Business Resilience During Power Shortages: A Power Saving Rate Measured by Power Consumption Time Series in Industrial Sector Before and After the Great East Japan Earthquake in 2011

  • Yoshio KajitaniEmail author
Part of the Fields Institute Communications book series (FIC, volume 78)


Many power crises have occurred in developing and developed countries such as through disruptions in transmission lines, excessive demand during heat waves, and regulatory failures. The 2011 Great Japan Earthquake caused one of most severe power crises ever recorded. This study measures the industry’s ability to conserve power without critically reducing production (“power saving rate”) as one of the indicator of resilience as a lesson of disaster. The quantification of the power saving rate leads to grasping the potential power reduction of industrial sector or production losses caused by the future incidents in many regions or countries. Using time series data sets of monthly industrial production and power consumption, this study investigates the power saving rate of Japanese industries during power shortages after the great earthquake. The results demonstrates the size of power saving rate right after the disaster, during the first severe peak demand season, as well as long-term continuous efforts of power saving in different business.


Power shortage Resilience Great East Japan Earthquake Industrial sector 


  1. 1.
    Agency for Natural Resources and Energy. (2011). Follow-up of countermeasures to electricity power shortages in this summer (large business, small business, and household). problem_committee/006/pdf/6-42.pdf. Accessed October 16 2015 (in Japanese).
  2. 2.
    An, N., Zhao, W., Wang, J., Shang, D., & Zhao, E. (2013). Using multi-output feedforward neural network with empirical mode decomposition based signal filtering for electricity demand forecasting. Energy, 49, 279–288. doi: 10.1016/ Scholar
  3. 3.
    Azadeh, A., Ghaderi, S. F., & Sohrabkhani, S. (2008). Annual electricity consumption forecasting by neural network in high energy consuming industrial sectors. Energy Conversion and Management, 49(8), 2272–2278. doi: 10.1016/j.enconman.2008.01.035.Google Scholar
  4. 4.
    Bank of Japan. (2011). Yen/dollar spot in the Tokyo market at 17:00 on July 11, 2011. Accessed October 4 2015.
  5. 5.
    Fatai, K., Oxley, L., & Scrimgeour, F. G. (2004). Modelling the causal relationship between energy consumption and GDP in New Zealand, Australia, India, Indonesia, The Philippines and Thailand. Mathematics and Computers in Simulation, 64(3–4), 431–445. doi: 10.1016/s0378-4754(03)00109-5.MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Fujimi, T., & Chang, S. E. (2014). Adaptation to electricity crisis: Businesses in the 2011 Great East Japan triple disaster. Energy Policy, 68, 447–457. doi: 10.1016/j.enpol.2013.12.019.CrossRefGoogle Scholar
  7. 7.
    González-Romera, E., Jaramillo-Morán, M. Á., & Carmona-Fernández, D. (2007). Forecasting of the electric energy demand trend and monthly fluctuation with neural networks. Computers & Industrial Engineering, 52(3), 336–343. doi: 10.1016/j.cie.2006.12.010.Google Scholar
  8. 8.
    Hipel, K. W., & McLeod, A. I. (1994). Time series modelling of water resources and environmental systems. Amsterdam: Elsevier.Google Scholar
  9. 9.
    Hippert, H. S., Bunn, D. W., & Souza, R. C. (2005). Large neural networks for electricity load forecasting: Are they overfitted? International Journal of Forecasting, 21(3), 425–434. doi: 10.1016/j.ijforecast.2004.12.004.Google Scholar
  10. 10.
    Hyodo, T. (2012). Demand analysis on electricity during energy crisis period after the earthquake 2011. Transport Policy Studies’ Review, 15(1), 20–25 (in Japanese).Google Scholar
  11. 11.
    International Energy Agency (IEA). (2005). Saving electricity in a hurry. Accessed February 14 2015.
  12. 12.
    Japan Meteorological Agency (JMA). (2015). Past climate information. Accessed October 4 2015 (in Japanese).
  13. 13.
    Kajitani, Y., & Tatano, H. (2009). Estimation of resilience factors based on surveys of Japanese industries. Earthquake Spectra, 25(4), 755–776.Google Scholar
  14. 14.
    Kanto Bureau of Economy, Trade and Industry (METI-KANTO). (2015). Result of electricity power demand (December). Accessed February 14 2012 (in Japanese).
  15. 15.
    Kanto Bureau of Economy, Trade and Industry (METI-KANTO). (2015b). Trend of industrial production. Accessed February 14 2012 (in Japanese).
  16. 16.
    Kaytez, F., Taplamacioglu, M. C., Cam, E., & Hardalac, F. (2015). Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines. International Journal of Electrical Power & Energy Systems, 67, 431–438. doi: 10.1016/j.ijepes.2014.12.036.CrossRefGoogle Scholar
  17. 17.
    Nawaz, S., Iqbal, N., & Anwar, S. (2014). Modelling electricity demand using the STAR (Smooth Transition Auto-Regressive) model in Pakistan. Energy, 78, 535–542. doi: 10.1016/ Scholar
  18. 18.
    Nihon Keizai Shinbun (Nikkei). (2011). Earthquake disaster and macro-economic. Analysis, 24, (in Japanese).Google Scholar
  19. 19.
    Pappas, S. S., Ekonomou, L., Karamousantas, D. C., Chatzarakis, G. E., Katsikas, S. K., & Liatsis, P. (2008). Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models. Energy, 33(9), 1353–1360. doi: 10.1016/ Scholar
  20. 20.
    Taylor, J. W. (2010). Triple seasonal methods for short-term electricity demand forecasting. European Journal of Operational Research, 204(1), 139–152. doi: 10.1016/j.ejor.2009.10.003.Google Scholar
  21. 21.
    Taylor, J. W., & Buizzab, R. (2003). Using weather ensemble predictions in electricity demand forecasting. International Journal of Forecasting, 19, 57–70.CrossRefGoogle Scholar
  22. 22.
    The Japan Iron and Steel Federation. (2015). Monthly steel supply and demand statistics. Accessed August 14 2015 (in Japanese).
  23. 23.
    Tohoku Electric Power Co. (2015). Past power demand statistics. Accessed August 14 2015 (in Japanese).
  24. 24.
    Vu, D. H., Muttaqi, K. M., & Agalgaonkar, A. P. (2015). A variance inflation factor and backward elimination based robust regression model for forecasting monthly electricity demand using climatic variables. Applied Energy, 140, 385–394. doi: 10.1016/j.apenergy.2014.12.011.CrossRefGoogle Scholar
  25. 25.
    Zahedi, G., Azizi, S., Bahadori, A., Elkamel, A., & Wan Alwi, S. R. (2013). Electricity demand estimation using an adaptive neuro-fuzzy network: A case study from the Ontario province—Canada. Energy, 49, 323–328. doi: 10.1016/ Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Central Research Institute of Electric Power IndustryAbiko CityJapan

Personalised recommendations