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

作者：Mostafavi Elham Sadat^*; Mostafavi Seyyed Iman; Jaafari Arefeh; Hosseinpour Fariba

来源：Energy Conversion and Management, 2013, 74: 548-555.

DOI：10.1016/j.enconman.2013.06.031

• Electricity demand
• Hybrid method
• Genetic programming
• Simulated annealing
• Prediction

This study proposes 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.

• 出版日期2013-10