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Long-Term Demand Forecasting in a Scenario of Energy Transition

Author

Listed:
  • Rafael Sánchez-Durán

    (Endesa, Av. de la Borbolla, 41004 Sevilla, Spain)

  • Joaquín Luque

    (Tecnología Electrónica, Universidad de Sevilla, Av, Reina Mercedes s/n, 41004 Sevilla, Spain)

  • Julio Barbancho

    (Tecnología Electrónica, Universidad de Sevilla, Av, Reina Mercedes s/n, 41004 Sevilla, Spain)

Abstract

The energy transition from fossil fuels to carbon-free sources will be a big challenge in the coming decades. In this context, the long-term prediction of energy demand plays a key role in planning energy infrastructures and in adopting economic and energy policies. In this article, we aimed to forecast energy demand for Spain, mainly employing econometrics techniques. From information obtained from institutional databases, energy demand was decomposed into many factors and economy-related activity sectors, obtaining a set of disaggregated sequences of time-dependent values. Using time-series techniques, a long-term prediction was then obtained for each component. Finally, every element was aggregated to obtain the final long-term energy demand forecast. For the year 2030, an energy demand equivalent to 82 million tons of oil was forecast. Due to improvements in energy efficiency in the post-crisis period, a decoupling of economy and energy demand was obtained, with a 30% decrease in energy intensity for the period 2005–2030. World future scenarios show a significant increase in energy demand due to human development of less developed economies. For Spain, our research concluded that energy demand will remain stable in the next decade, despite the foreseen 2% annual growth of the nation’s economy. Despite the enormous energy concentration and density of fossil fuels, it will not be affordable to use them to supply energy demand in the future. The consolidation of renewable energies and increasing energy efficiency is the only way to satisfy the planet’s energy needs.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:16:p:3095-:d:256899
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    References listed on IDEAS

