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Forecasting of peak electricity demand in Mauritius using the non-homogeneous Gompertz diffusion process

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  • Badurally Adam, N.R.
  • Elahee, M.K.
  • Dauhoo, M.Z.

Abstract

In this study, the non-homogeneous Gompertz diffusion process (NHGDP) is used to model the monthly peak electricity demand in Mauritius in order to predict the future values on the basis of a Genetic Algorithm (GA) approach. Our model is developed based a key economic indicator which is the gross domestic product (GDP) and the weather factors such as temperature, hours of sunshine and humidity. Genetic Algorithm then searches for the best coefficients by minimizing the root mean square error. Monthly data from January 2005 to December 2008 are considered to test the model. Finally, the Artificial Neural Network (ANN) is used to forecast each independent variable for the year 2009 and the NHGDP model is validated for that year. Our results show that the model provides an accurate and reliable prediction for the monthly peak electricity demand in Mauritius.

Suggested Citation

  • Badurally Adam, N.R. & Elahee, M.K. & Dauhoo, M.Z., 2011. "Forecasting of peak electricity demand in Mauritius using the non-homogeneous Gompertz diffusion process," Energy, Elsevier, vol. 36(12), pages 6763-6769.
    • Handle: RePEc:eee:energy:v:36:y:2011:i:12:p:6763-6769
    DOI: 10.1016/j.energy.2011.10.027
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    as
    1. Heidari, M.D. & Omid, M., 2011. "Energy use patterns and econometric models of major greenhouse vegetable productions in Iran," Energy, Elsevier, vol. 36(1), pages 220-225.
    2. Rout, Ullash K. & Voβ, Alfred & Singh, Anoop & Fahl, Ulrich & Blesl, Markus & Ó Gallachóir, Brian P., 2011. "Energy and emissions forecast of China over a long-time horizon," Energy, Elsevier, vol. 36(1), pages 1-11.
    3. Gutiérrez, R. & Gutiérrez-Sánchez, R. & Nafidi, A., 2006. "Electricity consumption in Morocco: Stochastic Gompertz diffusion analysis with exogenous factors," Applied Energy, Elsevier, vol. 83(10), pages 1139-1151, October.
    4. Gutiérrez, R. & Nafidi, A. & Gutiérrez Sánchez, R., 2005. "Forecasting total natural-gas consumption in Spain by using the stochastic Gompertz innovation diffusion model," Applied Energy, Elsevier, vol. 80(2), pages 115-124, February.
    5. Azadeh, A. & Ghaderi, S.F. & Sohrabkhani, S., 2008. "A simulated-based neural network algorithm for forecasting electrical energy consumption in Iran," Energy Policy, Elsevier, vol. 36(7), pages 2637-2644, July.
    6. Ozturk, Harun Kemal & Ceylan, Halim & Canyurt, Olcay Ersel & Hepbasli, Arif, 2005. "Electricity estimation using genetic algorithm approach: a case study of Turkey," Energy, Elsevier, vol. 30(7), pages 1003-1012.
    7. Saab, Samer & Badr, Elie & Nasr, George, 2001. "Univariate modeling and forecasting of energy consumption: the case of electricity in Lebanon," Energy, Elsevier, vol. 26(1), pages 1-14.
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    2. Rosa Galleguillos-Pozo & Bruno Domenech & Laia Ferrer-Martí & Rafael Pastor, 2022. "Balancing Cost and Demand in Electricity Access Projects: Case Studies in Ecuador, Mexico and Peru," Mathematics, MDPI, vol. 10(12), pages 1-19, June.
    3. Trotter, Ian Michael & Féres, José Gustavo & Bolkesjø, Torjus Folsland & de Hollanda, Lavínia Rocha, 2015. "Simulating Brazilian Electricity Demand Under Climate Change Scenarios," Working Papers in Applied Economics 208689, Universidade Federal de Vicosa, Departamento de Economia Rural.
    4. Eva María Ramos-Ábalos & Ramón Gutiérrez-Sánchez & Ahmed Nafidi, 2020. "Powers of the Stochastic Gompertz and Lognormal Diffusion Processes, Statistical Inference and Simulation," Mathematics, MDPI, vol. 8(4), pages 1-13, April.
    5. Zhang, Ning & Li, Zhiying & Zou, Xun & Quiring, Steven M., 2019. "Comparison of three short-term load forecast models in Southern California," Energy, Elsevier, vol. 189(C).
    6. Carolina Deina & João Lucas Ferreira dos Santos & Lucas Henrique Biuk & Mauro Lizot & Attilio Converti & Hugo Valadares Siqueira & Flavio Trojan, 2023. "Forecasting Electricity Demand by Neural Networks and Definition of Inputs by Multi-Criteria Analysis," Energies, MDPI, vol. 16(4), pages 1-24, February.
    7. Kong, Xiangyu & Li, Chuang & Wang, Chengshan & Zhang, Yusen & Zhang, Jian, 2020. "Short-term electrical load forecasting based on error correction using dynamic mode decomposition," Applied Energy, Elsevier, vol. 261(C).
    8. Trotter, Philipp A. & McManus, Marcelle C. & Maconachie, Roy, 2017. "Electricity planning and implementation in sub-Saharan Africa: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 1189-1209.
    9. Mungur, Maheshsingh & Poorun, Yashna & Juggurnath, Diksha & Ruhomally, Yusra Bibi & Rughooputh, Reshma & Dauhoo, Muhammad Zaid & Khoodaruth, Abdel & Shamachurn, Heman & Gooroochurn, Mahendra & Boodia,, 2020. "A numerical and experimental investigation of the effectiveness of green roofs in tropical environments: The case study of Mauritius in mid and late winter," Energy, Elsevier, vol. 202(C).
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