Short-Term Power Load Forecasting Based on EMGCSO-Elman
DOI:
https://doi.org/10.4108/ew.14137Keywords:
Chicken swarm optimization algorithm, Short term load forecasting, Elite multi-mode guided strategy, Elman neural networkAbstract
INTRODUCTION: In short-term electrical load forecasting, Elman neural architectures often suffer from convergence instability and limited prediction accuracy due to random parameter initialization. To address this issue, an Elite Multi-mode Guided Chicken Swarm Optimization (EMGCSO) methodology is developed to determine the optimal weights and biases for the Elman network.
OBJECTIVES: The primary objective is to develop an EMGCSO-Elman integrated predictive framework for short-term power load forecasting. This involves designing an enhanced chicken swarm optimization algorithm that effectively guides the search process by classifying roosters based on their fitness scores into elite, failed, and ordinary categories, and leveraging optimal, random, and neighborhood optimal solution information to formulate three distinct search strategies.
METHODS: EMGCSO introduces three search strategies focusing on exploitation, exploration, and a balance between the two. These strategies are guided by elite individuals, random search, and neighborhood optima, respectively. The proposed algorithm is first validated through benchmark function tests. Subsequently, it is applied to optimize the initial parameters (weights and biases) of the Elman network, thereby constructing the EMGCSO-Elman forecasting framework.
RESULTS: Benchmark function test results demonstrate that EMGCSO achieves fast convergence and strong global search capability. When deployed for forecasting electric load in the short term, the EMGCSO-Elman model significantly outperforms BP, Elman, and CSO-Elman models in predictive accuracy together with stability.
CONCLUSION: The proposed EMGCSO-Elman model effectively overcomes the sensitivity of Elman neural networks to initial parameter settings, demonstrating clear superiority for short-term power load prediction tasks.
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[1] HABBAK H, MAHMOUD M, METWALLY K, et al. Load forecasting techniques and their applications in smart grids[J]. Energies, 2023, 16(3): 1480.
[2] JALALIFAR R, DELAVAR M R, GHADERI S F. SAC-ConvLSTM: A novel spatio-temporal deep learning-based approach for a short term power load forecasting[J]. Expert Systems with Applications, 2024, 237: 121487.
[3] PINHEIRO M G, MADEIRA S C, FRANCISCO A P. Short-term electricity load forecasting—A systematic approach from system level to secondary substations[J]. Applied Energy, 2023, 332: 120493.
[4] WANG J, WU K, WEN Y, et al. Short-term electric load forecasting based on enhanced GA-BP multi-parameter algorithm[J]. Measurement, 2025: 117855.
[5] WU H, ZHU X. Short-term electric load forecasting model based on PSO-BP[C]//2023 4th International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE). IEEE, 2023: 224-228.
[6] SU H, LI Z, LI J, et al. Short term load forecasting based on BP network model[C]//2025 6th International Conference on Energy Power and Automation Engineering (ICEPAE). IEEE, 2025: 81-84.
[7] LIN W, ZHANG B, LI H, et al. Short-term load forecasting based on EEMD-Adaboost-BP[J]. Systems Science & Control Engineering, 2022, 10(1): 846-853.
[8] AIHMOUD L, NAWAFLEH Q. Short-term load forecasting for Jordan power system based on NARX-ELMAN neural network and ARMA model[J]. IEEE Canadian Journal of Electrical and Computer Engineering, 2021, 44(3): 356-363.
[9] ISMAEL L, ISMAEL A I. Prediction the data consumption for power demands by Elman neural network[J]. International Journal of Electrical and Computer Engineering (IJECE), 2019, 9(5): 4003-4009.
[10] MAHMOUD I A, MUHAMMAD U J, KAWU S J, et al. Enhancing Energy Demand Prediction Using Elman Neural Network and Support Vector Machine Model: A Case Study in Lagos State, Nigeria[J]. Artificial Intelligence Evolution, 2024: 39-51.
[11] LIU J C, YIN Y. Power load forecasting considering climate factors based on IPSO-Elman method in China. Energies, 2022, 15(3), 1236.
