A novel machine learning approach for estimation of electricity demand: An empirical evidence from Thailand

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@Article{Mostafavi:2013:ECM,

• author = "Elham Sadat Mostafavi and Seyyed Iman Mostafavi and Arefeh Jaafari and Fariba Hosseinpour",
• title = "A novel machine learning approach for estimation of electricity demand: An empirical evidence from {Thailand}",
• journal = "Energy Conversion and Management",
• volume = "74",
• pages = "548--555",
• year = "2013",
• keywords = "genetic algorithms, genetic programming, Electricity demand, Hybrid method, Simulated annealing, Prediction",
• ISSN = "0196-8904",
• DOI = "doi:10.1016/j.enconman.2013.06.031",
• URL = "http://www.sciencedirect.com/science/article/pii/S0196890413003439",
• size = "8 pages",
• abstract = "We propose an innovative hybrid approach for the estimation of the long-term electricity demand. A new prediction equation was developed for the electricity demand using an integrated search method of genetic programming and simulated annealing, called GSA. The annual electricity demand was formulated in terms of population, gross domestic product (GDP), stock index, and total revenue from exporting industrial products of the same year. A comprehensive database containing total electricity demand in Thailand from 1986 to 2009 was used to develop the model. The generalization of the model was verified using a separate testing data. A sensitivity analysis was conducted to investigate the contribution of the parameters affecting the electricity demand. The GSA model provides accurate predictions of the electricity demand. Furthermore, the proposed model outperforms a regression and artificial neural network-based models.",
• notes = "p554 'A distinctive feature of GSA (GP and simulated annealing) over ANN is that it provides very practical prediction equations.'",

}

Genetic Programming entries for Elham Sadat Mostafavi Seyyed Iman Mostafavi Arefeh Jaafari Fariba Hosseinpour

Citations