IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v89y2015icp35-44.html
   My bibliography  Save this article

An econometric model for annual peak demand for small utilities

Author

Listed:
  • Mirlatifi, A.M.
  • Egelioglu, F.
  • Atikol, U.

Abstract

This paper presents a model that can be used to estimate the annual peak demand in electricity consumption for small sized electric utilities. The developed model was used to investigate the influence of econometric variables on the power demand of N. Cyprus. Utilizing historical annual databases, analysis of variance (ANOVA), and the statistical methods, it was found that number of customers, price of electricity, number of tourists, population, as well as heating degree days correlate with the annual peak demand with R2 = 0.995. The performance of the model was measured using mean absolute scaled error (MASE) and mean absolute percentage error (MAPE) for in-sample (1992–2010) and out-of-sample (2011–2013) data. Also, to ensure the validity of the outcomes, models were regenerated by decreasing the in-sample data (i.e. increasing the out of sample data) back to five consecutive years and MAPE and MASE calculations were repeated. The results indicated that the model, using the above-mentioned parameters as regressors, has very strong predictive ability and can be used to estimate the annual peak demand. The combination of peak demand, base demand and energy consumption models can be utilized as a useful technique for the purpose of resource planning for small sized utilities.

Suggested Citation

  • Mirlatifi, A.M. & Egelioglu, F. & Atikol, U., 2015. "An econometric model for annual peak demand for small utilities," Energy, Elsevier, vol. 89(C), pages 35-44.
    • Handle: RePEc:eee:energy:v:89:y:2015:i:c:p:35-44
    DOI: 10.1016/j.energy.2015.06.119
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544215008750
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2015.06.119?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. Zachariadis, Theodoros & Hadjinicolaou, Panos, 2014. "The effect of climate change on electricity needs – A case study from Mediterranean Europe," Energy, Elsevier, vol. 76(C), pages 899-910.
    2. Wang, Chi-hsiang & Grozev, George & Seo, Seongwon, 2012. "Decomposition and statistical analysis for regional electricity demand forecasting," Energy, Elsevier, vol. 41(1), pages 313-325.
    3. Sinden, Graham, 2007. "Characteristics of the UK wind resource: Long-term patterns and relationship to electricity demand," Energy Policy, Elsevier, vol. 35(1), pages 112-127, January.
    4. Dirks, James A. & Gorrissen, Willy J. & Hathaway, John H. & Skorski, Daniel C. & Scott, Michael J. & Pulsipher, Trenton C. & Huang, Maoyi & Liu, Ying & Rice, Jennie S., 2015. "Impacts of climate change on energy consumption and peak demand in buildings: A detailed regional approach," Energy, Elsevier, vol. 79(C), pages 20-32.
    5. Badri, Masood A. & Al-Mutawa, Ahmed & Davis, Donald & Davis, Donna, 1997. "EDSSF: A decision support system (DSS) for electricity peak-load forecasting," Energy, Elsevier, vol. 22(6), pages 579-589.
    6. Tashman, Leonard J., 2000. "Out-of-sample tests of forecasting accuracy: an analysis and review," International Journal of Forecasting, Elsevier, vol. 16(4), pages 437-450.
    7. Dilaver, Zafer & Hunt, Lester C., 2011. "Turkish aggregate electricity demand: An outlook to 2020," Energy, Elsevier, vol. 36(11), pages 6686-6696.
    8. AfDB AfDB, . "Annual Report 2012," Annual Report, African Development Bank, number 461.
    9. Hong, Tianzhen & Chang, Wen-Kuei & Lin, Hung-Wen, 2013. "A fresh look at weather impact on peak electricity demand and energy use of buildings using 30-year actual weather data," Applied Energy, Elsevier, vol. 111(C), pages 333-350.
    10. Zahedi, Gholamreza & Azizi, Saeed & Bahadori, Alireza & Elkamel, Ali & Wan Alwi, Sharifah R., 2013. "Electricity demand estimation using an adaptive neuro-fuzzy network: A case study from the Ontario province – Canada," Energy, Elsevier, vol. 49(C), pages 323-328.
    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. Denholm, Paul & Margolis, Robert M., 2007. "Evaluating the limits of solar photovoltaics (PV) in traditional electric power systems," Energy Policy, Elsevier, vol. 35(5), pages 2852-2861, May.
    13. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    14. Schaeffer, Roberto & Szklo, Alexandre Salem & Pereira de Lucena, André Frossard & Moreira Cesar Borba, Bruno Soares & Pupo Nogueira, Larissa Pinheiro & Fleming, Fernanda Pereira & Troccoli, Alberto & , 2012. "Energy sector vulnerability to climate change: A review," Energy, Elsevier, vol. 38(1), pages 1-12.
    15. Egelioglu, F. & Mohamad, A.A. & Guven, H., 2001. "Economic variables and electricity consumption in Northern Cyprus," Energy, Elsevier, vol. 26(4), pages 355-362.
    16. Adom, Philip Kofi & Bekoe, William, 2012. "Conditional dynamic forecast of electrical energy consumption requirements in Ghana by 2020: A comparison of ARDL and PAM," Energy, Elsevier, vol. 44(1), pages 367-380.
    17. Arisoy, Ibrahim & Ozturk, Ilhan, 2014. "Estimating industrial and residential electricity demand in Turkey: A time varying parameter approach," Energy, Elsevier, vol. 66(C), pages 959-964.
    18. Weisser, Daniel, 2004. "On the economics of electricity consumption in small island developing states: a role for renewable energy technologies?," Energy Policy, Elsevier, vol. 32(1), pages 127-140, January.
    19. Tabatabaie, Seyed Mohammad Hossein & Rafiee, Shahin & Keyhani, Alireza, 2012. "Energy consumption flow and econometric models of two plum cultivars productions in Tehran province of Iran," Energy, Elsevier, vol. 44(1), pages 211-216.
    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. Neves, Sónia Almeida & Marques, António Cardoso & Fuinhas, José Alberto, 2018. "On the drivers of peak electricity demand: What is the role played by battery electric cars?," Energy, Elsevier, vol. 159(C), pages 905-915.
    2. Sigauke, Caston & Bere, Alphonce, 2017. "Modelling non-stationary time series using a peaks over threshold distribution with time varying covariates and threshold: An application to peak electricity demand," Energy, Elsevier, vol. 119(C), pages 152-166.
    3. Loganthurai, P. & Rajasekaran, V. & Gnanambal, K., 2016. "Evolutionary algorithm based optimum scheduling of processing units in rice industry to reduce peak demand," Energy, Elsevier, vol. 107(C), pages 419-430.
    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).

