Algoritme Genetika untuk Peningkatan Prediksi Kebutuhan Permintaan Energi Listrik
Abstract
Predicting the demand of electrical energy with a high degree of accuracy is expected. Application of an appropriate model using exact method will greatly affect the level of accuracy result. Neural Network (NN) and Support Vector Machine (SVM) models are used to predict the needs of electricity demand. Those models have weaknesses. Both are still difficult in determining the value of parameters used, thus, affecting the level of accuracy. Genetic Algorithm (GA) is proposed as a method to optimize the value of NN and SVM parameters in predicting the demand of electrical energy. The result shows that the NN and GA models have a better accuracy than the SVM and GA.
References
R.Menaa, F. Rodríguez, M. Castilla & M.R. Arahal, "A prediction model based on neural networks for the energy consumption of a bioclimatic building ," Energy and Buildings 8, 2014 , pp. 142–155
Ping Zhang & Hui Wang, "Fuzzy Wavelet Neural Networks for City Electric Energy Consumption Forecasting", 2012 International Conference on Future Electrical Power and Energy Systems, Energy Procedia 17, ( 2012 ), pp.1332 – 1338
Council, E.P.A., “Directive 2010/31/EU of the European Parliament and of the Council of 19 May, 2010 on the energy performance of buildings “. Official Journal of the European Union; 2010. pp.13–35.
L. Ekonomou, "Greek Long-Term Energy Consumption Prediction Using Artificial Neural Networks", Energy 35 ,(2010), pp. 512–517
Yang L, Lam JC, Tsang CL. “Energy performance of building envelopes in different climate zones in China”. Appl Energy 2008; 85: 800–17.
A.S.Ahmad, M.Y.Hassan, M.P.Abdullah, H.A.Rahman, F.Hussin, H.Abdullah & R.Saidur, “A review on applications of ANN and SVM for building electrical energy consumption forecasting“, Renewable and Sustainable Energy Reviews 33, (2014), p.102
Zebulum Ricardo S, Vellasco Marley, Guedes Karla & Pacheco Marco Aurélio. “Short term load forecasting using neural nets”. In : Mira J, Sandoval F, editors. From natural to artificial neural computation. Berlin Heidelberg : Springer; 1995, pp.1001–8.109
R.E. Abdel-Aal, "Univariate modeling and forecasting of monthly energy demand time series using abductive and neural networks", Computers & Industrial Engineering 54, (2008), 903–917
Zhao H-X, MagoulèsF. “A review on the prediction of building energy consumption. Renew Sustain Energy Rev “, 2012 ;16(6) : 3586–92.
Lee Yi-Shian,TongL-I. “Forecasting timeseries using a methodology based on autoregressive integrated moving average and genetic programming.” Knowl- Based Syst 2011; 24(1) : 66–72.
Mohammed El-Telbany & Fawwaz El-Karmi, ”Short-Term Forecasting Of Jordanian Electricity Demand Using Particle Swarm Optimization”, Electric Power Systems Research 78 , 2008, pp. 425–433
Mehmet Bilgili, Besir Sahin, Abdulkadir Yasara & Erdogan Simseka, "Electric energy demands of Turkey in residential and industrial sectors", Renewable and Sustainable Energy Reviews 16, (2012), pp. 404– 414
A. Kheirkhah, A. Azadeh , M. Saberi , A. Azaron & H. Shakouri, "Improved estimation of electricity demand function by using of artificial neural network, principal component analysis and data envelopment analysis", Computers & Industrial Engineering 64, (2013), pp. 425–441
Fausett, L., Fundamental Of Neural Network Architecture, Algorithms, And Applications. New Jersey: Prentice Hall, 1994.
Shi, Y., Tian, Y., & Kou, G., Optimization Based Data Mining Theory and Applications. (xx, Ed.). Springer London., 2011.
Ilhan, I., & Tezel, G., “A Genetic Algorithm-Support Vector Machine Method With Parameter Optimization For Selecting The Tag SNPs”. Journal of Biomedical Informatics, 46(2), 2013.
Wu, G., ”Research on Parameter Optimization of Neural Network”. International Journal of Hybrid Information Technology Vol. 2, No. 1, January, 2009, 81.
Moro, S., & Laureano, R. M. S., “Using Data Mining for Bank Direct Marketing: An application of the CRISP-DM methodology”. European Simulation and Modelling Conference, (Figure 1), pp.117–121, 2012.
Haupt, R. L. ., & Haupt, S. E., Practical Genetic Algorithms (Second Edi.). New Jersey: A John Wiley and Sons, Ltd., 2004.
Shukla, A., Tiwari, R., & Kala, R., Real Life Applications of Soft Computing. New York: CRC Press, 2010.
Astuti, E. D. Pengantar Jaringan Saraf Tiruan. Wonosobo: Star Publishing, 2009.
Purnomo, M. H., & Kurniawan, A., Supervised Neural Network. Surabaya: Graha Ilmu, 2006.
Gorunescu, F., Intelligent Systems Reference Library. (Gorunescu, Ed.), 2011.
Cortes, C., & Vapnik, V., “Support vector machine”. In Machine learning (pp. 1303–1308). doi:10.1007/978-0-387-730035_299, 1995.
Purnomo, M. H., & Kurniawan, A., Supervised Neural Network. Surabaya: Graha Ilmu, 2006.
Weise, T., Global Optimization Algorithms – Theory and Application (Second Edition.). it-weise.de (self-published), 2009.
© Jurnal Nasional Teknik Elektro dan Teknologi Informasi, under the terms of the Creative Commons Attribution-ShareAlike 4.0 International License.
1.png)
