IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i5p1303-d148029.html
   My bibliography  Save this article

A New Perspective on Improving Hospital Energy Administration Based on Recurrence Interval Analysis

Author

Listed:
  • Fei Wang

    (School of Economics and Management, Southeast University, Nanjing 211189, China)

  • Wei Chao

    (College of International Studies, Yangzhou University, Yangzhou 225000, China)

Abstract

Based on 15-min high-frequency power load data from a Chinese hospital, by adopting recurrence interval analysis, an attempt is made to provide a new perspective for improving hospital energy administration in electrical efficiency and safety. Initially, the definition of extreme fluctuation of the power load, as well as the recurrence interval, is given. Next, the stretched exponential distribution function is provided, which fits quite well with the probability density distribution of recurrence intervals. Then, tests on recurrence intervals, including scaling behavior and short-term and long-term memory effect are conducted. At last, a risk estimation method of VaR is proposed for hospital energy administrator to forecast risk probability. Results clearly indicate that the recurrence interval analysis (RIA) method works well on forecasting extreme power load fluctuation in hospital. However, there is no evidence to support the existence of the long-term memory effect of recurrence intervals, which means that hospital energy management plans have to be continuously fixed and updated with time. Some relevant applicant suggestions are provided for the energy administrator at the end of this paper.

Suggested Citation

  • Fei Wang & Wei Chao, 2018. "A New Perspective on Improving Hospital Energy Administration Based on Recurrence Interval Analysis," Energies, MDPI, vol. 11(5), pages 1-18, May.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:5:p:1303-:d:148029
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/5/1303/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/5/1303/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chi Zhang & Zhengning Pu & Qin Zhou, 2018. "Sustainable Energy Consumption in Northeast Asia: A Case from China’s Fuel Oil Futures Market," Sustainability, MDPI, vol. 10(1), pages 1-14, January.
    2. Lucheng Hong & Wantao Shu & Angela C. Chao, 2018. "Recurrence Interval Analysis on Electricity Consumption of an Office Building in China," Sustainability, MDPI, vol. 10(2), pages 1-15, January.
    3. Cirillo, Pasquale & Hüsler, Jürg, 2009. "On the upper tail of Italian firms’ size distribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(8), pages 1546-1554.
    4. Fei Ren & Wei-Xing Zhou, 2009. "Recurrence interval analysis of high-frequency financial returns and its application to risk estimation," Papers 0909.0123, arXiv.org.
    5. Pao, H.T., 2009. "Forecasting energy consumption in Taiwan using hybrid nonlinear models," Energy, Elsevier, vol. 34(10), pages 1438-1446.
    6. Xie, Wen-Jie & Jiang, Zhi-Qiang & Zhou, Wei-Xing, 2014. "Extreme value statistics and recurrence intervals of NYMEX energy futures volatility," Economic Modelling, Elsevier, vol. 36(C), pages 8-17.
    7. Nima Amjady & Farshid Keynia, 2011. "A New Neural Network Approach to Short Term Load Forecasting of Electrical Power Systems," Energies, MDPI, vol. 4(3), pages 1-16, March.
    8. Kantelhardt, Jan W. & Zschiegner, Stephan A. & Koscielny-Bunde, Eva & Havlin, Shlomo & Bunde, Armin & Stanley, H.Eugene, 2002. "Multifractal detrended fluctuation analysis of nonstationary time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 316(1), pages 87-114.
    9. Suo, Yuan-Yuan & Wang, Dong-Hua & Li, Sai-Ping, 2015. "Risk estimation of CSI 300 index spot and futures in China from a new perspective," Economic Modelling, Elsevier, vol. 49(C), pages 344-353.
    10. Lo, Andrew W, 1991. "Long-Term Memory in Stock Market Prices," Econometrica, Econometric Society, vol. 59(5), pages 1279-1313, September.
    11. Lin, Aijing & Ma, Hui & Shang, Pengjian, 2015. "The scaling properties of stock markets based on modified multiscale multifractal detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 525-537.
    12. Chi Zhang & Zhengning Pu & Jiasha Fu, 2018. "The Recurrence Interval Difference of Power Load in Heavy/Light Industries of China," Energies, MDPI, vol. 11(1), pages 1-20, January.
    13. Gerhard, Frank & Hautsch, Nikolaus, 2002. "Volatility estimation on the basis of price intensities," Journal of Empirical Finance, Elsevier, vol. 9(1), pages 57-89, January.
    14. Wai-Ming To & Peter Ka Chun Lee & Tsz-Ming Lai, 2017. "Modeling of Monthly Residential and Commercial Electricity Consumption Using Nonlinear Seasonal Models—The Case of Hong Kong," Energies, MDPI, vol. 10(7), pages 1-16, June.
    15. Resnick, Sidney I., 1997. "Discussion of the Danish Data on Large Fire Insurance Losses," ASTIN Bulletin, Cambridge University Press, vol. 27(1), pages 139-151, May.
    16. Harris, John L. & Liu, Lon-Mu, 1993. "Dynamic structural analysis and forecasting of residential electricity consumption," International Journal of Forecasting, Elsevier, vol. 9(4), pages 437-455, December.
    17. Alexander J. McNeil & Rüdiger Frey & Paul Embrechts, 2015. "Quantitative Risk Management: Concepts, Techniques and Tools Revised edition," Economics Books, Princeton University Press, edition 2, number 10496.
    18. Zhou, P. & Ang, B.W. & Poh, K.L., 2006. "A trigonometric grey prediction approach to forecasting electricity demand," Energy, Elsevier, vol. 31(14), pages 2839-2847.
    19. Fei Ren & Wei-Xing Zhou, 2008. "Multiscaling behavior in the volatility return intervals of Chinese indices," Papers 0809.0250, arXiv.org.
    20. Kantelhardt, Jan W & Koscielny-Bunde, Eva & Rego, Henio H.A & Havlin, Shlomo & Bunde, Armin, 2001. "Detecting long-range correlations with detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 295(3), pages 441-454.
    Full references (including those not matched with items on IDEAS)

