IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i20p6568-d654676.html
   My bibliography  Save this article

Interpretable Forecasting of Energy Demand in the Residential Sector

Author

Listed:
  • Nikos Sakkas

    (Department of Mechanical Engineering, Hellenic Mediterranean University, 71410 Heraklion, Greece)

  • Sofia Yfanti

    (Department of Mechanical Engineering, Hellenic Mediterranean University, 71410 Heraklion, Greece)

  • Costas Daskalakis

    (Apintech Ltd., POLIS-21 Group, Spatharikou 5 Str., 4004 Limassol, Cyprus)

  • Eduard Barbu

    (Institute of Computer Science, University of Tartu, Narva mnt 18, 51009 Tartu, Estonia)

  • Marharyta Domnich

    (Institute of Computer Science, University of Tartu, Narva mnt 18, 51009 Tartu, Estonia)

Abstract

Energy demand forecasting is practiced in several time frames; different explanatory variables are used in each case to serve different decision support mandates. For example, in the short, daily, term building level, forecasting may serve as a performance baseline. On the other end, we have long-term, policy-oriented forecasting exercises. TIMES (an acronym for The Integrated Markal Efom System) allows us to model supply and anticipated technology shifts over a long-term horizon, often extending as far away in time as 2100. Between these two time frames, we also have a mid-term forecasting time frame, that of a few years ahead. Investigations here are aimed at policy support, although in a more mid-term horizon, we address issues such as investment planning and pricing. In this paper, we develop and evaluate statistical and neural network approaches for this mid-term forecasting of final energy and electricity for the residential sector in six EU countries (Germany, the Netherlands, Sweden, Spain, Portugal and Greece). Various possible approaches to model the explanatory variables used are presented, discussed, and assessed as to their suitability. Our end goal extends beyond model accuracy; we also include interpretability and counterfactual concepts and analysis, aiming at the development of a modelling approach that can provide decision support for strategies aimed at influencing energy demand.

Suggested Citation

  • Nikos Sakkas & Sofia Yfanti & Costas Daskalakis & Eduard Barbu & Marharyta Domnich, 2021. "Interpretable Forecasting of Energy Demand in the Residential Sector," Energies, MDPI, vol. 14(20), pages 1-17, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:20:p:6568-:d:654676
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/20/6568/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/20/6568/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dilaver, Zafer & Hunt, Lester C, 2011. "Modelling and forecasting Turkish residential electricity demand," Energy Policy, Elsevier, vol. 39(6), pages 3117-3127, June.
    2. 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.
    3. Burke, Paul J. & Kurniawati, Sandra, 2018. "Electricity subsidy reform in Indonesia: Demand-side effects on electricity use," Energy Policy, Elsevier, vol. 116(C), pages 410-421.
    4. Wang, Nan & Mogi, Gento, 2017. "Industrial and residential electricity demand dynamics in Japan: How did price and income elasticities evolve from 1989 to 2014?," Energy Policy, Elsevier, vol. 106(C), pages 233-243.
    5. Nakajima, Tadahiro & Hamori, Shigeyuki, 2010. "Change in consumer sensitivity to electricity prices in response to retail deregulation: A panel empirical analysis of the residential demand for electricity in the United States," Energy Policy, Elsevier, vol. 38(5), pages 2470-2476, May.
    6. Chang, Yoosoon & Kim, Chang Sik & Miller, J. Isaac & Park, Joon Y. & Park, Sungkeun, 2014. "Time-varying Long-run Income and Output Elasticities of Electricity Demand with an Application to Korea," Energy Economics, Elsevier, vol. 46(C), pages 334-347.
    7. Mori, Keibun, 2012. "Modeling the impact of a carbon tax: A trial analysis for Washington State," Energy Policy, Elsevier, vol. 48(C), pages 627-639.
    8. Yoosoon Chang & Chang Sik Kim & J. Isaac Miller & Joon Y. Park & Sungkeun Park, 2014. "Time-varying Long-run Income and Output Elasticities of Electricity Demand," Working Papers 1409, Department of Economics, University of Missouri.
    9. Wei, Taoyuan & Liu, Yang, 2017. "Estimation of global rebound effect caused by energy efficiency improvement," Energy Economics, Elsevier, vol. 66(C), pages 27-34.
    10. Berkhout, Peter H. G. & Muskens, Jos C. & W. Velthuijsen, Jan, 2000. "Defining the rebound effect," Energy Policy, Elsevier, vol. 28(6-7), pages 425-432, June.
    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. Aras, Serkan & Hanifi Van, M., 2022. "An interpretable forecasting framework for energy consumption and CO2 emissions," Applied Energy, Elsevier, vol. 328(C).