[12] HAO J, ZHU C S, GUO X T. A new CIGWO-Elman hybrid model for power load forecasting. Journal of Electrical Engineering & Technology, 2022, 17: 1319-1333.
[13] LIU Z L, NING D Y, HOU J Y. A novel Elman neural network based on Gaussian kernel and improved SOA and its applications. Expert Systems with Applications, 2024, 249: 123453.
[14] MA X Y, ZHANG X H. A short-term prediction model to forecast power of photovoltaic based on MFA-Elman. Energy Reports, 2022, 8: 495-507.
[15] Narayanan V L, Narayan J, Dhaked D K, et al. Elman Neural Network with Customized Particle Swarm Optimization for Hydraulic Pitch Control Strategy of Offshore Wind Turbine[J]. Processes, 2025, 13(3): 808.
[16] MENG X, LIU Y, GAO X, et al. A new bio-inspired algorithm: chicken swarm optimization[C]//, Fifth International Conference on Swarm Intelligence, Hefei, China, 17 October 2014: 86-94.
[17] Oguntoye J P, Ajagbe S A, Adedeji O T, et al. An improved chicken swarm optimization techniques based on cultural algorithm operators for biometric access control[J]. Computers, Materials & Continua, 2025, 84(3): 5713-5732.
[18] Abbas M, Che Y, Maqsood S, et al. Self-adaptive evolutionary neural networks for high-precision short-term electric load forecasting[J]. Scientific Reports, 2025, 15(1): 21674.
[19] Nagarajan B, Svn S K, Selvi M, et al. A fuzzy based chicken swarm optimization algorithm for efficient fault node detection in Wireless Sensor Networks[J]. Scientific Reports, 2024, 14(1): 27532.
[20] DEB S, GAO X, TAMMI K, et al. Recent studies on chicken swarm optimization algorithm: a review (2014–2018)[J]. Artificial Intelligence Review, 2020, 53(4):1737-1765.
[21] REZAEI F, SAFAVI H R. GU A. SPSO: a new approach to hold a better exploration-exploitation balance in PSO algorithm[J]. Soft Computing, 2020, 24(7): 4855-4875.
[22] ASKARZADEH A, ALIPOUR M A. E2: A basic optimization method using exploration-exploitation concept[J]. Soft Computing, 2025, 29(19): 5519-5539.
[23] KIRAN M S, HAKLI H, GUNDUZ M, et al. Artificial bee colony algorithm with variable search strategy for continuous optimization[J]. Information Sciences, 2015, 300: 140-157.
[24] WANG H, WANG W J, XIAO S Y, et al. Improving artificial bee colony algorithm using a new neighborhood selection mechanism[J]. Information Sciences, 2020, 527: 227-240.
[25] SEKYERE Y O M, EFFAH F B, OKYERE P Y. Hyperbolic tangent-based adaptive inertia weight particle swarm optimization[J]. Jurnal Nasional Teknik Elektro, 2023, 12(2): 89-96.
[26] HAKLI H, KIRAN M S. An improved artificial bee colony algorithm for balancing local and global search behaviors in continuous optimization[J]. International Journal of Machine Learning and Cybernetics, 2020 11(9): 2051-2076.
[27] ZHOU X, LU J, HUANG J, et al. Enhancing artificial bee colony algorithm with multi-elite guidance[J]. Information Sciences, 2021, 543: 242-258.
[28] LYNN N, SUGANTHAN P N. Ensemble particle swarm optimizer[J]. Applied Soft Computing, 2017, 55: 533-548.
[29] XIA X, GUI L, HE G, et al. An expanded particle swarm optimization based on multi-exemplar and forgetting ability[J]. Information Sciences, 2020, 508: 105-120.
[30] LIU Z, NISHI T. Strategy dynamics particle swarm optimizer[J]. Information Sciences, 2022, 582: 665-703.
[31] ALI B, LASHARI S A, SHARIF W, et al. An efficient learning weight of elman neural network with chicken swarm optimization algorithm[J]. Procedia Computer Science, 2021, 192: 3060-3069.
[32] MAHDI Q A M N, ALI M, ATTA M N, et al. Training learning weights of elman neural network using salp swarm optimization algorithm[J]. Procedia Computer Science, 2023, 225: 1974-1986.
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