    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. Lucheng Hong & Wantao Shu & Angela C. Chao, 2018. "Recurrence Interval Analysis on Electricity Consumption of an Office Building in China," Sustainability, MDPI, vol. 10(2), pages 1-15, January.
    2. Chi Zhang & Zhengning Pu & Jiasha Fu, 2018. "The Recurrence Interval Difference of Power Load in Heavy/Light Industries of China," Energies, MDPI, vol. 11(1), pages 1-20, January.
    3. Ni, Xiao-Hui & Jiang, Zhi-Qiang & Gu, Gao-Feng & Ren, Fei & Chen, Wei & Zhou, Wei-Xing, 2010. "Scaling and memory in the non-Poisson process of limit order cancelation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(14), pages 2751-2761.
    4. Gholami, M. & Barbaresi, A. & Torreggiani, D. & Tassinari, P., 2020. "Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    5. Chi Zhang & Zhengning Pu & Qin Zhou, 2018. "Sustainable Energy Consumption in Northeast Asia: A Case from China’s Fuel Oil Futures Market," Sustainability, MDPI, vol. 10(1), pages 1-14, January.
    6. Zeng, Yayun & Wang, Jun & Xu, Kaixuan, 2017. "Complexity and multifractal behaviors of multiscale-continuum percolation financial system for Chinese stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 364-376.
    7. Li, Daye & Nishimura, Yusaku & Men, Ming, 2016. "The long memory and the transaction cost in financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 442(C), pages 312-320.
    8. Li, Daye & Kou, Zhun & Sun, Qiankun, 2015. "The scale-dependent market trend: Empirical evidences using the lagged DFA method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 433(C), pages 26-35.
    9. Olivares, Felipe & Sun, Xiaoqian & Wandelt, Sebastian & Zanin, Massimiliano, 2023. "Measuring landing independence and interactions using statistical physics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 170(C).
    10. Martín-Montoya, L.A. & Aranda-Camacho, N.M. & Quimbay, C.J., 2015. "Long-range correlations and trends in Colombian seismic time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 124-133.
    11. 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.
    12. Suo, Yuan-Yuan & Wang, Dong-Hua & Li, Sai-Ping, 2015. "Risk estimation of CSI 300 index spot and futures in China from a new perspective," Economic Modelling, Elsevier, vol. 49(C), pages 344-353.
    13. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    14. İşcanoğlu-Çekiç, Ayşegül & Gülteki̇n, Havva, 2019. "Are cross-correlations between Turkish Stock Exchange and three major country indices multifractal or monofractal?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 978-990.
    15. Lavička, Hynek & Kracík, Jiří, 2020. "Fluctuation analysis of electric power loads in Europe: Correlation multifractality vs. Distribution function multifractality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    16. Vitanov, Nikolay K. & Sakai, Kenshi & Dimitrova, Zlatinka I., 2008. "SSA, PCA, TDPSC, ACFA: Useful combination of methods for analysis of short and nonstationary time series," Chaos, Solitons & Fractals, Elsevier, vol. 37(1), pages 187-202.
    17. Zhong, Meirui & Zhang, Rui & Ren, Xiaohang, 2023. "The time-varying effects of liquidity and market efficiency of the European Union carbon market: Evidence from the TVP-SVAR-SV approach," Energy Economics, Elsevier, vol. 123(C).
    18. Ruan, Qingsong & Bao, Junjie & Zhang, Manqian & Fan, Limin, 2019. "The effects of exchange rate regime reform on RMB markets: A new perspective based on MF-DCCA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 522(C), pages 122-134.
    19. Wang, Dong-Hua & Yu, Xiao-Wen & Suo, Yuan-Yuan, 2012. "Statistical properties of the yuan exchange rate index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(12), pages 3503-3512.
    20. Zhuang, Xiaoyang & Wei, Yu & Ma, Feng, 2015. "Multifractality, efficiency analysis of Chinese stock market and its cross-correlation with WTI crude oil price," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 430(C), pages 101-113.

    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:11:y:2018:i:5:p:1303-:d:148029. 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.