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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.
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Agency for Natural Resources and Energy. (2011). Follow-up of countermeasures to electricity power shortages in this summer (large business, small business, and household). http://www.enecho.meti.go.jp/committee/council/basic_ problem_committee/006/pdf/6-42.pdf. Accessed October 16 2015 (in Japanese).
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.
Bank of Japan. (2011). Yen/dollar spot in the Tokyo market at 17:00 on July 11, 2011. http://www.stat-search.boj.or.jp/ssi/mtshtml/d.html. Accessed October 4 2015.
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. MathSciNetCrossRefMATH
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.
Hipel, K. W., & McLeod, A. I. (1994). Time series modelling of water resources and environmental systems. Amsterdam: Elsevier.
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.
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).
International Energy Agency (IEA). (2005). Saving electricity in a hurry. http://www.iea.org/publications/freepublications/publication/saving-electricity-in-a-hurry-2005.html. Accessed February 14 2015.
Japan Meteorological Agency (JMA). (2015). Past climate information. http://www.data.jma.go.jp/obd/stats/etrn/index.php. Accessed October 4 2015 (in Japanese).
Kajitani, Y., & Tatano, H. (2009). Estimation of resilience factors based on surveys of Japanese industries. Earthquake Spectra, 25(4), 755–776.
Kanto Bureau of Economy, Trade and Industry (METI-KANTO). (2015). Result of electricity power demand (December). http://www.kanto.meti.go.jp/tokei/denryoku/20130214index.html. Accessed February 14 2012 (in Japanese).
Kanto Bureau of Economy, Trade and Industry (METI-KANTO). (2015b). Trend of industrial production. http://www.kanto.meti.go.jp/tokei/kokogyo/kokogyo_index.html. Accessed February 14 2012 (in Japanese).
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. CrossRef
Nihon Keizai Shinbun (Nikkei). (2011). Earthquake disaster and macro-economic. Analysis, 24, (in Japanese).
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/j.energy.2008.05.008.
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.
Taylor, J. W., & Buizzab, R. (2003). Using weather ensemble predictions in electricity demand forecasting. International Journal of Forecasting, 19, 57–70. CrossRef
The Japan Iron and Steel Federation. (2015). Monthly steel supply and demand statistics. http://www.jisf.or.jp/data/tokei/index.html. Accessed August 14 2015 (in Japanese).
Tohoku Electric Power Co. (2015). Past power demand statistics. http://setsuden.tohoku-epco.co.jp/download.html. Accessed August 14 2015 (in Japanese).
- 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
- Springer New York
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