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Energy consumption forecasting is vitally important for the deregulated electricity industry in the world. A large variety of mathematical models have been developed in the literature for energy forecasting. However, researchers are involved in developing novel methods to estimate closer values. In this paper, authors attempted to develop new models in minimizing the forecasting errors. In the present study, the economic indicators of the state including population, gross state domestic product, yearly peak demand, and per capita income were considered for forecasting the electricity consumption of a state in a developing country. Initially, a multiple linear regression model has been developed. Then, the coefficients of the regression model were optimized using two heuristic approaches namely genetic algorithm and simulated annealing. The mean absolute percentage error obtained for the three models were 2.00 for multiple linear regression model, 1.94 for genetic algorithm based linear regression and 1.86 for simulated annealing based linear regression.
PAPER REVISED: 2018-01-16
PAPER ACCEPTED: 2018-02-02
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THERMAL SCIENCE YEAR 2019, VOLUME 23, ISSUE Issue 5, PAGES [2885 - 2894]
  1. Aghay Kaboli., et a.l, Long-term electric energy consumption forecasting via artificial cooperative search algorithm, Energy, 115 (2016) , pp.857-871
  2. Amjadi M.H., et al., Estimation of electricity demand of Iran using two heuristic algorithms, Energy Conversion and Management, 51 (2010), pp.493-497
  3. Ardakani F.J., et al., Long-term electrical energy consumption forecasting for developing and developed economies based on different optimized models and historical data types, Energy, 65 (2014), pp.452-461
  4. Arturo Morales., et al., Forecasting future energy demand: Electrical energy in Mexico as an example case, Energy Procedia, 57 (2014), pp.782-790
  5. Assareh E., et al., Application of PSO and GA techniques on demand estimation of oil in Iran, Energy, 35 (2010), pp.5223-5229
  6. Azadeh A., et al., Integration of genetic algorithm, computer simulation and design of experiments for forecasting electrical energy consumption, Energy Policy, 35 (2007), pp.5229-5241
  7. Azadeh, S.F., et al., Integration of artificial neural networks and genetic algorithm to predict electrical energy consumption, Applied Mathematics and Computation, 186 (2007), pp.1731-1741
  8. Didem Cinar., et al., Development of future energy scenarios with intelligent algorithms: Case of hydro in Turkey, Energy, 35 (2010), pp.1724-1729
  9. Harun Kemal Ozturk., et al., Electricity estimation using genetic algorithm approach: a case study of Turkey, Energy, 30 (2005), pp.1003-1012
  10. Jose Francisco., et al., Forecasting long-term electricity demand in the residential sector, Procedia Computer Science, 55 (2015), pp.529-538
  11. Julian Perez-Garcia., et al., Analysis and long term forecasting of electricity demand trough a decomposition model: A case study for Spain, Energy, 97 (2016), pp.121-143
  12. Kumar B D., et al., Monjur Moursheda, Samuel Pak Kheong Chewa, Modelling and forecasting energy demand in rural households of Bangladesh, Energy Procedia, 75 (2015), pp.2731-2737
  13. Mehdi Piltan., et al., Energy demand forecasting in Iranian metal industry using linear and nonlinear models based on evolutionary algorithms, Energy Conversion and Management, 58 (2012), pp.1-9
  14. Nyun-Bae Park., et al., An analysis of long-term scenarios for the transition to renewable energy in the Korean electricity sector, Energy Policy, 52 (2013), pp.288-296
  15. Olcay Ersel Canyurt., et al, Application of genetic algorithm (GA) technique on demand estimation of fossil fuels in Turkey, Energy Policy, 36 (2008) pp.2562- 2569
  16. Olcay Ersel Canyurt., et al., Three different applications of genetic algorithm search techniques on oil demand estimation, Energy Conversion and Management, 47 (2006), pp.3138-3148
  17. Ping-Feng Pai., et al., Support vector machines with simulated annealing algorithms in electricity load forecasting, Energy Conversion and Management, 46 (2005), pp.2669-2688
  18. Shiwei Yu., et al., Energy demand projection of China using a path-coefficient analysis and PSO-GA approach, Energy Conversion and Management, 53 (2012a), pp.142-153
  19. Shi-wei Yu., et al., A hybrid procedure for energy demand forecasting in China, Energy, 37 (2012b), pp.396-404
  20. Shiwei Yu., et al., A PSO-GA optimal model to estimate primary energy demand of China, Energy Policy, 42 (2012c), pp.329-340
  21. Usama Perwez., et al., The long-term forecast of Pakistan's electricity supply and demand: An application of long range energy alternatives planning, Energy, 93 (2015), pp.2423- 2435
  22. Yi-Shian Lee., et al., Forecasting energy consumption using a grey model improved by incorporating genetic programming, Energy Conversion and Management, 52 (2011), pp. 147-341
  23. Yi-Shian Lee., et al., Forecasting nonlinear time series of energy consumption using a hybrid dynamic model, Applied Energy, 94 (2012), pp. 251-256
  24. Zaid Mohamed., et al., Forecasting electricity consumption in New Zealand using economic and demographic variables, Energy, 30 (2005), pp. 1833-1843

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