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A bottom-up bayesian extension for long term electricity consumption forecasting

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  • da Silva, Felipe L.C.
  • Cyrino Oliveira, Fernando L.
  • Souza, Reinaldo C.

Abstract

Long term electricity consumption forecasting has been extensively investigated in recent years in different countries due to its economic and social importance. In this context, the long term electricity consumption projections of a country or region are highly relevant for decision-making of companies and organizations operating in any energy system. In this paper, it is proposed a methodology that combines the bottom-up approach with hierarchical linear models for long term electricity consumption forecasting of a particular industrial sector considering energy efficiency scenarios. In addition, the Bayesian inference is used for model parameter estimation and, enabling the inclusion of uncertainty in the forecasts produced by the model. The model was applied to the Brazilian pulp and paper industry and it was able to capture the trajectory of the real consumption observed during the 2008–2014 period. The model was also used to generate long term point and probability distribution forecasts for the period ranging from 2015 until 2050.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:167:y:2019:i:c:p:198-210
    DOI: 10.1016/j.energy.2018.10.201
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    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. Pao, Hsiao-Tien & Yu, Hsiao-Cheng & Yang, Yeou-Herng, 2011. "Modeling the CO2 emissions, energy use, and economic growth in Russia," Energy, Elsevier, vol. 36(8), pages 5094-5100.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. Rajbhandari, Ashish & Zhang, Fan, 2018. "Does energy efficiency promote economic growth? Evidence from a multicountry and multisectoral panel dataset," Energy Economics, Elsevier, vol. 69(C), pages 128-139.
    19. 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.
    20. Worrell, Ernst & Price, Lynn, 2001. "Policy scenarios for energy efficiency improvement in industry," Energy Policy, Elsevier, vol. 29(14), pages 1223-1241, November.
    21. 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.
    22. 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.
    23. 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.
    24. 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.
    25. Wene, C.-O., 1996. "Energy-economy analysis: Linking the macroeconomic and systems engineering approaches," Energy, Elsevier, vol. 21(9), pages 809-824.
    26. 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.
    27. 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.
    28. Giraldo, Luis & Hyman, Barry, 1995. "Energy end-use models for pulp, paper, and paperboard mills," Energy, Elsevier, vol. 20(10), pages 1005-1019.
    29. 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.
    30. 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.
    31. 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.
    32. 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.
    33. 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.
    34. 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.
    35. 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.
    36. 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.
    37. 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.
    38. 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.
    39. Hong, Tao & Fan, Shu, 2016. "Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, Elsevier, vol. 32(3), pages 914-938.
    40. 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.
    41. Giraldo, Luis & Hyman, Barry, 1996. "An energy process-step model for manufacturing paper and paperboard," Energy, Elsevier, vol. 21(7), pages 667-681.
    42. 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.
    43. 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.
    44. 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.
    45. 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.
    46. 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.
    47. 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.
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