IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v144y2018icp1107-1118.html
   My bibliography  Save this article

A bottom-up methodology for long term electricity consumption forecasting of an industrial sector - Application to pulp and paper sector in Brazil

Author

Listed:
  • Silva, Felipe L.C.
  • Souza, Reinaldo C.
  • Cyrino Oliveira, Fernando L.
  • Lourenco, Plutarcho M.
  • Calili, Rodrigo F.

Abstract

Long term annual electricity consumption forecasting is very important for country's energy planning. These forecasts are influenced by several factors (political, technological, social, environmental and economic), and brings with itself a high uncertainty degree in its results and difficulties in the evaluation of such factors over them. A methodology that eases to take into account these factors aiming improve the results and help understanding the electricity consumption annual trajectory till the forecast horizon is, therefore, very much useful and desired. So, we propose a modelling structure using the bottom-up approach to cope with these matters and to evaluate the trajectory of long term annual electricity consumption of a sector of the Brazilian industry up to 2050 considering energy efficiency (EE) scenarios. It is important to emphasize that Brazil is a developing country, and to build a bottom-up approach was a challenge, mainly due to the fact that this model is data intensive. In particular, this modelling was applied in the pulp and paper sector. The main goal was to consider technological diffusion scenarios in EE measures, and show the energy savings achieved. The results point an energy savings in the order of 25% when an actual scenario is considered.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:energy:v:144:y:2018:i:c:p:1107-1118
    DOI: 10.1016/j.energy.2017.12.078
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544217321217
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2017.12.078?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. Koltsaklis, Nikolaos E. & Liu, Pei & Georgiadis, Michael C., 2015. "An integrated stochastic multi-regional long-term energy planning model incorporating autonomous power systems and demand response," Energy, Elsevier, vol. 82(C), pages 865-888.
    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. 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.
    4. Ardakani, F.J. & Ardehali, M.M., 2014. "Long-term electrical energy consumption forecasting for developing and developed economies based on different optimized models and historical data types," Energy, Elsevier, vol. 65(C), pages 452-461.
    5. Hahn, Heiko & Meyer-Nieberg, Silja & Pickl, Stefan, 2009. "Electric load forecasting methods: Tools for decision making," European Journal of Operational Research, Elsevier, vol. 199(3), pages 902-907, December.
    6. Lapillonne, B. & Chateau, B., 1981. "The medee models for long term energy demand forecasting," Socio-Economic Planning Sciences, Elsevier, vol. 15(2), pages 53-58.
    7. He, Yongxiu & Jiao, Jie & Chen, Qian & Ge, Sifan & Chang, Yan & Xu, Yang, 2017. "Urban long term electricity demand forecast method based on system dynamics of the new economic normal: The case of Tianjin," Energy, Elsevier, vol. 133(C), pages 9-22.
    8. Dangerfield, Byron J. & Morris, John S., 1992. "Top-down or bottom-up: Aggregate versus disaggregate extrapolations," International Journal of Forecasting, Elsevier, vol. 8(2), pages 233-241, October.
    9. Perwez, Usama & Sohail, Ahmed & Hassan, Syed Fahad & Zia, Usman, 2015. "The long-term forecast of Pakistan's electricity supply and demand: An application of long range energy alternatives planning," Energy, Elsevier, vol. 93(P2), pages 2423-2435.
    10. Saygin, D. & Patel, M.K. & Worrell, E. & Tam, C. & Gielen, D.J., 2011. "Potential of best practice technology to improve energy efficiency in the global chemical and petrochemical sector," Energy, Elsevier, vol. 36(9), pages 5779-5790.
    11. Worrell, Ernst & Price, Lynn, 2001. "Policy scenarios for energy efficiency improvement in industry," Energy Policy, Elsevier, vol. 29(14), pages 1223-1241, November.
    12. Yi, Bo-Wen & Xu, Jin-Hua & Fan, Ying, 2016. "Inter-regional power grid planning up to 2030 in China considering renewable energy development and regional pollutant control: A multi-region bottom-up optimization model," Applied Energy, Elsevier, vol. 184(C), pages 641-658.
    13. Wierzbowski, Michal & Lyzwa, Wojciech & Musial, Izabela, 2016. "MILP model for long-term energy mix planning with consideration of power system reserves," Applied Energy, Elsevier, vol. 169(C), pages 93-111.
    14. Zellner, Arnold & Tobias, Justin, 1998. "A Note on Aggregation, Disaggregation and Forecasting Performance," CUDARE Working Papers 198677, University of California, Berkeley, Department of Agricultural and Resource Economics.
