IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v353y2024ipas0306261923014332.html
   My bibliography  Save this article

Energy demand forecasting using adaptive ARFIMA based on a novel dynamic structural break detection framework

Author

Listed:
  • Nikseresht, Ali
  • Amindavar, Hamidreza

Abstract

Forecasting energy demand has become increasingly important due to technological advances, especially new power systems and population growth. Accurate predictions of energy demand and supply are crucial for academics and policymakers. Also, energy consumption is non-stationary and dynamic in time, requiring an adaptive forecasting algorithm. This paper presents a new adaptive hybrid approach for energy time series forecasting using statistical methods. Some of the most popular energy demand time series applications are used to demonstrate the superiority of the proposed algorithm, which covers a wide range of critical energy demand forecasting situations at various scales. The paper's objectives are presented in three phases. In Phase I, ARFIMA is used to forecast the entire original time series as a statistical inspector and later for comparison. Although ARFIMA's fractionality feature helps to capture long and short-term memory behavior in time series, to be more adaptive and become close to a nonlinear algorithm that can handle non-stationarity, and Gaussian and non-Gaussian time series closely, a novel dynamic statistical structural break detection framework is developed, and used in phase II to identify the time series' change points and associated time indexes. Since time series are partitioned based on structural change detections, they approach one in terms of the Hurst exponent and exhibit pure long memory, which is ideal for ARFIMA modeling. Phase III captures non-stationary statistical properties and memory characteristics by applying adaptive ARFIMA modeling to segmented time series. Finally, the predicted partitions are concatenated. Various performance assessment metrics, e.g., Mean Absolute Percentage Error (MAPE), tested on the final achieved results show that the proposed adaptive algorithm outperforms the forecasting capabilities of the existing algorithms on the same energy demand time series used in the current paper. MAPE of the proposed approach for the four utilized real-life cases are 1.72%, 1.35%, 0.22%, and 4.4%, respectively. Therefore, the proposed model is a satisfactory method for energy demand forecasting due to its high accuracy.

