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Accuracy indicators for evaluating retrospective performance of energy system models

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  • Wen, Xin
  • Jaxa-Rozen, Marc
  • Trutnevyte, Evelina

Abstract

Retrospective evaluation of energy system models and scenarios is essential for ensuring their robustness for prospective policy support. However, quantitative evaluations currently lack systematic methods to be more holistic and informative. This paper reviews existing accuracy indicators used for retrospective evaluations of energy models and scenarios with the aim to find a small suite of complementary indicators. We quantify and compare 24 indicators to assess the retrospective performance of D-EXPANSE electricity sector modeling framework, used to model 31 European countries in parallel from 1990–2019. We find that symmetric mean percentage error, symmetric mean absolute percentage error, symmetric median absolute percentage error, root-mean-squared logarithmic error, and growth error together form the most informative suite of indicators. This study is the first step towards developing a model accuracy testbench to assess energy models and scenarios in multiple dimensions retrospectively.

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  • Wen, Xin & Jaxa-Rozen, Marc & Trutnevyte, Evelina, 2022. "Accuracy indicators for evaluating retrospective performance of energy system models," Applied Energy, Elsevier, vol. 325(C).
    • Handle: RePEc:eee:appene:v:325:y:2022:i:c:s0306261922011667
    DOI: 10.1016/j.apenergy.2022.119906
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    as
    1. Labandeira, Xavier & Labeaga, José M. & López-Otero, Xiral, 2017. "A meta-analysis on the price elasticity of energy demand," Energy Policy, Elsevier, vol. 102(C), pages 549-568.
    2. McConnell, Dylan & Hearps, Patrick & Eales, Dominic & Sandiford, Mike & Dunn, Rebecca & Wright, Matthew & Bateman, Lachlan, 2013. "Retrospective modeling of the merit-order effect on wholesale electricity prices from distributed photovoltaic generation in the Australian National Electricity Market," Energy Policy, Elsevier, vol. 58(C), pages 17-27.
    3. Fujimori, Shinichiro & Dai, Hancheng & Masui, Toshihiko & Matsuoka, Yuzuru, 2016. "Global energy model hindcasting," Energy, Elsevier, vol. 114(C), pages 293-301.
    4. Huntington, Hillard G., 2021. "Model evaluation for policy insights: Reflections on the forum process," Energy Policy, Elsevier, vol. 156(C).
    5. 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.
    6. Qudrat-Ullah, Hassan, 2017. "How to enhance the future use of energy policy simulation models through ex post validation," Energy, Elsevier, vol. 120(C), pages 58-66.
    7. van Ruijven, Bas & de Vries, Bert & van Vuuren, Detlef P. & van der Sluijs, Jeroen P., 2010. "A global model for residential energy use: Uncertainty in calibration to regional data," Energy, Elsevier, vol. 35(1), pages 269-282.
    8. Richard Loulou & Maryse Labriet, 2008. "ETSAP-TIAM: the TIMES integrated assessment model Part I: Model structure," Computational Management Science, Springer, vol. 5(1), pages 7-40, February.
    9. Goodwin, Paul & Lawton, Richard, 1999. "On the asymmetry of the symmetric MAPE," International Journal of Forecasting, Elsevier, vol. 15(4), pages 405-408, October.
    10. Li, Francis G.N. & Trutnevyte, Evelina, 2017. "Investment appraisal of cost-optimal and near-optimal pathways for the UK electricity sector transition to 2050," Applied Energy, Elsevier, vol. 189(C), pages 89-109.
    11. 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.
    12. Chris Tofallis, 2015. "A better measure of relative prediction accuracy for model selection and model estimation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(8), pages 1352-1362, August.
    13. Fischer, Carolyn & Herrnstadt, Evan & Morgenstern, Richard, 2009. "Understanding errors in EIA projections of energy demand," Resource and Energy Economics, Elsevier, vol. 31(3), pages 198-209, August.
    14. Evan D. Sherwin & Max Henrion & Inês M. L. Azevedo, 2018. "Estimation of the year-on-year volatility and the unpredictability of the United States energy system," Nature Energy, Nature, vol. 3(4), pages 341-346, April.
    15. Abosedra, Salah & Baghestani, Hamid, 2004. "On the predictive accuracy of crude oil futures prices," Energy Policy, Elsevier, vol. 32(12), pages 1389-1393, August.
    16. Linderoth, Hans, 2002. "Forecast errors in IEA-countries' energy consumption," Energy Policy, Elsevier, vol. 30(1), pages 53-61, January.
    17. Liao, Hua & Cai, Jia-Wei & Yang, Dong-Wei & Wei, Yi-Ming, 2016. "Why did the historical energy forecasting succeed or fail? A case study on IEA's projection," Technological Forecasting and Social Change, Elsevier, vol. 107(C), pages 90-96.
    18. Meira, Erick & Cyrino Oliveira, Fernando Luiz & de Menezes, Lilian M., 2021. "Point and interval forecasting of electricity supply via pruned ensembles," Energy, Elsevier, vol. 232(C).
    19. Richard Loulou, 2008. "ETSAP-TIAM: the TIMES integrated assessment model. part II: mathematical formulation," Computational Management Science, Springer, vol. 5(1), pages 41-66, February.
    20. Siddons, Craig & Allan, Grant & McIntyre, Stuart, 2015. "How accurate are forecasts of costs of energy? A methodological contribution," Energy Policy, Elsevier, vol. 87(C), pages 224-228.
    21. Hillard G. Huntington, 1994. "Oil Price Forecasting in the 1980s: What Went Wrong?," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 1-22.