    15. Geroski, P. A., 2000. "Models of technology diffusion," Research Policy, Elsevier, vol. 29(4-5), pages 603-625, April.
    16. Giraldo, Luis & Hyman, Barry, 1995. "Energy end-use models for pulp, paper, and paperboard mills," Energy, Elsevier, vol. 20(10), pages 1005-1019.
    17. Calili, Rodrigo F. & Souza, Reinaldo C. & Galli, Alain & Armstrong, Margaret & Marcato, André Luis M., 2014. "Estimating the cost savings and avoided CO2 emissions in Brazil by implementing energy efficient policies," Energy Policy, Elsevier, vol. 67(C), pages 4-15.
    18. Athanasopoulos, George & Ahmed, Roman A. & Hyndman, Rob J., 2009. "Hierarchical forecasts for Australian domestic tourism," International Journal of Forecasting, Elsevier, vol. 25(1), pages 146-166.
    19. Lee, Chul-Yong & Huh, Sung-Yoon, 2017. "Forecasting new and renewable energy supply through a bottom-up approach: The case of South Korea," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 207-217.
    20. Jaffe, Adam B. & Stavins, Robert N., 1994. "The energy paradox and the diffusion of conservation technology," Resource and Energy Economics, Elsevier, vol. 16(2), pages 91-122, May.
    21. Farla, Jacco & Blok, Kornelis & Schipper, Lee, 1997. "Energy efficiency developments in the pulp and paper industry : A cross-country comparison using physical production data," Energy Policy, Elsevier, vol. 25(7-9), pages 745-758.
    22. Worrell, Ernst & Laitner, John A & Ruth, Michael & Finman, Hodayah, 2003. "Productivity benefits of industrial energy efficiency measures," Energy, Elsevier, vol. 28(11), pages 1081-1098.
    23. Pao, Hsiao-Tien & Tsai, Chung-Ming, 2011. "Modeling and forecasting the CO2 emissions, energy consumption, and economic growth in Brazil," Energy, Elsevier, vol. 36(5), pages 2450-2458.
    24. Hong, Tao & Fan, Shu, 2016. "Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, Elsevier, vol. 32(3), pages 914-938.
    25. Marc C. Kennedy & Anthony O'Hagan, 2001. "Bayesian calibration of computer models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(3), pages 425-464.
    26. Karali, Nihan & Xu, Tengfang & Sathaye, Jayant, 2014. "Reducing energy consumption and CO2 emissions by energy efficiency measures and international trading: A bottom-up modeling for the U.S. iron and steel sector," Applied Energy, Elsevier, vol. 120(C), pages 133-146.
    27. Giraldo, Luis & Hyman, Barry, 1996. "An energy process-step model for manufacturing paper and paperboard," Energy, Elsevier, vol. 21(7), pages 667-681.
    28. Fleiter, Tobias & Fehrenbach, Daniel & Worrell, Ernst & Eichhammer, Wolfgang, 2012. "Energy efficiency in the German pulp and paper industry – A model-based assessment of saving potentials," Energy, Elsevier, vol. 40(1), pages 84-99.
    29. Cabral, Joilson de Assis & Legey, Luiz Fernando Loureiro & Freitas Cabral, Maria Viviana de, 2017. "Electricity consumption forecasting in Brazil: A spatial econometrics approach," Energy, Elsevier, vol. 126(C), pages 124-131.
    30. Mathews, John A. & Baroni, Paolo, 2013. "The industrial logistic surface: Displaying the impact of energy policy on uptake of new technologies," Energy, Elsevier, vol. 57(C), pages 733-740.
    31. Collins, Dw, 1976. "Predicting Earnings With Sub-Entity Data - Some Further Evidence," Journal of Accounting Research, Wiley Blackwell, vol. 14(1), pages 163-177.
    32. Rodrigo F. Calili & Reinaldo C. Souza & Alain Galli & Margaret Armstrong & André Luis M. Marcato, 2014. "Estimating the cost savings and avoided CO2 emissions in Brazil by implementing energy efficient policies," Post-Print hal-01110915, HAL.
    33. Hyndman, Rob J. & Ahmed, Roman A. & Athanasopoulos, George & Shang, Han Lin, 2011. "Optimal combination forecasts for hierarchical time series," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2579-2589, September.
    34. DeCarolis, Joseph F., 2011. "Using modeling to generate alternatives (MGA) to expand our thinking on energy futures," Energy Economics, Elsevier, vol. 33(2), pages 145-152, March.
    35. Neelis, Maarten & Patel, Martin & Blok, Kornelis & Haije, Wim & Bach, Pieter, 2007. "Approximation of theoretical energy-saving potentials for the petrochemical industry using energy balances for 68 key processes," Energy, Elsevier, vol. 32(7), pages 1104-1123.