    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. Engeland, Kolbjørn & Borga, Marco & Creutin, Jean-Dominique & François, Baptiste & Ramos, Maria-Helena & Vidal, Jean-Philippe, 2017. "Space-time variability of climate variables and intermittent renewable electricity production – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 600-617.
    2. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    3. Aryanpur, Vahid & Fattahi, Mahshid & Mamipour, Siab & Ghahremani, Mahsa & Gallachóir, Brian Ó & Bazilian, Morgan D. & Glynn, James, 2022. "How energy subsidy reform can drive the Iranian power sector towards a low-carbon future," Energy Policy, Elsevier, vol. 169(C).
    4. Salisu, Afees A. & Ayinde, Taofeek O., 2016. "Modeling energy demand: Some emerging issues," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 1470-1480.
    5. Nam, KiJeon & Hwangbo, Soonho & Yoo, ChangKyoo, 2020. "A deep learning-based forecasting model for renewable energy scenarios to guide sustainable energy policy: A case study of Korea," Renewable and Sustainable Energy Reviews, Elsevier, vol. 122(C).
    6. 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.
    7. Cabral, Joilson de Assis & Freitas Cabral, Maria Viviana de & Pereira Júnior, Amaro Olímpio, 2020. "Elasticity estimation and forecasting: An analysis of residential electricity demand in Brazil," Utilities Policy, Elsevier, vol. 66(C).
    8. 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).
    9. Hamzacebi, Coskun & Es, Huseyin Avni, 2014. "Forecasting the annual electricity consumption of Turkey using an optimized grey model," Energy, Elsevier, vol. 70(C), pages 165-171.
    10. Arisoy, Ibrahim & Ozturk, Ilhan, 2014. "Estimating industrial and residential electricity demand in Turkey: A time varying parameter approach," Energy, Elsevier, vol. 66(C), pages 959-964.
    11. 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).
    12. Spiliotis, Evangelos & Petropoulos, Fotios, 2024. "On the update frequency of univariate forecasting models," European Journal of Operational Research, Elsevier, vol. 314(1), pages 111-121.
    13. Van Belle, Jente & Guns, Tias & Verbeke, Wouter, 2021. "Using shared sell-through data to forecast wholesaler demand in multi-echelon supply chains," European Journal of Operational Research, Elsevier, vol. 288(2), pages 466-479.
    14. Evangelos Spiliotis & Fotios Petropoulos & Vassilios Assimakopoulos, 2023. "On the Disagreement of Forecasting Model Selection Criteria," Forecasting, MDPI, vol. 5(2), pages 1-12, June.
    15. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios & Chen, Zhi & Gaba, Anil & Tsetlin, Ilia & Winkler, Robert L., 2022. "The M5 uncertainty competition: Results, findings and conclusions," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1365-1385.
    16. Makridakis, Spyros & Hyndman, Rob J. & Petropoulos, Fotios, 2020. "Forecasting in social settings: The state of the art," International Journal of Forecasting, Elsevier, vol. 36(1), pages 15-28.
    17. Huang, Tao & Fildes, Robert & Soopramanien, Didier, 2019. "Forecasting retailer product sales in the presence of structural change," European Journal of Operational Research, Elsevier, vol. 279(2), pages 459-470.
    18. Jahanpour, Ehsan & Ko, Hoo Sang & Nof, Shimon Y., 2016. "Collaboration protocols for sustainable wind energy distribution networks," International Journal of Production Economics, Elsevier, vol. 182(C), pages 496-507.
    19. Barrow, Devon K. & Crone, Sven F., 2016. "A comparison of AdaBoost algorithms for time series forecast combination," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1103-1119.
    20. Semenoglou, Artemios-Anargyros & Spiliotis, Evangelos & Makridakis, Spyros & Assimakopoulos, Vassilios, 2021. "Investigating the accuracy of cross-learning time series forecasting methods," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1072-1084.

    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:energy:v:89:y:2015:i:c:p:35-44. 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.journals.elsevier.com/energy .

    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.