Time Series Forecasting with Artificial Neural Network Trained by Imperialist Competitive Algorithm: A Case Study of Renewable Energy Production and Consumption
Subject Areas : Renewable energies and artificial intelligenceMohammad Amirkhan 1 , Salman Amirkhan 2 , Mohammad Reza Aloustani 3
1 - Department of Industrial Engineering, Aliabad Katoul Branch, Islamic Azad University, Aliabad Katoul, Iran
2 - Department of Electrical Engineering, Aliabad Katoul Branch, Islamic Azad University, Aliabad Katoul, Iran
3 - Department of Industrial Engineering, Aliabad Katoul Branch, Islamic Azad University, Aliabad Katoul, Iran
Keywords: Forecasting, Time Series, Artificial Neural Network, Imperialist Competitive Algorithm, Renewable Energy Production and Consumption,
Abstract :
The use of renewable energy sources is very valuable as an important aspect of the sustainability and progress of societies in the future. In recent times, the importance of developing the use of renewable energy and replacing fossil energy with this type of energy is not hidden from any country. In recent times, the importance of developing the use of renewable energy and replacing fossil energy with this type of energy is not hidden from any country, and therefore, the need for powerful models to forecast the amount of production and consumption of this energy is very necessary. The application of evolutionary neural network as a powerful forecasting technique has been gaining more and more attention in recent researches. In this paper, an efficient method based on the artificial neural network (ANN) has been presented to forecast the renewable energy production and consumption. To enhance the efficiency of the network, an evolutionary algorithm called imperialist competitive algorithm (ICA) has been applied to optimize the network weights. The performance of the ANN-ICA is compared with ANN on real data of the aforementioned case study and the results demonstrate the effectiveness of the ANN-ICA.
1. Siddique, N. and H. Adeli, Computational intelligence: synergies of fuzzy logic, neural networks and evolutionary computing. 2013: John Wiley & Sons.
2. Atashpaz-Gargari, E. and C. Lucas. Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. in Evolutionary Computation, 2007. CEC 2007. IEEE Congress on. 2007. IEEE.
3. Bhattacharya, M., et al., The effect of renewable energy consumption on economic growth: Evidence from top 38 countries. Applied energy, 2016. 162: p. 733-741.
4. Wang, Q., F. Zhang, and R. Li, Revisiting the environmental kuznets curve hypothesis in 208 counties: The roles of trade openness, human capital, renewable energy and natural resource rent. Environ Res, 2023. 216(Pt 3): p. 114637.
5. Adebiyi, A.A., A.O. Adewumi, and C.K. Ayo, Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction. Journal of Applied Mathematics, 2014. 2014.
6. Abu-Salih, B., et al., Short-term renewable energy consumption and generation forecasting: A case study of Western Australia. Heliyon, 2022. 8(3): p. e09152.
7. Ali, N.S.M. and F.A. Mohammed, The use of ARIMA, ANN and SVR models in time series hybridization with practical application. International Journal of Nonlinear Analysis and Applications, 2023. 14(3): p. 87-102.
8. Zhang, C. and X. Zhou, Forecasting value-at-risk of crude oil futures using a hybrid ARIMA-SVR-POT model. Heliyon, 2024. 10(1): p. e23358.
9. Menéndez-García, L.A., et al., Time series analysis for COMEX platinum spot price forecasting using SVM, MARS, MLP, VARMA and ARIMA models: A case study. Resources Policy, 2024. 95: p. 105148.
10. Peyghami, M.R. and R. Khanduzi, Predictability and forecasting automotive price based on a hybrid train algorithm of MLP neural network. Neural Computing and Applications, 2012. 21(1): p. 125-132.
11. Haykin, S., Neural networks: a comprehensive foundation, 1994. Mc Millan, New Jersey, 2010.
12. Wang, R.L., Z. Tang, and Q.P. Cao, An efficient approximation algorithm for finding a maximum clique using Hopfield network learning. Neural computation, 2003. 15(7): p. 1605-1619.
13. Mahmoudi, M.T., et al. Artificial neural network weights optimization based on imperialist competitive algorithm. in 7th International conference on computer science and information technologies (CSIT’09), Yerevan. 2009.
14. Hu, M.J.-C., Application of the adaline system to weather forecasting. 1964, Department of Electrical Engineering, Stanford University.
15. Lapedes, A. and R. Farber, Nonlinear signal processing using neural networks. 1987.
16. Khalili-Damghani, K. and S. Sadi-Nezhad, Application of multi-layer recurrent neural network in chaotic time series prediction: a real case study of crude oil distillation capacity. International Journal of Artificial Intelligence and Soft Computing, 2011. 2(4): p. 367-380.
17. Zhang, G., B. Eddy Patuwo, and M. Y Hu, Forecasting with artificial neural networks:: The state of the art. International journal of forecasting, 1998. 14(1): p. 35-62.
18. Kumar, S., Neural networks: a classroom approach. 2004: Tata McGraw-Hill Education.
19. Peyghami, M.R. and R. Khanduzi, A hybrid model based on neural network and hybrid genetic algorithm for automotive price forecasting. International Journal of Applied Mathematics and Computation, 2011. 3(3): p. 158-168.
20. Tang, Z. and P.A. Fishwick, Feedforward neural nets as models for time series forecasting. ORSA Journal on Computing, 1993. 5(4): p. 374-385.
21. Che, Z., PSO-based back-propagation artificial neural network for product and mold cost estimation of plastic injection molding. Computers & Industrial Engineering, 2010. 58(4): p. 625-637.
22. Hecht-Nielsen, R. Kolmogorov’s mapping neural network existence theorem. in Proceedings of the international conference on Neural Networks. 1987. New York: IEEE Press.
23. Zhang, X., Time series analysis and prediction by neural networks. Optimization Methods and Software, 1994. 4(2): p. 151-170.
24. Lee, C.-M. and C.-N. Ko, Time series prediction using RBF neural networks with a nonlinear time-varying evolution PSO algorithm. Neurocomputing, 2009. 73(1): p. 449-460.
25. Azoff, E.M., Neural network time series forecasting of financial markets. 1994: John Wiley & Sons, Inc.
26. Lapedes, A.S. and R.M. Farber. How neural nets work. in Neural information processing systems. 1988.
27. Srinivasan, D., A. Liew, and C. Chang, A neural network short-term load forecaster. Electric Power Systems Research, 1994. 28(3): p. 227-234.
28. Weigend, A.S., B.A. Huberman, and D.E. Rumelhart. Predicting sunspots and exchange rates with connectionist networks. in SANTA FE INSTITUTE STUDIES IN THE SCIENCES OF COMPLEXITY-PROCEEDINGS VOLUME-. 1992. ADDISON-WESLEY PUBLISHING CO.
29. Lachtermacher, G. and J.D. Fuller, Back propagation in time‐series forecasting. Journal of Forecasting, 1995. 14(4): p. 381-393.
30. Cai, X., et al., Time series prediction with recurrent neural networks trained by a hybrid PSO–EA algorithm. Neurocomputing, 2007. 70(13): p. 2342-2353.