    as
    1. Syed Aziz Ur Rehman & Yanpeng Cai & Rizwan Fazal & Gordhan Das Walasai & Nayyar Hussain Mirjat, 2017. "An Integrated Modeling Approach for Forecasting Long-Term Energy Demand in Pakistan," Energies, MDPI, vol. 10(11), pages 1-23, November.
    2. Pérez-García, Julián & Moral-Carcedo, Julián, 2016. "Analysis and long term forecasting of electricity demand trough a decomposition model: A case study for Spain," Energy, Elsevier, vol. 97(C), pages 127-143.
    3. Ciulla, Giuseppina & Lo Brano, Valerio & D’Amico, Antonino, 2016. "Modelling relationship among energy demand, climate and office building features: A cluster analysis at European level," Applied Energy, Elsevier, vol. 183(C), pages 1021-1034.
    4. Hall, Lisa M.H. & Buckley, Alastair R., 2016. "A review of energy systems models in the UK: Prevalent usage and categorisation," Applied Energy, Elsevier, vol. 169(C), pages 607-628.
    5. Shailendra Singh & Abdulsalam Yassine, 2018. "Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting," Energies, MDPI, vol. 11(2), pages 1-26, February.
    6. Ang, B.W & Zhang, F.Q & Choi, Ki-Hong, 1998. "Factorizing changes in energy and environmental indicators through decomposition," Energy, Elsevier, vol. 23(6), pages 489-495.
    7. Consolación Quintana-Rojo & Fernando E. Callejas-Albiñana & Miguel-Angel Tarancón & Pablo del Río, 2019. "Identifying the Drivers of Wind Capacity Additions: The Case of Spain. A Multiequational Approach," Energies, MDPI, vol. 12(10), pages 1-19, May.
    8. 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.
    9. Mustafa Akpinar & Nejat Yumusak, 2016. "Year Ahead Demand Forecast of City Natural Gas Using Seasonal Time Series Methods," Energies, MDPI, vol. 9(9), pages 1-17, September.
    10. Ang, B.W. & Liu, F.L. & Chung, Hyun-Sik, 2004. "A generalized Fisher index approach to energy decomposition analysis," Energy Economics, Elsevier, vol. 26(5), pages 757-763, September.
    11. Wei Sun & Hua Cai & Yuwei Wang, 2018. "Refined Laspeyres Decomposition-Based Analysis of Relationship between Economy and Electric Carbon Productivity from the Provincial Perspective—Development Mode and Policy," Energies, MDPI, vol. 11(12), pages 1-20, December.
    12. Seunghyoung Ryu & Jaekoo Noh & Hongseok Kim, 2016. "Deep Neural Network Based Demand Side Short Term Load Forecasting," Energies, MDPI, vol. 10(1), pages 1-20, December.
    13. Mario Gómez & Aitor Ciarreta & Ainhoa Zarraga, 2018. "Linear and Nonlinear Causality between Energy Consumption and Economic Growth: The Case of Mexico 1965–2014," Energies, MDPI, vol. 11(4), pages 1-15, March.
    14. Fadi Abdelradi & Teresa Serra, 2015. "Asymmetric price volatility transmission between food and energy markets: The case of Spain," Agricultural Economics, International Association of Agricultural Economists, vol. 46(4), pages 503-513, July.
    15. 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.
    16. Salah Bouktif & Ali Fiaz & Ali Ouni & Mohamed Adel Serhani, 2018. "Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †," Energies, MDPI, vol. 11(7), pages 1-20, June.
    17. Cansino, José M. & Sánchez-Braza, Antonio & Rodríguez-Arévalo, María L., 2015. "Driving forces of Spain׳s CO2 emissions: A LMDI decomposition approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 749-759.
    18. Sungkyun Ha & Sungho Tae & Rakhyun Kim, 2019. "Energy Demand Forecast Models for Commercial Buildings in South Korea," Energies, MDPI, vol. 12(12), pages 1-19, June.
    19. Hong, Tao & Pinson, Pierre & Fan, Shu & Zareipour, Hamidreza & Troccoli, Alberto & Hyndman, Rob J., 2016. "Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond," International Journal of Forecasting, Elsevier, vol. 32(3), pages 896-913.
    20. 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.
    21. 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.
    22. Ang, B.W. & Liu, F.L., 2001. "A new energy decomposition method: perfect in decomposition and consistent in aggregation," Energy, Elsevier, vol. 26(6), pages 537-548.
    23. Jenne, C. A. & Cattell, R. K., 1983. "Structural change and energy efficiency in industry," Energy Economics, Elsevier, vol. 5(2), pages 114-123, April.
    24. Ang, B.W. & Zhang, F.Q., 2000. "A survey of index decomposition analysis in energy and environmental studies," Energy, Elsevier, vol. 25(12), pages 1149-1176.
    25. Sarah Gelper & Roland Fried & Christophe Croux, 2010. "Robust forecasting with exponential and Holt-Winters smoothing," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(3), pages 285-300.
    26. de Oliveira, Erick Meira & Cyrino Oliveira, Fernando Luiz, 2018. "Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods," Energy, Elsevier, vol. 144(C), pages 776-788.
    27. Komi Nagbe & Jairo Cugliari & Julien Jacques, 2018. "Short-Term Electricity Demand Forecasting Using a Functional State Space Model," Energies, MDPI, vol. 11(5), pages 1-24, May.
    28. Duscha, Vicki & Fougeyrollas, Arnaud & Nathani, Carsten & Pfaff, Matthias & Ragwitz, Mario & Resch, Gustav & Schade, Wolfgang & Breitschopf, Barbara & Walz, Rainer, 2016. "Renewable energy deployment in Europe up to 2030 and the aim of a triple dividend," Energy Policy, Elsevier, vol. 95(C), pages 314-323.
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    2. Beatriz M. Paredes-Sánchez & José P. Paredes-Sánchez & Paulino J. García-Nieto, 2020. "Energy Multiphase Model for Biocoal Conversion Systems by Means of a Nodal Network," Energies, MDPI, vol. 13(11), pages 1-13, May.
    3. Vincent Le & Joshua Ramirez & Miltiadis Alamaniotis, 2021. "Intelligent Room-Based Identification of Electricity Consumption with an Ensemble Learning Method in Smart Energy," Energies, MDPI, vol. 14(20), pages 1-13, October.
    4. Aldona Kluczek & Patrycja Żegleń & Daniela Matušíková, 2021. "The Use of Prospect Theory for Energy Sustainable Industry 4.0," Energies, MDPI, vol. 14(22), pages 1-29, November.
    5. Jun-Lin Lin & Yiqing Zhang & Kunhuang Zhu & Binbin Chen & Feng Zhang, 2020. "Asymmetric Loss Functions for Contract Capacity Optimization," Energies, MDPI, vol. 13(12), pages 1-13, June.

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