IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v158y2015icp255-262.html
   My bibliography  Save this article

A comparison of grid-level residential electricity demand scenarios in Japan for 2050

Author

Listed:
  • Yamagata, Yoshiki
  • Murakami, Daisuke
  • Seya, Hajime

Abstract

Scenarios of future population at the grid level (e.g., 1km2) is very important for various types of regional planning, including energy planning. Though the most often used method for population projection is the so-called cohort-component method, if our focus is world level, not country or finer level, we must rely on much simpler methods. In this study, we compare the performance of several typical simple population projection methods using the residential electricity demand derived from each population scenario, especially focusing on spatial patterns. We show that different projection methods may produce different spatial patterns such as “compact” or “dispersed” urban form, which is quantified using the Gini coefficient. Also, we show that the projection method for gravity-based residential electricity demand is very flexible in that it can create the most compact and the most dispersed urban form among the focused methods. Furthermore, we point out the usefulness of model ensemble in projection.

Suggested Citation

  • Yamagata, Yoshiki & Murakami, Daisuke & Seya, Hajime, 2015. "A comparison of grid-level residential electricity demand scenarios in Japan for 2050," Applied Energy, Elsevier, vol. 158(C), pages 255-262.
    • Handle: RePEc:eee:appene:v:158:y:2015:i:c:p:255-262
    DOI: 10.1016/j.apenergy.2015.08.079
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261915010156
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2015.08.079?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. Hyndman, Rob J. & Koehler, Anne B. & Snyder, Ralph D. & Grose, Simone, 2002. "A state space framework for automatic forecasting using exponential smoothing methods," International Journal of Forecasting, Elsevier, vol. 18(3), pages 439-454.
    2. Daniel A. Griffith, 2003. "Spatial Autocorrelation and Spatial Filtering," Advances in Spatial Science, Springer, number 978-3-540-24806-4, February.
    3. Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.
    4. Morito Tsutsumi & Daisuke Murakami, 2014. "New Spatial Econometrics–Based Areal Interpolation Method," International Regional Science Review, , vol. 37(3), pages 273-297, July.
    5. Taylor, James W. & Buizza, Roberto, 2003. "Using weather ensemble predictions in electricity demand forecasting," International Journal of Forecasting, Elsevier, vol. 19(1), pages 57-70.
    6. Shlomo Yitzhaki, 1979. "Relative Deprivation and the Gini Coefficient," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 93(2), pages 321-324.
    7. Hideo Shiogama & Seita Emori & Naota Hanasaki & Manabu Abe & Yuji Masutomi & Kiyoshi Takahashi & Toru Nozawa, 2011. "Observational constraints indicate risk of drying in the Amazon basin," Nature Communications, Nature, vol. 2(1), pages 1-7, September.
    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. Sueyoshi, Toshiyuki & Qu, Jingjing & Li, Aijun & Liu, Xiaohong, 2021. "A new approach for evaluating technology inequality and diffusion barriers: The concept of efficiency Gini coefficient and its application in Chinese provinces," Energy, Elsevier, vol. 235(C).
    2. Rauner, Sebastian & Eichhorn, Marcus & Thrän, Daniela, 2016. "The spatial dimension of the power system: Investigating hot spots of Smart Renewable Power Provision," Applied Energy, Elsevier, vol. 184(C), pages 1038-1050.
    3. Rafael Sánchez-Durán & Julio Barbancho & Joaquín Luque, 2019. "Solar Energy Production for a Decarbonization Scenario in Spain," Sustainability, MDPI, vol. 11(24), pages 1-29, December.
    4. Rizzati, Massimiliano & De Cian, Enrica & Guastella, Gianni & Mistry, Malcolm N. & Pareglio, Stefano, 2022. "Residential electricity demand projections for Italy: A spatial downscaling approach," Energy Policy, Elsevier, vol. 160(C).
    5. Daisuke Murakami & Yoshiki Yamagata, 2019. "Estimation of Gridded Population and GDP Scenarios with Spatially Explicit Statistical Downscaling," Sustainability, MDPI, vol. 11(7), pages 1-18, April.
    6. Qiushi Feng & Zhenglian Wang & Simon Choi & Yi Zeng, 2020. "Forecast Households at the County Level: An Application of the ProFamy Extended Cohort-Component Method in Six Counties of Southern California, 2010 to 2040," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 39(2), pages 253-281, April.

