Estimating the Spanish Energy Demand Using Variable Neighborhood Search

The increasing of the energy demand in every country has lead experts to find strategies for estimating the energy demand of a given country for the next year. The energy demand prediction in the last years has become a hard problem, since there are several factors (like economic crisis, industrial globalization, or population variation) that are not easy to control. For this reason, it is interesting to propose new strategies for efficiently perform this estimation. In this paper we propose a metaheuristic algorithm based on the Variable Neighborhood Search framework which is able to perform an accurate prediction of the energy demand for a given year. The algorithm is supported in a previously proposed exponential model for estimating the energy, and its input is conformed with a set of macroeconomic variables gathered during the last years. Experimental results show the excellent performance of the algorithm when compared with both previous approaches and the actual values.

[1]  Marc Sevaux,et al.  Solving dynamic memory allocation problems in embedded systems with parallel variable neighborhood search strategies , 2015, Electron. Notes Discret. Math..

[2]  Harun Kemal Ozturk,et al.  Estimating energy demand of Turkey based on economic indicators using genetic algorithm approach , 2004 .

[3]  Juan José Pantrigo,et al.  Parallel variable neighbourhood search strategies for the cutwidth minimization problem , 2016 .

[4]  Kejun Zhu,et al.  Energy demand projection of China using a path-coefficient analysis and PSO–GA approach , 2012 .

[5]  Juan José Pantrigo,et al.  Combining intensification and diversification strategies in VNS. An application to the Vertex Separation problem , 2014, Comput. Oper. Res..

[6]  Turan Paksoy,et al.  A novel hybrid approach based on Particle Swarm Optimization and Ant Colony Algorithm to forecast energy demand of Turkey , 2012 .

[7]  Ke Wang,et al.  A PSO–GA optimal model to estimate primary energy demand of China , 2012 .

[8]  Seyed Farid Ghaderi,et al.  Energy demand forecasting in Iranian metal industry using linear and nonlinear models based on evolutionary algorithms , 2012 .

[9]  Shiwei Yu,et al.  A hybrid procedure for energy demand forecasting in China , 2012 .

[10]  Sancho Salcedo-Sanz,et al.  One-year-ahead energy demand estimation from macroeconomic variables using computational intelligence algorithms , 2015 .

[11]  Murat Kankal,et al.  Modeling and forecasting of Turkey's energy consumption using socio-economic and demographic variables , 2011 .

[12]  V. Ediger,et al.  ARIMA forecasting of primary energy demand by fuel in Turkey , 2007 .

[13]  Alper Ünler,et al.  Improvement of energy demand forecasts using swarm intelligence: The case of Turkey with projections to 2025 , 2008 .

[14]  Fred W. Glover,et al.  Hybrid scatter tabu search for unconstrained global optimization , 2011, Ann. Oper. Res..

[15]  William E. Roper,et al.  Energy demand estimation of South Korea using artificial neural network , 2009 .

[16]  Turan Paksoy,et al.  Swarm intelligence approaches to estimate electricity energy demand in Turkey , 2012, Knowl. Based Syst..

[17]  Pierre Hansen,et al.  Variable neighborhood search: Principles and applications , 1998, Eur. J. Oper. Res..

[18]  M. Toksari.  Ant colony optimization approach to estimate energy demand of Turkey , 2007 .

[19]  M. Duran Toksarı,et al.  Estimating the net electricity energy generation and demand using the ant colony optimization approach: Case of Turkey , 2009 .