Suggested Citation

  • Nikseresht, Ali & Amindavar, Hamidreza, 2024. "Energy demand forecasting using adaptive ARFIMA based on a novel dynamic structural break detection framework," Applied Energy, Elsevier, vol. 353(PA).
  • Handle: RePEc:eee:appene:v:353:y:2024:i:pa:s0306261923014332
    DOI: 10.1016/j.apenergy.2023.122069
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261923014332
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2023.122069?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. Caner, M. & Kilian, L., 2001. "Size distortions of tests of the null hypothesis of stationarity: evidence and implications for the PPP debate," Journal of International Money and Finance, Elsevier, vol. 20(5), pages 639-657, October.
    2. Granger, Clive W. J. & Hyung, Namwon, 2004. "Occasional structural breaks and long memory with an application to the S&P 500 absolute stock returns," Journal of Empirical Finance, Elsevier, vol. 11(3), pages 399-421, June.
    3. Chang, Chih-Hao & Chen, Zih-Bing & Huang, Shih-Feng, 2022. "Forecasting of high-resolution electricity consumption with stochastic climatic covariates via a functional time series approach," Applied Energy, Elsevier, vol. 309(C).
    4. Diebold, Francis X. & Inoue, Atsushi, 2001. "Long memory and regime switching," Journal of Econometrics, Elsevier, vol. 105(1), pages 131-159, November.
    5. Im, Kyung So & Pesaran, M. Hashem & Shin, Yongcheol, 2003. "Testing for unit roots in heterogeneous panels," Journal of Econometrics, Elsevier, vol. 115(1), pages 53-74, July.
    6. Wang, Lin & Lv, Sheng-Xiang & Zeng, Yu-Rong, 2018. "Effective sparse adaboost method with ESN and FOA for industrial electricity consumption forecasting in China," Energy, Elsevier, vol. 155(C), pages 1013-1031.
    7. Jushan Bai & Pierre Perron, 1998. "Estimating and Testing Linear Models with Multiple Structural Changes," Econometrica, Econometric Society, vol. 66(1), pages 47-78, January.
    8. Ding, Zhuanxin & Granger, Clive W. J., 1996. "Modeling volatility persistence of speculative returns: A new approach," Journal of Econometrics, Elsevier, vol. 73(1), pages 185-215, July.
    9. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.
    10. Jushan Bai & Pierre Perron, 2003. "Computation and analysis of multiple structural change models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 1-22.
    11. Xiong, Xin & Hu, Xi & Guo, Huan, 2021. "A hybrid optimized grey seasonal variation index model improved by whale optimization algorithm for forecasting the residential electricity consumption," Energy, Elsevier, vol. 234(C).
    12. Cecchetti, Stephen G & Lam, Pok-sang, 1994. "Variance-Ratio Tests: Small-Sample Properties with an Application to International Output Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(2), pages 177-186, April.
    13. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    14. Xu, Xiuqin & Chen, Ying & Goude, Yannig & Yao, Qiwei, 2021. "Day-ahead probabilistic forecasting for French half-hourly electricity loads and quantiles for curve-to-curve regression," Applied Energy, Elsevier, vol. 301(C).
    15. Zhao, Yang & Li, Jianping & Yu, Lean, 2017. "A deep learning ensemble approach for crude oil price forecasting," Energy Economics, Elsevier, vol. 66(C), pages 9-16.
    16. Xiu, Jin & Jin, Yao, 2007. "Empirical study of ARFIMA model based on fractional differencing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 377(1), pages 138-154.
    17. Maltais, Louis-Gabriel & Gosselin, Louis, 2022. "Forecasting of short-term lighting and plug load electricity consumption in single residential units: Development and assessment of data-driven models for different horizons," Applied Energy, Elsevier, vol. 307(C).
    18. Andersen, Torben G. & Bollerslev, Tim, 1997. "Intraday periodicity and volatility persistence in financial markets," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 115-158, June.
    19. Yang, Wangwang & Shi, Jing & Li, Shujian & Song, Zhaofang & Zhang, Zitong & Chen, Zexu, 2022. "A combined deep learning load forecasting model of single household resident user considering multi-time scale electricity consumption behavior," Applied Energy, Elsevier, vol. 307(C).
    20. Ciulla, G. & D'Amico, A., 2019. "Building energy performance forecasting: A multiple linear regression approach," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    21. Peng, Lu & Wang, Lin & Xia, De & Gao, Qinglu, 2022. "Effective energy consumption forecasting using empirical wavelet transform and long short-term memory," Energy, Elsevier, vol. 238(PB).
    22. Baillie, Richard T & Chung, Ching-Fan & Tieslau, Margie A, 1996. "Analysing Inflation by the Fractionally Integrated ARFIMA-GARCH Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(1), pages 23-40, Jan.-Feb..
    23. Zhou, Xinlei & Lin, Wenye & Kumar, Ritunesh & Cui, Ping & Ma, Zhenjun, 2022. "A data-driven strategy using long short term memory models and reinforcement learning to predict building electricity consumption," Applied Energy, Elsevier, vol. 306(PB).
    24. Munkhammar, Joakim & van der Meer, Dennis & Widén, Joakim, 2021. "Very short term load forecasting of residential electricity consumption using the Markov-chain mixture distribution (MCM) model," Applied Energy, Elsevier, vol. 282(PA).