    36. 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.
    37. Kinney, Wr, 1971. "Predicting Earnings - Entity Versus Subentity Data," Journal of Accounting Research, Wiley Blackwell, vol. 9(1), pages 127-136.
    38. Klinge Jacobsen, Henrik, 1998. "Integrating the bottom-up and top-down approach to energy-economy modelling: the case of Denmark," Energy Economics, Elsevier, vol. 20(4), pages 443-461, September.
    39. Ang, B. W. & Liu, F. L. & Chew, E. P., 2003. "Perfect decomposition techniques in energy and environmental analysis," Energy Policy, Elsevier, vol. 31(14), pages 1561-1566, November.
    40. Andrea Herbst & Felipe Andrés Toro & Felix Reitze & Eberhard Jochem, 2012. "Introduction to Energy Systems Modelling," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 148(II), pages 111-135, June.
    41. Trutnevyte, Evelina, 2016. "Does cost optimization approximate the real-world energy transition?," Energy, Elsevier, vol. 106(C), pages 182-193.
    42. Xu, Jin-Hua & Fleiter, Tobias & Fan, Ying & Eichhammer, Wolfgang, 2014. "CO2 emissions reduction potential in China’s cement industry compared to IEA’s Cement Technology Roadmap up to 2050," Applied Energy, Elsevier, vol. 130(C), pages 592-602.
    43. Trutnevyte, Evelina, 2013. "EXPANSE methodology for evaluating the economic potential of renewable energy from an energy mix perspective," Applied Energy, Elsevier, vol. 111(C), pages 593-601.
    44. Soytas, Ugur & Sari, Ramazan, 2007. "The relationship between energy and production: Evidence from Turkish manufacturing industry," Energy Economics, Elsevier, vol. 29(6), pages 1151-1165, November.
    45. Thangavelu, Sundar Raj & Khambadkone, Ashwin M. & Karimi, Iftekhar A., 2015. "Long-term optimal energy mix planning towards high energy security and low GHG emission," Applied Energy, Elsevier, vol. 154(C), pages 959-969.
    46. Xu, Jin-Hua & Fleiter, Tobias & Eichhammer, Wolfgang & Fan, Ying, 2012. "Energy consumption and CO2 emissions in China's cement industry: A perspective from LMDI decomposition analysis," Energy Policy, Elsevier, vol. 50(C), pages 821-832.
    47. Huang, Yun-Hsun & Chang, Yi-Lin & Fleiter, Tobias, 2016. "A critical analysis of energy efficiency improvement potentials in Taiwan's cement industry," Energy Policy, Elsevier, vol. 96(C), pages 14-26.
    48. Kaboli, S. Hr. Aghay & Fallahpour, A. & Selvaraj, J. & Rahim, N.A., 2017. "Long-term electrical energy consumption formulating and forecasting via optimized gene expression programming," Energy, Elsevier, vol. 126(C), pages 144-164.
    49. Dai, Hancheng & Mischke, Peggy & Xie, Xuxuan & Xie, Yang & Masui, Toshihiko, 2016. "Closing the gap? Top-down versus bottom-up projections of China’s regional energy use and CO2 emissions," Applied Energy, Elsevier, vol. 162(C), pages 1355-1373.
    50. Hussain, Anwar & Rahman, Muhammad & Memon, Junaid Alam, 2016. "Forecasting electricity consumption in Pakistan: the way forward," Energy Policy, Elsevier, vol. 90(C), pages 73-80.
    51. Swan, Lukas G. & Ugursal, V. Ismet, 2009. "Modeling of end-use energy consumption in the residential sector: A review of modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 1819-1835, October.
    52. Kuramochi, Takeshi & Wakiyama, Takako & Kuriyama, Akihisa, 2017. "Assessment of national greenhouse gas mitigation targets for 2030 through meta-analysis of bottom-up energy and emission scenarios: A case of Japan," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 924-944.
    53. Rao, K. Usha & Kishore, V.V.N., 2010. "A review of technology diffusion models with special reference to renewable energy technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(3), pages 1070-1078, April.
    54. Daniels, B.W. & Van Dril, A.W.N., 2007. "Save production: A bottom-up energy model for Dutch industry and agriculture," Energy Economics, Elsevier, vol. 29(4), pages 847-867, July.
    55. Boßmann, T. & Staffell, I., 2015. "The shape of future electricity demand: Exploring load curves in 2050s Germany and Britain," Energy, Elsevier, vol. 90(P2), pages 1317-1333.
