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Forecasting Long-term Electricity Demand: Evolution from Experience-Based Techniques to Sophisticated Artificial Intelligence (AI) Models

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Computational Management

Part of the book series: Modeling and Optimization in Science and Technologies ((MOST,volume 18))

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Abstract

Demand forecasting is one of the primary activities in the planning phase of any business and a key input to many crucial business decisions. It forms the basis of answering managerial questions such as how much raw materials to procure, how much resources to allocate, or how much to invest. Typically, demand for any product or service is stochastic and is linked to various extrinsic factors ranging from socio-economic conditions of the economy to consumers’ taste and perception, and intrinsic factors like quality and value proposition of the product or service itself. The earliest attempts for electricity demand forecasting were of short-term and, based on wisdom, experience, and speculations of the vertically integrated electricity utilities, even before separate system operators came into practice. The first set of formalized methods made use of Trend Analysis, Econometrics, and End-Use Approaches. These methods, when applied individually, are prone to inherent errors; attempts were made to improve them or combine two or more of them to design hybrid techniques. With the realization that these methods are incapable of adequately capturing the variabilities of demand over time, emphasis began to be given on computationally intelligent methods. Subsequently, with advances in scientific knowledge and computational capabilities, smarter and more intelligent algorithms started being progressively used in this field. This chapter presents a systematic evolution of these methods with their merits, demerits and applicability criteria along with an outline on the shift from stand-alone demand forecasting to an integrated energy system modelling approach.

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References

  1. Livewire, World Bank Group (2017) Forecasting electricity demand: an aid for practitioners

    Google Scholar 

  2. Singh A, Pratap M, Das, Sharma PA, Gupta KK et al (2019) Regulatory framework for long-term demand forecasting and power procurement planning. Centre for Energy Regulation (CER), IITK; ISBN 978-93-5321-969-7

    Google Scholar 

  3. Makkonen M, Patari S, Jantunen A, Viljainen S et al (2012) Competition in the European electricity markets-outcomes of a Delphi study. Energy Policy 44:431–440

    Google Scholar 

  4. Linstone HA, Turoff M (2002) The Delphi method techniques and applications. Addison-Wesley Publishing Company, Advanced Book Program, ISBN 0-201-04294-0

    Google Scholar 

  5. Paul AC, Myers EC, Palmer KL et al (2009) A partial adjustment model of US electricity demand by region, season, and sector. Resources for the Future Discussion Paper No. 08-50. https://doi.org/10.2139/ssrn.1372228

  6. Zellner A (1962) An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias. J Am Stat Assoc 57(298):348–368

    Google Scholar 

  7. Khan MA., Khan MZ, Zaman K, Arif M (2014) Global estimates of energy-growth nexus: application of seemingly unrelated regressions. Renew Sustain Energy Rev 29: 63–71. https://doi.org/10.1016/j.rser.2013.08.088

  8. Mitchell TM (1997) Machine learning. McGraw-Hill, New York, ISBN: 978-0-07-042807-2

    Google Scholar 

  9. Box GEP (1976) Science and statistics. J Am Stat Assoc 71(356):791–799. https://doi.org/10.1080/01621459.1976.10480949

  10. Central Electricity Authority (2019) Long term electricity semand forecasting. New Delhi

    Google Scholar 

  11. Lipinski AJ (1990) 2.0. Introduction. Energy. https://doi.org/10.1016/0360-5442(90)90084-F

  12. Duch W, Mańdziuk J (eds) Challenges for computational intelligence. Studies in computational intelligence, vol 63. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71984-7_1

  13. He Y, Jiao J, Chen Q, Ge S, Chang Y, Xu Y et al (2017) Urban long term electricity demand forecast method based on system dynamics of the new economic normal: the case of Tianjin. Energy 133:9–22

    Google Scholar 

  14. Angelopoulos D, Siskos Y, Psarras J (2019) Disaggregating time series on multiple criteria for robust forecasting: the case of long-term electricity demand in Greece. Eur J Oper Res 275(1):252–265

    Google Scholar 

  15. Mirjat NH, Uqaili MA, Harijan K, Walasai GD, Mondal MA, Sahin H et al (2018) Long-term electricity demand forecast and supply side scenarios for Pakistan (2015–2050): a LEAP model application for policy analysis. Energy 165:512–526

    Google Scholar 

  16. IPCC, 2018: Global warming of 1.5 °C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty [Masson-Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T. Waterfield (eds)]