    25. C. W. J. Granger & Roselyne Joyeux, 1980. "An Introduction To Long‐Memory Time Series Models And Fractional Differencing," Journal of Time Series Analysis, Wiley Blackwell, vol. 1(1), pages 15-29, January.
    26. John Elder & Peter E. Kennedy, 2001. "Testing for Unit Roots: What Should Students Be Taught?," The Journal of Economic Education, Taylor & Francis Journals, vol. 32(2), pages 137-146, January.
    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. Ngene, Geoffrey & Tah, Kenneth A. & Darrat, Ali F., 2017. "Long memory or structural breaks: Some evidence for African stock markets," Review of Financial Economics, Elsevier, vol. 34(C), pages 61-73.
    2. Abderrazak Ben Maatoug & Rim Lamouchi & Russell Davidson & Ibrahim Fatnassi, 2018. "Modelling Foreign Exchange Realized Volatility Using High Frequency Data: Long Memory versus Structural Breaks," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 10(1), pages 1-25, March.
    3. Geoffrey Ngene & Kenneth A. Tah & Ali F. Darrat, 2017. "Long memory or structural breaks: Some evidence for African stock markets," Review of Financial Economics, John Wiley & Sons, vol. 34(1), pages 61-73, September.
    4. Geoffrey Ngene & Ann Nduati Mungai & Allen K. Lynch, 2018. "Long-Term Dependency Structure and Structural Breaks: Evidence from the U.S. Sector Returns and Volatility," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 21(02), pages 1-38, June.
    5. Gadea, Maria Dolores & Sabate, Marcela & Serrano, Jose Maria, 2004. "Structural breaks and their trace in the memory: Inflation rate series in the long-run," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 14(2), pages 117-134, April.
    6. Choi, Kyongwook & Yu, Wei-Choun & Zivot, Eric, 2010. "Long memory versus structural breaks in modeling and forecasting realized volatility," Journal of International Money and Finance, Elsevier, vol. 29(5), pages 857-875, September.
    7. Gil-Alana, Luis A. & Shittu, Olanrewaju I. & Yaya, OlaOluwa S., 2014. "On the persistence and volatility in European, American and Asian stocks bull and bear markets," Journal of International Money and Finance, Elsevier, vol. 40(C), pages 149-162.
    8. Lu, Yang K. & Perron, Pierre, 2010. "Modeling and forecasting stock return volatility using a random level shift model," Journal of Empirical Finance, Elsevier, vol. 17(1), pages 138-156, January.
    9. Choi, Kyongwook & Zivot, Eric, 2007. "Long memory and structural changes in the forward discount: An empirical investigation," Journal of International Money and Finance, Elsevier, vol. 26(3), pages 342-363, April.
    10. Rinke, Saskia & Busch, Marie & Leschinski, Christian, 2017. "Long Memory, Breaks, and Trends: On the Sources of Persistence in Inflation Rates," Hannover Economic Papers (HEP) dp-584, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    11. C.S. Bos & S.J. Koopman & M. Ooms, 2007. "Long Memory Modelling of Inflation with Stochastic Variance and Structural Breaks," Tinbergen Institute Discussion Papers 07-099/4, Tinbergen Institute.
    12. Mateo Isoardi & Luis A. Gil-Alana, 2019. "Inflation in Argentina: Analysis of Persistence Using Fractional Integration," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 45(2), pages 204-223, April.
    13. Banerjee, Anindya & Urga, Giovanni, 2005. "Modelling structural breaks, long memory and stock market volatility: an overview," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 1-34.
    14. Paul Johnson & Chris Papageorgiou, 2020. "What Remains of Cross-Country Convergence?," Journal of Economic Literature, American Economic Association, vol. 58(1), pages 129-175, March.
    15. Caporale, Guglielmo Maria & Gil-Alana, Luis A., 2008. "Modelling the US, UK and Japanese unemployment rates: Fractional integration and structural breaks," Computational Statistics & Data Analysis, Elsevier, vol. 52(11), pages 4998-5013, July.
    16. Charfeddine, Lanouar & Ajmi, Ahdi Noomen, 2013. "The Tunisian stock market index volatility: Long memory vs. switching regime," Emerging Markets Review, Elsevier, vol. 16(C), pages 170-182.
    17. Giorgio Canarella & Luis A. Gil‐Alana & Rangan Gupta & Stephen M. Miller, 2022. "The behaviour of real interest rates: New evidence from a 'suprasecular' perspective," International Finance, Wiley Blackwell, vol. 25(1), pages 46-64, April.
    18. Kunal Saha & Vinodh Madhavan & Chandrashekhar G. R. & David McMillan, 2020. "Pitfalls in long memory research," Cogent Economics & Finance, Taylor & Francis Journals, vol. 8(1), pages 1733280-173, January.
    19. Zied Ftiti & Slim Chaouachi, 2018. "What Can We Learn About the Real Exchange Rate Behavior in the Case of a Peripheral Country?," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 16(3), pages 681-707, September.
    20. Zeynel Abidin Ozdemir & Mehmet Balcilar & Aysit Tansel, 2013. "International Labour Force Participation Rates By Gender: Unit Root Or Structural Breaks?," Bulletin of Economic Research, Wiley Blackwell, vol. 65, pages 142-164, May.

    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:353:y:2024:i:pa:s0306261923014332. 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.