Abstract
In this paper we tackle a problem of one-year ahead energy demand estimation from macroeconomic variables. A modified Harmony Search (HS) algorithm is proposed to this end, as one of the novelties of the paper. The modifications on the proposed HS include a hybrid encoding, with a binary part to carry out a feature selection, and a real part, to select the parameter of a given prediction model. Some other adaptation focussed on the HS operators are also introduced. We study the performance of the proposed approach in a real problem of Energy demand estimation in Spain, from 14 macroeconomic variables with values for the last 30 years, including years of the crisis, from 2008. The performance of the proposed HS with feature selection is excellent, providing an accurate one year ahead prediction that improves previous proposals in the literature.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Suganthi, L., Samuel, A.A.: Energy models for demand forecasting – A review. Renewable and Sustainable Energy Reviews 16, 1223–1240 (2012)
CSIRO and the Natural Edge Project. Energy Transformed: sustainable energy solutions for climate change mitigation, p. 6 (2007)
Ceylan, H., Ozturk, H.K.: Estimating energy demand of Turkey based on economic indicators using genetic algorithm approach. Energy Conversion and Management 45, 2525–2537 (2004)
Ünler, A.: Improvement of energy demand forecasts using swarm intelligence: The case of Turkey with projections to 2025. Energy Policy 36, 1937–1944 (2008)
Kiran, M.S., Özceylan, E., Gündüz, M., Paksoy, T.: Swarm intelligence approaches to estimate electricity energy demand in Turkey. Knowledge-Based Systems 36, 93–103 (2012)
Yu, S., Zhu, K.J.: A hybrid procedure for energy demand forecasting in China. Energy 37, 396–404 (2012)
Kiran, M.S., Özceylan, E., Gündüz, M., Paksoy, T.: A novel hybrid approach based on Particle Swarm Optimization and Ant Colony Optimization to forecast energy demand of Turkey. Energy Conversion and Management 53, 75–83 (2012)
Yu, S., Wei, Y.M., Wang, K.: A PSO-GA optimal model to estimate primary energy demand of China. Energy Policy 42, 329–340 (2012)
Yu, S., Zhu, K., Zhang, X.: Energy demand projection of China using a path-coefficient analysis and PSO-GA approach. Energy Conversion and Management 53, 142–153 (2012)
Piltan, M., Shiri, H., Ghaderi, S.F.: Energy demand forecasting in Iranian metal industry using linear and nonlinear models based on evolutionary algorithms. Energy Conversion and Management 58, 1–9 (2012)
Geem, Z.W., Hoon Kim, J., Loganathan, G.V.: A New Heuristic Optimization Algorithm: Harmony Search. Simulation 76(2), 60–68 (2001)
Manjarres, D., Landa-Torres, I., Gil-Lopez, S., Del Ser, J., Bilbao, M.N., Salcedo-Sanz, S., Geem, Z.W.: A survey on applications of the harmony search algorithm. Engineering Applications of Artificial Intelligence 26, 1818–1831 (2013)
Geem, Z.W.: Novel derivative of harmony search algorithm for discrete design variables. Applied Mathematics and Computation 199(1), 223–230 (2008)
Geem, Z.W., Sim, K.B.: Parameter-setting-free harmony search algorithm. Applied Mathematics and Computation 217(8), 3881–3889 (2010)
Salcedo-Sanz, S., Camps-Valls, G., Pérez-Cruz, F., Sepúlveda-Sanchis, J., Bousoño-Calzón, C.: Enhancing genetic feature selection through restricted search and Walsh analysis. IEEE Transactions on Systems, Man and Cyberntics–Part C 34(4) (2004)
Wang, L., Yang, R., Xu, Y., Niu, Q., Pardalos, P.M., Fei, M.: An improved adaptive binary Harmony Search algorithm. Information Sciences 232, 58–87 (2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Salcedo-Sanz, S., Portilla-Figueras, J.A., Muñoz-Bulnes, J., del Ser, J., Bilbao, M.N. (2014). A Novel Harmony Search Algorithm for One-Year-Ahead Energy Demand Estimation Using Macroeconomic Variables. In: de la Puerta, J., et al. International Joint Conference SOCO’14-CISIS’14-ICEUTE’14. Advances in Intelligent Systems and Computing, vol 299. Springer, Cham. https://doi.org/10.1007/978-3-319-07995-0_25
Download citation
DOI: https://doi.org/10.1007/978-3-319-07995-0_25
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07994-3
Online ISBN: 978-3-319-07995-0
eBook Packages: EngineeringEngineering (R0)