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Electricity demand estimation using an adaptive neuro-fuzzy network: A case study from the Ontario province – Canada

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  • Zahedi, Gholamreza
  • Azizi, Saeed
  • Bahadori, Alireza
  • Elkamel, Ali
  • Wan Alwi, Sharifah R.

Abstract

Electricity is an important asset that influences not only the economy, but political or social security of a country. Reliable and accurate planning and prediction of electricity demand for a country are therefore vital. In this paper, electricity demand in Ontario province of Canada from the year 1976–2005 is modeled by using an (adaptive neuro fuzzy inference system) ANFIS. A neuro fuzzy structure can be defined as an ANN (artificial neural network) which is trained by experimental data to find the parameters of (fuzzy inference system) FIS. Inputs for the model include number of employment, (gross domestic product) GDP, population, dwelling count and two meteorological parameters related to annual weather temperature. The data were collected and screened using statistical methods. Then, based on the data, a neuro-fuzzy model for the electricity demand is built. It was found that electricity demand is most sensitive to employment.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:49:y:2013:i:c:p:323-328
    DOI: 10.1016/j.energy.2012.10.019
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    as
    1. Barros, Carlos Pestana, 2008. "Efficiency analysis of hydroelectric generating plants: A case study for Portugal," Energy Economics, Elsevier, vol. 30(1), pages 59-75, January.
    2. Chen, Sheng-Tung & Kuo, Hsiao-I & Chen, Chi-Chung, 2007. "The relationship between GDP and electricity consumption in 10 Asian countries," Energy Policy, Elsevier, vol. 35(4), pages 2611-2621, April.
    3. Sari, Ramazan & Soytas, Ugur, 2004. "Disaggregate energy consumption, employment and income in Turkey," Energy Economics, Elsevier, vol. 26(3), pages 335-344, May.
    4. Pao, H.T., 2009. "Forecasting energy consumption in Taiwan using hybrid nonlinear models," Energy, Elsevier, vol. 34(10), pages 1438-1446.
    5. Azadeh, A. & Saberi, M. & Seraj, O., 2010. "An integrated fuzzy regression algorithm for energy consumption estimation with non-stationary data: A case study of Iran," Energy, Elsevier, vol. 35(6), pages 2351-2366.
    6. Tan, Zhongfu & Zhang, Jinliang & Wang, Jianhui & Xu, Jun, 2010. "Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models," Applied Energy, Elsevier, vol. 87(11), pages 3606-3610, November.
    7. Nguyen, Hang T. & Nabney, Ian T., 2010. "Short-term electricity demand and gas price forecasts using wavelet transforms and adaptive models," Energy, Elsevier, vol. 35(9), pages 3674-3685.
    8. De Vita, G. & Endresen, K. & Hunt, L.C., 2006. "An empirical analysis of energy demand in Namibia," Energy Policy, Elsevier, vol. 34(18), pages 3447-3463, December.
    9. Barros, Carlos Pestana & Managi, Shunsuke, 2009. "Productivity assessment of Angola's oil blocks," Energy, Elsevier, vol. 34(11), pages 2009-2015.
    10. Ekonomou, L., 2010. "Greek long-term energy consumption prediction using artificial neural networks," Energy, Elsevier, vol. 35(2), pages 512-517.
    11. F. Chui & A. Elkamel & R. Surit & E. Croiset & P.L. Douglas, 2009. "Long-term electricity demand forecasting for power system planning using economic, demographic and climatic variables," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 3(3), pages 277-304.
    12. Behrang, M.A. & Assareh, E. & Ghalambaz, M. & Assari, M.R. & Noghrehabadi, A.R., 2011. "Forecasting future oil demand in Iran using GSA (Gravitational Search Algorithm)," Energy, Elsevier, vol. 36(9), pages 5649-5654.
    13. Amjady, N. & Keynia, F., 2009. "Short-term load forecasting of power systems by combination of wavelet transform and neuro-evolutionary algorithm," Energy, Elsevier, vol. 34(1), pages 46-57.
    14. Pappas, S.Sp. & Ekonomou, L. & Karamousantas, D.Ch. & Chatzarakis, G.E. & Katsikas, S.K. & Liatsis, P., 2008. "Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models," Energy, Elsevier, vol. 33(9), pages 1353-1360.
    15. Mamlook, Rustum & Badran, Omar & Abdulhadi, Emad, 2009. "A fuzzy inference model for short-term load forecasting," Energy Policy, Elsevier, vol. 37(4), pages 1239-1248, April.
    16. Soytas, Ugur & Sari, Ramazan, 2003. "Energy consumption and GDP: causality relationship in G-7 countries and emerging markets," Energy Economics, Elsevier, vol. 25(1), pages 33-37, January.
    17. Hainoun, A. & Seif-Eldin, M.K. & Almoustafa, S., 2006. "Analysis of the Syrian long-term energy and electricity demand projection using the end-use methodology," Energy Policy, Elsevier, vol. 34(14), pages 1958-1970, September.
    18. Deihimi, Ali & Showkati, Hemen, 2012. "Application of echo state networks in short-term electric load forecasting," Energy, Elsevier, vol. 39(1), pages 327-340.
    19. Narayan, Paresh Kumar & Smyth, Russell, 2005. "Electricity consumption, employment and real income in Australia evidence from multivariate Granger causality tests," Energy Policy, Elsevier, vol. 33(9), pages 1109-1116, June.
    20. Beccali, M. & Cellura, M. & Lo Brano, V. & Marvuglia, A., 2008. "Short-term prediction of household electricity consumption: Assessing weather sensitivity in a Mediterranean area," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(8), pages 2040-2065, October.
    21. Yao, Albert W.L. & Chi, S.C. & Chen, C.K., 2005. "Development of an integrated Grey–fuzzy-based electricity management system for enterprises," Energy, Elsevier, vol. 30(15), pages 2759-2771.
    22. Wang, Jianzhou & Zhu, Suling & Zhang, Wenyu & Lu, Haiyan, 2010. "Combined modeling for electric load forecasting with adaptive particle swarm optimization," Energy, Elsevier, vol. 35(4), pages 1671-1678.
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    25. Yang, L. & Entchev, E., 2014. "Performance prediction of a hybrid microgeneration system using Adaptive Neuro-Fuzzy Inference System (ANFIS) technique," Applied Energy, Elsevier, vol. 134(C), pages 197-203.

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