    Google Scholar 

  17. Ringkjob HK, Haugan PM, Solbrekke IM et al (2018) A review of modelling tools for energy and electricity systems with large shares of variable renewables. Renew Sustain Energy Rev 96:440–459

    Google Scholar 

  18. Prina MG, Manzolini G, Moser D, Nastasi B, Sparber W et al (2020) Classification and challenges of bottom-up energy system models—a review. Renew Sustain Energy Rev 129:109917

    Google Scholar 

  19. Fattahi A, Sijm J, Faaij A et al (2020) A systemic approach to analyze integrated energy system modeling tools: a review of national models. Renew Sustain Energy Rev 133:110195

    Google Scholar 

  20. Lai CS, Locatelli G, Pimm A, Wu X, Lai LL et al (2020) A review on long-term electrical power system modeling with energy storage. J Cleaner Prod 124298

    Google Scholar 

  21. MoP (2020) Power sector at a glance ALL INDIA. Available at: https://powermin.nic.in/en/content/power-sector-glance-all-india; accessed on 31 May 2020

  22. HLEC, GoI (2018) Report of the high level empowered committee to address the issues of stressed thermal power projects. High Level Empowered Committee constituted by Govt. of India on 29 July 2018

    Google Scholar 

  23. Pérez-García J, Moral-Carcedo J (2016) Analysis and long term forecasting of electricity demand trough a decomposition model: a case study for Spain. Energy 97:127–143

    Google Scholar 

  24. Torrini FC, Souza RC, Oliveira FL, Pessanha JF et al (2016) Long term electricity consumption forecast in Brazil: a fuzzy logic approach. Socioecon Plann Sci 54:18–27

    Google Scholar 

  25. Pessanha JF, Leon N (2015) Forecasting long-term electricity demand in the residential sector. Proc Comput Sci 55:529–538

    Google Scholar 

  26. Bianco V, Manca O, Nardini S (2009) Electricity consumption forecasting in Italy using linear regression models. Energy 34(9):1413–1421

    Google Scholar 

  27. Mohamed Z, Bodger P (2005) Forecasting electricity consumption in New Zealand using economic and demographic variables. Energy 30(10):1833–1843

    Google Scholar 

  28. da Silva FL, Oliveira FL, Souza RC (2019) A bottom-up bayesian extension for long term electricity consumption forecasting. Energy 167:198–210

    Google Scholar 

  29. Ardakani FJ, Ardehali MM (2014) Long-term electrical energy consumption forecasting for developing and developed economies based on different optimized models and historical data types. Energy 65:452–461

    Google Scholar 

  30. Chen T, Wang YC (2012) Long-term load forecasting by a collaborative fuzzy-neural approach. Int J Electr Power Energy Syst 43(1):454–464

    Google Scholar 

  31. Melodi AO, Momoh JA, Adeyanju OM et al (2016) Probabilistic long term load forecast for Nigerian bulk power transmission system expansion planning. In: IEEE PES PowerAfrica, pp 301–305. https://doi.org/10.1109/PowerAfrica.2016.7556621

  32. Dalvand MM, Azami SB, Tarimoradi H et al (2008) Long-term load forecasting of Iranian power grid using fuzzy and artificial neural networks. In: 43rd International Universities Power Engineering Conference. IEEE, pp 1–4. https://doi.org/10.1109/UPEC.2008.4651538

  33. Daneshi H, Shahidehpour M, Choobbari AL (2008) Long-term load forecasting in electricity market. In: IEEE International Conference on Electro/Information Technology. IEEE, pp 395–400. https://doi.org/10.1109/EIT.2008.4554335

  34. Towill S (1974) Estimation of maximum demand on a British electricity-board system. Forecast periods of 1–3 years. Proc Inst Electrical Eng 121(7):609–615

    Google Scholar 

  35. Aslan Y, Yavasca S, Yasar C (2011) Long term electric peak load forecasting of Kutahya using different approaches. Int J Tech Phys Problems Eng 3(2):87–91

    Google Scholar 

  36. Hyndman RJ, Fan S (2009) Density forecasting for long-term peak electricity demand. IEEE Trans Power Syst 25(2):1142–1153

    Google Scholar 

  37. Boroojeni KG, Amini MH, Bahrami S, Iyengar SS, Sarwat AI, Karabasoglu O et al (2017) A novel multi-time-scale modeling for electric power demand forecasting: from short-term to medium-term horizon. Electric Power Syst Res 142:58–73

