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An econometric model for annual peak demand for small utilities

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  • 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
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    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. 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.
    3. 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.
    4. 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.
    5. Egelioglu, F. & Mohamad, A.A. & Guven, H., 2001. "Economic variables and electricity consumption in Northern Cyprus," Energy, Elsevier, vol. 26(4), pages 355-362.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. Dilaver, Zafer & Hunt, Lester C., 2011. "Turkish aggregate electricity demand: An outlook to 2020," Energy, Elsevier, vol. 36(11), pages 6686-6696.
    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. 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.
    15. AfDB AfDB, . "Annual Report 2012," Annual Report, African Development Bank, number 461.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    Full references (including those not matched with items on IDEAS)

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    2. 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.
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    4. 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.

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