Economic Load Forecasting of Power System Based on Hybrid Neural Network and Multi-objective Evolutionary Algorithm
DOI:
https://doi.org/10.13052/dgaej2156-3306.405613Keywords:
Wind energy, power systems, economic dispatch, neural networks, forecasting models, multi-objective evolutionary algorithms, distributional diversityAbstract
The accuracy and efficiency of power load forecasting are essential to the power grid’s stability and economic operation due to the ongoing growth of the power system scale and the widespread use of renewable energy. Traditional load forecasting methods are often difficult to balance the forecasting accuracy, economy, and environmental protection. Therefore, the study first combines wide deep neural network with temporal convolutional network to better extract the spatial features and temporal dependencies of load power data. Subsequently, the economic dispatch function under multi-objective is constructed and the function model is solved taking into account the generation cost, pollution emission and voltage deviation. The results indicate that the hybrid neural network has good convergence and loss results, with its loss value approaching 0 in the later stages of iteration. K-means clustering-support vector machines (K-means-SVM) and Improved Whale Optimization Algorithm-Long Short-Term Memory (IWOA-LSTM) are significantly affected by the number of sample points, and their minimum load power prediction error is greater than 0.5%. The training time of the hybrid model is always less than 0.2 seconds, and the maximum prediction accuracy exceeds 95%. The multi-objective evolutionary algorithm proposed in the study exhibits good solution set diversity and convergence. The average running time of the improved Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D) algorithm does not exceed 105 seconds, and the maximum electricity cost does not exceed 2.5 × 104 yuan. The research method provides a new solution for economic load forecasting of the power system, which provides a guarantee for its safe and stable operation.
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References
Wang H, Zhang N, Du E, Yan J, Han S, Liu Y. A comprehensive review for wind, solar, and electrical load forecasting methods. Global Energy Interconnection, 2022, 5(1): 9–30.
Shirzadi N, Nasiri F, El-Bayeh C, Eicker U. Optimal dispatching of renewable energy-based urban microgrids using a deep learning approach for electrical load and wind power forecasting. International Journal of Energy Research, 2022, 46(3): 3173–3188.
Basak S, Dey B, Bhattacharyya B. Uncertainty-based dynamic economic dispatch for diverse load and wind profiles using a novel hybrid algorithm. Environment, Development and Sustainability, 2023, 25(5): 4723–4763.
Jing J, Di H, Wang T, et al. Optimization of power system load forecasting and scheduling based on artificial neural networks. Energy Informatics, 2025, 8(1): 6.
Saglam M, Lv X, Spataru C, et al. Instantaneous electricity peak load forecasting using optimization and machine learning. Energies, 2024, 17(4): 777.
Osgonbaatar T, Matrenin P, Safaraliev M, et al. A rank analysis and ensemble machine learning model for load forecasting in the nodes of the central Mongolian power system. Inventions, 2023, 8(5): 114.
Zhang X, Chau T K, Chow Y H, et al. A novel sequence to sequence data modelling based CNN-LSTM algorithm for three years ahead monthly peak load forecasting. IEEE Transactions on Power Systems, 2023, 39(1): 1932–1947.
Yao Q, Ma B, Zhao T, Hu Y, Fang F. Optimized active power dispatching of wind farms considering data-driven fatigue load suppression. IEEE Transactions on Sustainable Energy, 2022, 14(1): 371–380.
Dong X, Deng S, Wang D. A short-term power load forecasting method based on k-means and SVM. Journal of Ambient Intelligence and Humanized Computing, 2022, 13(11): 5253–5267.
Zhuang Z, Zheng X, Chen Z, Jin T. A reliable short-term power load forecasting method based on VMD-IWOA-LSTM algorithm. IEEJ Transactions on Electrical and Electronic Engineering, 2022, 17(8): 1121–1132.
Guo Y, Li Y, Qiao X, Zhang Z, Zhou W, Mei Y, Lin J, Zhou Y, Nakanishi Y. BiLSTM multitask learning-based combined load forecasting considering the loads coupling relationship for multienergy system. IEEE Transactions on Smart Grid, 2022, 13(5): 3481–3492.
Zhuang W, Fan J, Xia M, Zhu K. A multi-scale spatial–temporal graph neural network-based method of multienergy load forecasting in integrated energy system. IEEE Transactions on Smart Grid, 2023, 15(3): 2652–2666.
Wang Y, Chen J, Chen X, Zeng X, Kong Y, Sun S, Guo Y, Liu Y. Short-term load forecasting for industrial customers based on TCN-LightGBM. IEEE Transactions on Power Systems, 2020, 36(3): 1984–1997.
Yang H, Niu W, Wang X, Gu W. Wind turbine output power forecasting based on temporal convolutional neural network and complete ensemble empirical mode decomposition with adaptive noise. International journal of green energy, 2023, 20(11/15): 1612–1627.
Wang L, Tian T, Xu H, Tong H. Short-term power load forecasting model based on t-SNE dimension reduction visualization analysis, VMD and LSSVM improved with chaotic sparrow search algorithm optimization. Journal of Electrical Engineering & Technology, 2022, 17(5): 2675–2691.
Ajagekar A, You F. Deep reinforcement learning based unit commitment scheduling under load and wind power uncertainty. IEEE Transactions on Sustainable Energy, 2022, 14(2): 803–812.
Hassan M H, Kamel S, Selim A, Shaheen A, Yu J, El-Sehiemy R. Efficient economic operation based on load dispatch of power systems using a leader white shark optimization algorithm. Neural Computing and Applications, 2024, 36(18): 10613–10635.
Wang L, Liu Y, Pang X, Wang Q, Wang X. Environmental economic dispatch method of power system based on multi-objective artificial bee colony algorithm. Electrical engineering, 2024, 106(1): 567–579.
Khan M, Shafi M A, Khosa M S K. An analysis of stochastic wind power approach for economic load dispatch optimization using genetic algorithm. Southern Journal of Engineering & Technology, 2023, 1(2): 36–45.
Shafiee M, Zamani A A, Sajadinia M. Using improved DDAO algorithm to solve economic emission load dispatch problem in the presence of wind farms. International Journal of Industrial Electronics Control and Optimization, 2023, 6(3): 161–169.
Hao W K, Wang J S, Li X D, Wang M, Zhang M. Arithmetic optimization algorithm based on elementary function disturbance for solving economic load dispatch problem in power system. Applied Intelligence, 2022, 52(10): 11846–11872.
Acharya S, Ganesan S, Kumar D V, Subramanian S. Optimization of cost and emission for dynamic load dispatch problem with hybrid renewable energy sources. Soft Computing, 2023, 27(20): 14969–15001.
Pedraza J M. The role of renewable energy in the transition to green, low-carbon power generation in Asia. Green and Low-Carbon Economy, 2023, 1(2): 68–84.
Trong H C, Nguyen T T. Jellyfish search algorithm for economic load dispatch under the considerations of prohibited operation zones, load demand variations, and renewable energy sources. IAES International Journal of Artificial Intelligence, 2024, 13(1): 74–81.
Xiang X, Yuan T, Cao G, et al. Short-term electric load forecasting based on signal decomposition and improved tcn algorithm. Energies, 2024, 17(8): 1815.
Zheng G, Kong L, Su Z, et al. Approach for short-term power load prediction utilizing the ICEEMDAN–LSTM–TCN–bagging model. Journal of Electrical Engineering & Technology, 2025, 20(1): 231–243.