    56. Koopmans, Carl C. & te Velde, Dirk Willem, 2001. "Bridging the energy efficiency gap: using bottom-up information in a top-down energy demand model," Energy Economics, Elsevier, vol. 23(1), pages 57-75, January.
    57. Chateau, B. & Lapillonne, B., 1978. "Long-term energy demand forecasting A new approach," Energy Policy, Elsevier, vol. 6(2), pages 140-157, June.
    58. Guilherme Fracaro & Esa Vakkilainen & Marcelo Hamaguchi & Samuel Nelson Melegari de Souza, 2012. "Energy Efficiency in the Brazilian Pulp and Paper Industry," Energies, MDPI, vol. 5(9), pages 1-23, September.
    59. Fleiter, Tobias & Worrell, Ernst & Eichhammer, Wolfgang, 2011. "Barriers to energy efficiency in industrial bottom-up energy demand models--A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(6), pages 3099-3111, August.
    60. Berntsen, Philip B. & Trutnevyte, Evelina, 2017. "Ensuring diversity of national energy scenarios: Bottom-up energy system model with Modeling to Generate Alternatives," Energy, Elsevier, vol. 126(C), pages 886-898.
    61. Saygin, D. & Worrell, E. & Patel, M.K. & Gielen, D.J., 2011. "Benchmarking the energy use of energy-intensive industries in industrialized and in developing countries," Energy, Elsevier, vol. 36(11), pages 6661-6673.
    62. E. Downey Brill, Jr. & Shoou-Yuh Chang & Lewis D. Hopkins, 1982. "Modeling to Generate Alternatives: The HSJ Approach and an Illustration Using a Problem in Land Use Planning," Management Science, INFORMS, vol. 28(3), pages 221-235, March.
    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. Wu, Lifeng & Gao, Xiaohui & Xiao, Yanli & Yang, Yingjie & Chen, Xiangnan, 2018. "Using a novel multi-variable grey model to forecast the electricity consumption of Shandong Province in China," Energy, Elsevier, vol. 157(C), pages 327-335.
    2. Felipe Leite Coelho da Silva & Kleyton da Costa & Paulo Canas Rodrigues & Rodrigo Salas & Javier Linkolk López-Gonzales, 2022. "Statistical and Artificial Neural Networks Models for Electricity Consumption Forecasting in the Brazilian Industrial Sector," Energies, MDPI, vol. 15(2), pages 1-12, January.
    3. Wu, Wen-Ze & Pang, Haodan & Zheng, Chengli & Xie, Wanli & Liu, Chong, 2021. "Predictive analysis of quarterly electricity consumption via a novel seasonal fractional nonhomogeneous discrete grey model: A case of Hubei in China," Energy, Elsevier, vol. 229(C).
    4. Tiago Silveira Gontijo & Marcelo Azevedo Costa, 2020. "Forecasting Hierarchical Time Series in Power Generation," Energies, MDPI, vol. 13(14), pages 1-17, July.
    5. Zhang, Jinjun & Abbasi, Kashif Raza & Hussain, Khadim & Akram, Sabahat & Alvarado, Rafael & Almulhim, Abdulaziz I., 2022. "Another perspective towards energy consumption factors in Pakistan: Fresh policy insights from novel methodological framework," Energy, Elsevier, vol. 249(C).
    6. Abbasi, Kashif Raza & Shahbaz, Muhammad & Jiao, Zhilun & Tufail, Muhammad, 2021. "How energy consumption, industrial growth, urbanization, and CO2 emissions affect economic growth in Pakistan? A novel dynamic ARDL simulations approach," Energy, Elsevier, vol. 221(C).
    7. Linlin Zhao & Zhansheng Liu & Jasper Mbachu, 2019. "Energy Management through Cost Forecasting for Residential Buildings in New Zealand," Energies, MDPI, vol. 12(15), pages 1-24, July.
    8. Amber, K.P. & Ahmad, R. & Aslam, M.W. & Kousar, A. & Usman, M. & Khan, M.S., 2018. "Intelligent techniques for forecasting electricity consumption of buildings," Energy, Elsevier, vol. 157(C), pages 886-893.
    9. Duca, Victor E.L.A. & Fonseca, Thaís C.O. & Cyrino Oliveira, Fernando Luiz, 2023. "An overview of non-Gaussian state-space models for wind speed data," Energy, Elsevier, vol. 266(C).
    10. Aneeque A. Mir & Mohammed Alghassab & Kafait Ullah & Zafar A. Khan & Yuehong Lu & Muhammad Imran, 2020. "A Review of Electricity Demand Forecasting in Low and Middle Income Countries: The Demand Determinants and Horizons," Sustainability, MDPI, vol. 12(15), pages 1-35, July.