    Google Scholar 

  38. Pillai GG, Putrus GA, Pearsall NM et al (2014) Generation of synthetic benchmark electrical load profiles using publicly available load and weather data. Int J Electr Power Energy Syst 61:1

    Google Scholar 

  39. Goude Y, Nedellec R, Kong N (2014) Local short and middle term electricity load forecasting with semi-parametric additive models. IEEE Trans Smart Grid 5(1):440–446

    Google Scholar 

  40. He Y, Jiao J, Chen Q, Ge S, Chang Y, Xu Y et al (2017) Urban long term electricity demand forecast method based on system dynamics of the new economic normal: the case of Tianjin. Energy 133:9–22

    Google Scholar 

  41. Zhao H, Guo S (2016) An optimized grey model for annual power load forecasting. Energy 107:272–286

    Google Scholar 

  42. Lindberg KB, Doorman G (2013) Hourly load modelling of non-residential building stock. In: IEEE Grenoble Conference France. IEEE, pp 1–6

    Google Scholar 

  43. Lindberg KB, Doorman G, Chacon JE, Fischer D et al (2015) Hourly electricity load modelling of non-residential passive buildings in a nordic climate. In: IEEE Eindhoven PowerTech. IEEE, pp 1–6

    Google Scholar 

  44. Veldman E, Gibescu M, Slootweg H, Kling WL et al (2011) Impact of electrification of residential heating on loading of distribution networks. In: IEEE Trondheim PowerTech. IEEE, pp 1–7

    Google Scholar 

  45. Bruninx K, Patteeuw D, Delarue E, Helsen L, D'haeseleer W et al (2012) Short-term demand response of flexible electric heating systems: the need for integrated simulations. In: 10th international conference on the European Energy Market (EEM). IEEE, pp 1–10

    Google Scholar 

  46. Fischer D, Wolf T, Wapler J, Hollinger R, Madani H et al (2017) Model-based flexibility assessment of a residential heat pump pool. Energy 18:853–864

    Google Scholar 

  47. Fischer D, Stephen B, Flunk A, Kreifels N, Lindberg KB, Wille-Haussmann B, Owens EH et al (2016) Modeling the effects of variable tariffs on domestic electric load profiles by use of occupant behavior submodels. IEEE Trans Smart Grid 8(6):2685–2693

    Google Scholar 

  48. Ericson T (2009) Direct load control of residential water heaters. Energy Policy 37(9):3502–3512

    Google Scholar 

  49. Baetens R, De Coninck R, Van Roy J, Verbruggen B, Driesen J, Helsen L, Saelens D et al (2012) Assessing electrical bottlenecks at feeder level for residential net zero-energy buildings by integrated system simulation. Appl Energy 96:74–83

    Google Scholar 

  50. Asare-Bediako B, Kling WL, Ribeiro PF et al (2014) Future residential load profiles: scenario-based analysis of high penetration of heavy loads and distributed generation. Energy Build 75:228–238

    Google Scholar 

  51. Pantoš M (2011) Stochastic optimal charging of electric-drive vehicles with renewable energy. Energy 36(11):6567–6576

    Google Scholar 

  52. Andersson SL, Elofsson AK, Galus MD, Göransson L, Karlsson S, Johnsson F, Andersson G et al (2010) Plug-in hybrid electric vehicles as regulating power providers: case studies of Sweden and Germany. Energy Policy 38(6):2751–2762

    Google Scholar 

  53. Boßmann T, Lickert F, Elsland R, Wietschel M et al (2013) The German load curve in 2050: structural changes through energy efficiency measures and their impacts on the electricity supply side. In: ECEEE Summer Study Proceedings, pp 1199–1211

    Google Scholar 

  54. Boßmann T, Lickert F, Elsland R, Wietschel M et al (2013) The German load curve in 2050: structural changes through energy efficiency measures and their impacts on the electricity supply side. In: ECEEE Summer Study Proceedings, pp 1199–1211

    Google Scholar 

  55. Moral-Carcedo J, Pérez-García J (2017) Integrating long-term economic scenarios into peak load forecasting: an application to Spain. Energy 140:682–695

    Google Scholar 

  56. Andersen FM, Baldini M, Hansen LG, Jensen CL et al (2017) Households’ hourly electricity consumption and peak demand in Denmark. Appl Energy 208:607–619

    Google Scholar 

  57. Veldman E, Gibescu M, Slootweg HJ, Kling WL et al (2013) Scenario-based modelling of future residential electricity demands and assessing their impact on distribution grids. Energy Policy 56:233–247