    22. Makridakis, Spyros, 1993. "Accuracy measures: theoretical and practical concerns," International Journal of Forecasting, Elsevier, vol. 9(4), pages 527-529, December.
    23. Glotin, David & Bourgeois, Cyril & Giraudet, Louis-Gaëtan & Quirion, Philippe, 2019. "Prediction is difficult, even when it's about the past: A hindcast experiment using Res-IRF, an integrated energy-economy model," Energy Economics, Elsevier, vol. 84(S1).
    24. Steinbuks, Jevgenijs, 2019. "Assessing the accuracy of electricity production forecasts in developing countries," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1175-1185.
    25. Jan-Philipp Sasse & Evelina Trutnevyte, 2020. "Regional impacts of electricity system transition in Central Europe until 2035," Nature Communications, Nature, vol. 11(1), pages 1-14, December.
    26. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    27. Katherine Calvin & Marshall Wise & Page Kyle & Leon Clarke & Jae Edmonds, 2017. "A Hindcast Experiment Using The Gcam 3.0 Agriculture And Land-Use Module," Climate Change Economics (CCE), World Scientific Publishing Co. Pte. Ltd., vol. 8(01), pages 1-21, February.
    28. Trutnevyte, Evelina, 2016. "Does cost optimization approximate the real-world energy transition?," Energy, Elsevier, vol. 106(C), pages 182-193.
    29. al Irsyad, Muhammad Indra & Halog, Anthony & Nepal, Rabindra, 2019. "Renewable energy projections for climate change mitigation: An analysis of uncertainty and errors," Renewable Energy, Elsevier, vol. 130(C), pages 536-546.
    30. O'Neill, Brian C. & Desai, Mausami, 2005. "Accuracy of past projections of US energy consumption," Energy Policy, Elsevier, vol. 33(8), pages 979-993, May.
    31. Müller, Jonas & Trutnevyte, Evelina, 2020. "Spatial projections of solar PV installations at subnational level: Accuracy testing of regression models," Applied Energy, Elsevier, vol. 265(C).
    32. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
    33. Groissböck, Markus & Pickl, Matthias J., 2016. "An analysis of the power market in Saudi Arabia: Retrospective cost and environmental optimization," Applied Energy, Elsevier, vol. 165(C), pages 548-558.
    34. Gilbert, Alexander Q. & Sovacool, Benjamin K., 2016. "Looking the wrong way: Bias, renewable electricity, and energy modelling in the United States," Energy, Elsevier, vol. 94(C), pages 533-541.
    35. DeCarolis, Joseph F. & Hunter, Kevin & Sreepathi, Sarat, 2012. "The case for repeatable analysis with energy economy optimization models," Energy Economics, Elsevier, vol. 34(6), pages 1845-1853.
    36. Trutnevyte, Evelina & McDowall, Will & Tomei, Julia & Keppo, Ilkka, 2016. "Energy scenario choices: Insights from a retrospective review of UK energy futures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 326-337.
    37. Bentzen, J. & Linderoth, H., 2001. "Has the accuracy of energy demand projections in the OECD countries improved since the 1970s?," Papers 01-5, Aarhus School of Business - Department of Economics.
    38. Manzoor, Davood & Aryanpur, Vahid, 2017. "Power sector development in Iran: A retrospective optimization approach," Energy, Elsevier, vol. 140(P1), pages 330-339.
    39. Hua Liao & Jia-Wei Cai & Dong-Wei Yang & Yi-Ming Wei, 2016. "Why did the historical energy forecasting succeed or fail? A case study on IEA¡¯s projection," CEEP-BIT Working Papers 92, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
    40. Priesmann, Jan & Nolting, Lars & Praktiknjo, Aaron, 2019. "Are complex energy system models more accurate? An intra-model comparison of power system optimization models," Applied Energy, Elsevier, vol. 255(C).
    41. Wachtmeister, Henrik & Henke, Petter & Höök, Mikael, 2018. "Oil projections in retrospect: Revisions, accuracy and current uncertainty," Applied Energy, Elsevier, vol. 220(C), pages 138-153.
    42. Vaclav Smil, 2008. "Long-range energy forecasts are no more than fairy tales," Nature, Nature, vol. 453(7192), pages 154-154, May.
    43. Winebrake, James J. & Sakva, Denys, 2006. "An evaluation of errors in US energy forecasts: 1982-2003," Energy Policy, Elsevier, vol. 34(18), pages 3475-3483, December.
    44. Bashmakov, Igor, 2007. "Three laws of energy transitions," Energy Policy, Elsevier, vol. 35(7), pages 3583-3594, July.
    45. Armstrong, J. Scott & Collopy, Fred, 1992. "Error measures for generalizing about forecasting methods: Empirical comparisons," International Journal of Forecasting, Elsevier, vol. 8(1), pages 69-80, June.
    46. Chris Tofallis, 2015. "A better measure of relative prediction accuracy for model selection and model estimation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(3), pages 524-524, March.
    47. Felix Creutzig & Peter Agoston & Jan Christoph Goldschmidt & Gunnar Luderer & Gregory Nemet & Robert C. Pietzcker, 2017. "The underestimated potential of solar energy to mitigate climate change," Nature Energy, Nature, vol. 2(9), pages 1-9, September.
    48. Jing Meng & Rupert Way & Elena Verdolini & Laura Diaz Anadon, 2021. "Comparing expert elicitation and model-based probabilistic technology cost forecasts for the energy transition," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 118(27), pages 1917165118-, July.
    49. 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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    Cited by:

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    4. Wen, Xin & Jaxa-Rozen, Marc & Trutnevyte, Evelina, 2023. "Hindcasting to inform the development of bottom-up electricity system models: The cases of endogenous demand and technology learning," Applied Energy, Elsevier, vol. 340(C).
    5. 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.

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