    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. Pereira Uhr, Daniel de Abreu & Squarize Chagas, André Luis & Ziero Uhr, Júlia Gallego, 2019. "Estimation of elasticities for electricity demand in Brazilian households and policy implications," Energy Policy, Elsevier, vol. 129(C), pages 69-79.
    2. Wang, Nan & Mogi, Gento, 2017. "Industrial and residential electricity demand dynamics in Japan: How did price and income elasticities evolve from 1989 to 2014?," Energy Policy, Elsevier, vol. 106(C), pages 233-243.
    3. Aslam, Misbah & Ahmad, Eatzaz, 2023. "Untangling electricity demand elasticities: Insights from heterogeneous household groups in Pakistan," Energy, Elsevier, vol. 282(C).
    4. Daniel de Abreu Pereira Uhr & Júlia Gallego Ziero Uhr, André Luis Squarize Chagas, 2017. "Estimation of price and income elasticities for the Brazilian household electricity demand," Working Papers, Department of Economics 2017_12, University of São Paulo (FEA-USP).
    5. Yasunobu Wakashiro, 2019. "Estimating price elasticity of demand for electricity: the case of Japanese manufacturing industry," International Journal of Economic Policy Studies, Springer, vol. 13(1), pages 173-191, January.
    6. Yuo-Hsien Shiau & Su-Fen Yang & Rishan Adha & Syamsiyatul Muzayyanah, 2022. "Modeling Industrial Energy Demand in Relation to Subsector Manufacturing Output and Climate Change: Artificial Neural Network Insights," Sustainability, MDPI, vol. 14(5), pages 1-18, March.
    7. Lin, Boqiang & Wang, Yao, 2020. "Analyzing the elasticity and subsidy to reform the residential electricity tariffs in China," International Review of Economics & Finance, Elsevier, vol. 67(C), pages 189-206.
    8. Hyo-Jin Kim & Gyeong-Sam Kim & Seung-Hoon Yoo, 2019. "Demand Function for Industrial Electricity: Evidence from South Korean Manufacturing Sector," Sustainability, MDPI, vol. 11(18), pages 1-11, September.
    9. Wang, Banban & Wei, Jie & Tan, Xiujie & Su, Bin, 2021. "The sectorally heterogeneous and time-varying price elasticities of energy demand in China," Energy Economics, Elsevier, vol. 102(C).
    10. Liddle, Brantley & Smyth, Russell & Zhang, Xibin, 2020. "Time-varying income and price elasticities for energy demand: Evidence from a middle-income panel," Energy Economics, Elsevier, vol. 86(C).
    11. Hyo-Jin Kim & Jae-Sung Paek & Seung-Hoon Yoo, 2019. "Price Elasticity of Heat Demand in South Korean Manufacturing Sector: An Empirical Investigation," Sustainability, MDPI, vol. 11(21), pages 1-10, November.
    12. Aryanpur, V. & Ghahremani, M. & Mamipour, S. & Fattahi, M. & Ó Gallachóir, B. & Bazilian, M.D. & Glynn, J., 2022. "Ex-post analysis of energy subsidy removal through integrated energy systems modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    13. Hortay, Olivér & Szőke, Tamás, 2019. "Keresleti árrugalmasság becslése a magyar villamosenergia-piacon [Estimating demand-price elasticity on the Hungarian electric energy market]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(7), pages 788-804.
    14. Daniel Morais de Souza & Rogerio Silva de Mattos & Alexandre Zanini, 2022. "Estimating Elasticities for the Residential Demand of Electricity in Brazil Using Cointegration Models," International Journal of Energy Economics and Policy, Econjournals, vol. 12(2), pages 315-324, March.
    15. Tiwari, Aviral Kumar & Menegaki, Angeliki N., 2019. "A time varying approach on the price elasticity of electricity in India during 1975–2013," Energy, Elsevier, vol. 183(C), pages 385-397.
    16. Soon, Byung Min & Thompson, Wyatt, 2017. "Testing for Persistent Japanese Beef Trade Impacts from BSE Using a Time-Varying Armington Model," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 259150, Agricultural and Applied Economics Association.
    17. Chang, Yoosoon & Kim, Chang Sik & Miller, J. Isaac & Park, Joon Y. & Park, Sungkeun, 2016. "A new approach to modeling the effects of temperature fluctuations on monthly electricity demand," Energy Economics, Elsevier, vol. 60(C), pages 206-216.
    18. Jeyhun I. Mikayilov & Fakhri J. Hasanov & Carlo A. Bollino & Ceyhun Mahmudlu, 2017. "Modeling of Electricity Demand for Azerbaijan: Time-Varying Coefficient Cointegration Approach," Energies, MDPI, vol. 10(11), pages 1-12, November.
    19. Ozturk, Ilhan & Arisoy, Ibrahim, 2016. "An estimation of crude oil import demand in Turkey: Evidence from time-varying parameters approach," Energy Policy, Elsevier, vol. 99(C), pages 174-179.
    20. Asuamah Yeboah, Samuel, 2018. "Do government activities determine electricity consumption in Ghana? An empirical investigation," MPRA Paper 89408, University Library of Munich, Germany.

    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:gam:jeners:v:14:y:2021:i:20:p:6568-:d:654676. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.