    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. Figueiredo, Nuno Carvalho & Silva, Patrícia Pereira da & Bunn, Derek, 2016. "Weather and market specificities in the regional transmission of renewable energy price effects," Energy, Elsevier, vol. 114(C), pages 188-200.
    2. Taylor, James W., 2008. "An evaluation of methods for very short-term load forecasting using minute-by-minute British data," International Journal of Forecasting, Elsevier, vol. 24(4), pages 645-658.
    3. Shao, Zhen & Chao, Fu & Yang, Shan-Lin & Zhou, Kai-Le, 2017. "A review of the decomposition methodology for extracting and identifying the fluctuation characteristics in electricity demand forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 123-136.
    4. Spiliotis, Evangelos & Petropoulos, Fotios & Kourentzes, Nikolaos & Assimakopoulos, Vassilios, 2020. "Cross-temporal aggregation: Improving the forecast accuracy of hierarchical electricity consumption," Applied Energy, Elsevier, vol. 261(C).
    5. Sharifzadeh, Mahdi & Sikinioti-Lock, Alexandra & Shah, Nilay, 2019. "Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 513-538.
    6. Yukseltan, Ergun & Yucekaya, Ahmet & Bilge, Ayse Humeyra, 2017. "Forecasting electricity demand for Turkey: Modeling periodic variations and demand segregation," Applied Energy, Elsevier, vol. 193(C), pages 287-296.
    7. J W Taylor, 2003. "Short-term electricity demand forecasting using double seasonal exponential smoothing," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(8), pages 799-805, August.
    8. Kaur, Amanpreet & Nonnenmacher, Lukas & Coimbra, Carlos F.M., 2016. "Net load forecasting for high renewable energy penetration grids," Energy, Elsevier, vol. 114(C), pages 1073-1084.
    9. Liu, Xuewei & Yuan, Zengwei & Xu, Yuan & Jiang, Songyan, 2017. "Greening cement in China: A cost-effective roadmap," Applied Energy, Elsevier, vol. 189(C), pages 233-244.
    10. Duclos, Jean-Yves, 2006. "Liberté ou égalité?," L'Actualité Economique, Société Canadienne de Science Economique, vol. 82(4), pages 441-476, décembre.
    11. 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.
    12. Hanna Dudek & Joanna Landmesser, 2012. "Income satisfaction and relative deprivation," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 13(2), pages 321-334, June.
    13. Tse-Chuan Yang & Stephen A Matthews, 2015. "Death by Segregation: Does the Dimension of Racial Segregation Matter?," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-26, September.
    14. Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, vol. 30(2), pages 291-302.
    15. Curtis, John & Lynch, Muireann Á. & Zubiate, Laura, 2016. "The impact of the North Atlantic Oscillation on electricity markets: A case study on Ireland," Energy Economics, Elsevier, vol. 58(C), pages 186-198.
    16. Maite Blázquez Cuesta & Santiago Budría, 2014. "Deprivation and Subjective Well-Being: Evidence from Panel Data," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 60(4), pages 655-682, December.
    17. Oded Stark & Walter Hyll & Yong Wang, 2012. "Endogenous Selection of Comparison Groups, Human Capital Formation, and Tax Policy," Economica, London School of Economics and Political Science, vol. 79(313), pages 62-75, January.
    18. Pak, Tae-Young, 2023. "Relative deprivation and financial risk taking✰," Finance Research Letters, Elsevier, vol. 55(PA).
    19. Alysha M De Livera, 2010. "Automatic forecasting with a modified exponential smoothing state space framework," Monash Econometrics and Business Statistics Working Papers 10/10, Monash University, Department of Econometrics and Business Statistics.
    20. Spiliotis, Evangelos & Makridakis, Spyros & Kaltsounis, Anastasios & Assimakopoulos, Vassilios, 2021. "Product sales probabilistic forecasting: An empirical evaluation using the M5 competition data," International Journal of Production Economics, Elsevier, vol. 240(C).

    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:appene:v:158:y:2015:i:c:p:255-262. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.