    11. 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.

    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. 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.
    2. Fleiter, Tobias & Worrell, Ernst & Eichhammer, Wolfgang, 2011. "Barriers to energy efficiency in industrial bottom-up energy demand models--A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(6), pages 3099-3111, August.
    3. Fleiter, Tobias & Fehrenbach, Daniel & Worrell, Ernst & Eichhammer, Wolfgang, 2012. "Energy efficiency in the German pulp and paper industry – A model-based assessment of saving potentials," Energy, Elsevier, vol. 40(1), pages 84-99.
    4. Huang, Yun-Hsun & Chang, Yi-Lin & Fleiter, Tobias, 2016. "A critical analysis of energy efficiency improvement potentials in Taiwan's cement industry," Energy Policy, Elsevier, vol. 96(C), pages 14-26.
    5. Aneeque A. Mir & Mohammed Alghassab & Kafait Ullah & Zafar A. Khan & Yuehong Lu & Muhammad Imran, 2020. "A Review of Electricity Demand Forecasting in Low and Middle Income Countries: The Demand Determinants and Horizons," Sustainability, MDPI, vol. 12(15), pages 1-35, July.
    6. Hyndman, Rob J. & Ahmed, Roman A. & Athanasopoulos, George & Shang, Han Lin, 2011. "Optimal combination forecasts for hierarchical time series," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2579-2589, September.
    7. Han Lin Shang, 2017. "Reconciling Forecasts of Infant Mortality Rates at National and Sub-National Levels: Grouped Time-Series Methods," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 36(1), pages 55-84, February.
    8. George Athanasopoulos & Rob J Hyndman & Nikolaos Kourentzes & Anastasios Panagiotelis, 2023. "Forecast Reconciliation: A Review," Monash Econometrics and Business Statistics Working Papers 8/23, Monash University, Department of Econometrics and Business Statistics.
    9. DeCarolis, Joseph & Daly, Hannah & Dodds, Paul & Keppo, Ilkka & Li, Francis & McDowall, Will & Pye, Steve & Strachan, Neil & Trutnevyte, Evelina & Usher, Will & Winning, Matthew & Yeh, Sonia & Zeyring, 2017. "Formalizing best practice for energy system optimization modelling," Applied Energy, Elsevier, vol. 194(C), pages 184-198.
    10. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    11. Changsheng Li & Lei Zhu & Tobias Fleiter, 2014. "Energy Efficiency Potentials in the Chlor-Alkali Sector — A Case Study of Shandong Province in China," Energy & Environment, , vol. 25(3-4), pages 661-686, April.
    12. Jeon, Jooyoung & Panagiotelis, Anastasios & Petropoulos, Fotios, 2019. "Probabilistic forecast reconciliation with applications to wind power and electric load," European Journal of Operational Research, Elsevier, vol. 279(2), pages 364-379.
    13. 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.
    14. Li, Han & Hyndman, Rob J., 2021. "Assessing mortality inequality in the U.S.: What can be said about the future?," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 152-162.
    15. Jan-Philipp Sasse & Evelina Trutnevyte, 2023. "A low-carbon electricity sector in Europe risks sustaining regional inequalities in benefits and vulnerabilities," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    16. Sasse, Jan-Philipp & Trutnevyte, Evelina, 2023. "Cost-effective options and regional interdependencies of reaching a low-carbon European electricity system in 2035," Energy, Elsevier, vol. 282(C).
    17. Athanasopoulos, George & Ahmed, Roman A. & Hyndman, Rob J., 2009. "Hierarchical forecasts for Australian domestic tourism," International Journal of Forecasting, Elsevier, vol. 25(1), pages 146-166.
    18. Ma, Weiwu & Fang, Song & Liu, Gang & Zhou, Ruoyu, 2017. "Modeling of district load forecasting for distributed energy system," Applied Energy, Elsevier, vol. 204(C), pages 181-205.
    19. Price, James & Keppo, Ilkka, 2017. "Modelling to generate alternatives: A technique to explore uncertainty in energy-environment-economy models," Applied Energy, Elsevier, vol. 195(C), pages 356-369.
    20. Berntsen, Philip B. & Trutnevyte, Evelina, 2017. "Ensuring diversity of national energy scenarios: Bottom-up energy system model with Modeling to Generate Alternatives," Energy, Elsevier, vol. 126(C), pages 886-898.

    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:energy:v:144:y:2018:i:c:p:1107-1118. 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.journals.elsevier.com/energy .

    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.