Liang & Dongxiao Niu & Ye Cao & Wei-Chiang Hong, 2016. "Analysis and Modeling for China’s Electricity Demand Forecasting Using a Hybrid Method Based on Multiple Regression and Extreme Learning Machine: A View from Carbon Emission," Energies, MDPI, vol. 9(11), pages 1-22, November.
  7. Farrokhifar, Meisam & Nie, Yinghui & Pozo, David, 2020. "Energy systems planning: A survey on models for integrated power and natural gas networks coordination," Applied Energy, Elsevier, vol. 262(C).
  8. Atul Anand & L Suganthi, 2018. "Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand," Energies, MDPI, vol. 11(4), pages 1-15, March.
  9. Jinchai Lin & Kaiwei Zhu & Zhen Liu & Jenny Lieu & Xianchun Tan, 2019. "Study on A Simple Model to Forecast the Electricity Demand under China’s New Normal Situation," Energies, MDPI, vol. 12(11), pages 1-28, June.
  10. García-Gusano, Diego & Suárez-Botero, Jasson & Dufour, Javier, 2018. "Long-term modelling and assessment of the energy-economy decoupling in Spain," Energy, Elsevier, vol. 151(C), pages 455-466.
  11. 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.
  12. Angelopoulos, Dimitrios & Siskos, Yannis & Psarras, John, 2019. "Disaggregating time series on multiple criteria for robust forecasting: The case of long-term electricity demand in Greece," European Journal of Operational Research, Elsevier, vol. 275(1), pages 252-265.
  13. Santos, Maria João & Ferreira, Paula & Araújo, Madalena, 2016. "A methodology to incorporate risk and uncertainty in electricity power planning," Energy, Elsevier, vol. 115(P2), pages 1400-1411.
  14. Shi, Kaifang & Yu, Bailang & Huang, Chang & Wu, Jianping & Sun, Xiufeng, 2018. "Exploring spatiotemporal patterns of electric power consumption in countries along the Belt and Road," Energy, Elsevier, vol. 150(C), pages 847-859.
  15. Oscar Trull & Angel Peiró-Signes & J. Carlos García-Díaz, 2019. "Electricity Forecasting Improvement in a Destination Using Tourism Indicators," Sustainability, MDPI, vol. 11(13), pages 1-16, July.
  16. Fan, Jing-Li & Hu, Jia-Wei & Zhang, Xian, 2019. "Impacts of climate change on electricity demand in China: An empirical estimation based on panel data," Energy, Elsevier, vol. 170(C), pages 880-888.
  17. Silva, Felipe L.C. & Souza, Reinaldo C. & Cyrino Oliveira, Fernando L. & Lourenco, Plutarcho M. & Calili, Rodrigo F., 2018. "A bottom-up methodology for long term electricity consumption forecasting of an industrial sector - Application to pulp and paper sector in Brazil," Energy, Elsevier, vol. 144(C), pages 1107-1118.
  18. Chi Zhang & Zhengning Pu & Jiasha Fu, 2018. "The Recurrence Interval Difference of Power Load in Heavy/Light Industries of China," Energies, MDPI, vol. 11(1), pages 1-20, January.
  19. Akbal, Yıldırım & Ünlü, Kamil Demirberk, 2022. "A univariate time series methodology based on sequence-to-sequence learning for short to midterm wind power production," Renewable Energy, Elsevier, vol. 200(C), pages 832-844.
  20. Rafael Sánchez-Durán & Joaquín Luque & Julio Barbancho, 2019. "Long-Term Demand Forecasting in a Scenario of Energy Transition," Energies, MDPI, vol. 12(16), pages 1-23, August.
  21. Paul Anton Verwiebe & Stephan Seim & Simon Burges & Lennart Schulz & Joachim Müller-Kirchenbauer, 2021. "Modeling Energy Demand—A Systematic Literature Review," Energies, MDPI, vol. 14(23), pages 1-58, November.
  22. da Silva, Felipe L.C. & Cyrino Oliveira, Fernando L. & Souza, Reinaldo C., 2019. "A bottom-up bayesian extension for long term electricity consumption forecasting," Energy, Elsevier, vol. 167(C), pages 198-210.
  23. Pedro J. Zarco-Periñán & Irene M. Zarco-Soto & Fco. Javier Zarco-Soto, 2021. "Influence of the Population Density of Cities on Energy Consumption of Their Households," Sustainability, MDPI, vol. 13(14), pages 1-15, July.
  24. Santiago, I. & Moreno-Munoz, A. & Quintero-Jiménez, P. & Garcia-Torres, F. & Gonzalez-Redondo, M.J., 2021. "Electricity demand during pandemic times: The case of the COVID-19 in Spain," Energy Policy, Elsevier, vol. 148(PA).
  25. 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.
  26. Ribó-Pérez, David & Van der Weijde, Adriaan H. & Álvarez-Bel, Carlos, 2019. "Effects of self-generation in imperfectly competitive electricity markets: The case of Spain," Energy Policy, Elsevier, vol. 133(C).
  27. Kaboli, S. Hr. Aghay & Selvaraj, J. & Rahim, N.A., 2016. "Long-term electric energy consumption forecasting via artificial cooperative search algorithm," Energy, Elsevier, vol. 115(P1), pages 857-871.
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