    Google Scholar 

  58. Lindberg, KB (2017) Impact of zero energy buildings on the power system—a study of load profiles, flexibility and system investments. Doctoral Thesis. Norwegian University of Science and Technology (NTNU) Retrieved. https://hdl.handle.net/11250/2450566

  59. Lindberg KB, Dyrendahl T, Doorman G, Korpås M, Øyslebø E, Endresen H, Skotland CH et al (2016) Large scale introduction of zero energy buildings in the nordic power system. In: 13th International Conference on the European Energy Market (EEM). IEEE, pp 1–6

    Google Scholar 

  60. Statnett SF (2018) Forbruksprognose Stor-Oslo. Retrieved. https://www.statnett.no/globalassets/for-aktorer-i-kraftsystemet/planer-og-analyser/2018-Forbruksprognose-Stor-Oslo

  61. Bertsch J, Growitsch C, Lorenczik S, Nagl S et al (2016) Flexibility in Europe’s power sector—an additional requirement or an automatic complement? Energy Econ 53:118–131

    Google Scholar 

  62. Gils HC (2014) Assessment of the theoretical demand response potential in Europe. Energy 67:1–8

    Google Scholar 

  63. Pina A, Silva C, Ferrão P et al (2012) The impact of demand side management strategies in the penetration of renewable electricity. Energy 41(1):128–137

    Google Scholar 

  64. Pina A, Baptista P, Silva C, Ferrão P et al (2014) Energy reduction potential from the shift to electric vehicles: the Flores island case study. Energy Policy 67:37–47

    Google Scholar 

  65. Lund H, Kempton W (2008) Integration of renewable energy into the transport and electricity sectors through V2G. Energy Policy 36(9):3578–3587

    Google Scholar 

  66. Graabak I, Wu Q, Warland L, Liu Z et al (2016) Optimal planning of the Nordic transmission system with 100% electric vehicle penetration of passenger cars by 2050. Energy 107:648–660

    Google Scholar 

  67. Juul N, Meibom P (2011) Optimal configuration of an integrated power and transport system. Energy 36(5):3523–3530

    Google Scholar 

  68. Hedegaard K, Ravn H, Juul N, Meibom P et al (2012) Effects of electric vehicles on power systems in Northern Europe. Energy 48(1):356–368

    Google Scholar 

  69. Ringkjob HK, Haugan PM, Solbrekke IM et al (2018) A review of modelling tools for energy and electricity systems with large shares of variable renewables. Renew Sustain Energy Rev 96:440–459

    Google Scholar 

  70. FINGRID, Landsnet, Svenska_Kraftnat, Statnett, Energinet, dk et al (2014) Nordic grid development plan 2014. Retrieved. https://www.statnett.no/Global/Dokumenter/Media/Nyheter2014/NordicGridDevelopmentPlan.pdf

  71. Bøhnsdalen ET et al (2016) Long term market analysis. The Nordic Region and Europe 2016–2040. https://www.accenture.com/_acnmedia/Accenture/next-gen/top-tenchallenges/challenge10/pdfs/Accenture-2016-Top-10-Challenges-10-Market-Data.pdf

  72. Lindberg KB, Seljom P, Madsen H, Fischer D, Korpås M (2019) Long-term electricity load forecasting: current and future trends. Utilities Policy 58(C):102–119, Elsevier

    Google Scholar 

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Authors and Affiliations

  1. Department of Management Studies, Indian Institute of Science, Bengaluru, India

    Abhishek Das

  2. School of Management Studies, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, India

    Somen Dey

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  1. Abhishek Das
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  2. Somen Dey
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Correspondence to Abhishek Das .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, Faculty of Engineering, SOA University, Bhubaneswar, Odisha, India

    Srikanta Patnaik

  2. Strategic Management and International Business, Tokyo Institute of Technology, Tokyo, Japan

    Kayhan Tajeddini

  3. School of Management, Victoria University of Wellington, Wellington, New Zealand

    Vipul Jain

Appendix 1

Appendix 1

See Table 27.1.

Table 27.1 Existing literature on electricity demand forecasting
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Das, A., Dey, S. (2021). Forecasting Long-term Electricity Demand: Evolution from Experience-Based Techniques to Sophisticated Artificial Intelligence (AI) Models. In: Patnaik, S., Tajeddini, K., Jain, V. (eds) Computational Management. Modeling and Optimization in Science and Technologies, vol 18. Springer, Cham. https://doi.org/10.1007/978-3-030-72929-5_27

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