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Comparison between radial basis neural network improvement method with SALP optimization algorithm (RBF-SSA) with other hybrid optimization algorithms | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 234، دوره 13، شماره 2، مهر 2022، صفحه 2923-2932 اصل مقاله (931.5 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2022.27662.3673 | ||
| نویسندگان | ||
| Mohsen Ahmadnia* ؛ Ahmad Hajipour؛ Seyed Saeed Bani Fatemi | ||
| Faculty of Electrical Engineering and Computer, Hakim Sabzevari University, Sabzevar, Iran | ||
| تاریخ دریافت: 23 اسفند 1400، تاریخ بازنگری: 10 اردیبهشت 1401، تاریخ پذیرش: 16 اردیبهشت 1401 | ||
| چکیده | ||
| In the electricity industry, load forecasting is one of the most important tasks in planning, distribution, operations management, and providing appropriate solutions for power systems. Power consumption plays an important role in the planning and optimal use of power systems. With the existing technology, it is not yet possible to store this energy in large dimensions, so accurate forecasting of consumption can play an important role in the economic use of electricity. The amount of electrical charge consumption is not constant but is complex and nonlinearly a function of several parameters. Due to the variable amount of electrical charge consumption, power companies must anticipate it in different timelines of the information needed to make decisions. In this article, a new method is presented according to the efficiency of short-term load prediction, which can be from the next few hours to a week or a few weeks. Due to the efficiency of evolutionary methods in setting the parameters of forecasting methods, in this paper, the SALP optimization algorithm is used as an algorithm with high convergence accuracy to improve the neural network of the radial base function. Therefore, in this paper, a comparison between the method of improving the neural network of the radial base function with the SALP optimization algorithm for short-term load prediction by considering meteorological factors with other combined methods of optimization algorithms is shown. The results of comparison between predictions in the proposed model (improved neural network with SALP algorithm) compared to other combined methods of load prediction, show that the proposed neural network method improves the radial base function with SALP (RBF-SSA) better. Other combined methods improve the results. | ||
| کلیدواژه‌ها | ||
| Short Term Load Prediction؛ Radial Base Function Neural Network؛ SALP Optimization Algorithm | ||
| مراجع | ||
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[1] A.Z. Ala’M, A.A. Heidari, M. Habib, H. Faris, I. Aljarah and M.A. Hassonah, SALP chain-based optimization of support vector machines and feature weighting for medical diagnostic information systems, Evolutionary Machine Learning Techniques, (2020), 11–34. [2] I. Aljarah, H. Faris, S. Mirjalili and N. Al-Madi, Training radial basis function networks using biogeography-based optimizer, Neural Comput. Appl. 29 (2016), no. 7, 529–553. [3] M. Barman, N.D. Choudhury and S. Sutradhar, A regional hybrid SSA-SVM model based on similar day approach for short-term load forecasting in Assam, India, Energy 145 (2018), 710–720. [4] G. Cervone, L. Clemente-Harding, S. Alessandrini and L. Delle Monache, Short-term photovoltaic power forecasting using artificial neural networks and an analog ensemble, Renew. Energy 108 (2017), 274–286. [5] G.W. Chang, H.J. Lu, Y.R. Chang and Y.D. Lee, An improved neural network-based approach for short-term wind speed and power forecast, Renew. Energy 105 (2017), 301–311. [6] S. Ding, K.W. Hipel and Y.G. Dang, Forecasting China’s electricity consumption using a new grey prediction model, Energy 149 (2018), 314–328. [7] Y. Liang, D. Niu and W.C. Hong, Short term load forecasting based on feature extraction and improved general regression neural network model, Energy 166 (2019), 653–663. [8] Y. Liu, W. Wang and N. Ghadimi, Electricity load forecasting by an improved forecast engine for building level consumers, Energy 139 (2017), 18–30. [9] M. Meng, L. Wang and W. Shang, Decomposition and forecasting analysis of China’s household electricity consumption using three-dimensional decomposition and hybrid trend extrapolation models, Energy 165 (2018), 143– 152. [10] S. Mirjalili, A.H. Gandomi, S.Z. Mirjalili, S. Saremi, H. Faris and S.M. Mirjalili, SALP swarm algorithm: a bio-inspired optimizer for engineering design problems, Adv. Engin. Software 114 (2017), 163–191. [11] M.Q. Raza, M. Nadarajah, D.Q. Hung and Z. Baharudin, An intelligent hybrid short-term load forecasting model for smart power grids, Sustain. Cities Soc. 31 (2017), 264–275. [12] S.R. Salkuti, Short-term electrical load forecasting using radial basis function neural networks considering weather factors, Electr. Eng. 100 (2018), no. 3, 1985–1995. [13] A. Tarsitano and I.L. Amerise, Short-term load forecasting using a two-stage sarimax model, Energy 133 (2017), 108–114. [14] W. Yang, J. Wang, T. Niu and P. Du, A hybrid forecasting system based on a dual decomposition strategy and multi-objective optimization for electricity price forecasting, Appl. Energy 235 (2019), 1205–1225. [15] W. Yin, Y. Han, H. Zhou, M. Ma, L. Li and H. Zhu, A novel non-iterative correction method for short-term photovoltaic power forecasting, Renew. Energy 159 (2020), 23–32. [16] J. Zhang, Y.M. Wei, D. Li, Z. Tan and J. Zhou, Short term electricity load forecasting using a hybrid model, Energy 158 (2018), 774–781. | ||
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