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Load Forecasting Method Based on QPSO-LSTM
- 1 South-Central Minzu University, Hubei Province of China
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Abstract
With the continuous expansion of the power system scale and the increasing growth of electricity demand, accurate load forecasting is of great significance for the safe, stable operation and economic dispatch of the power system. Traditional load forecasting methods have certain limitations in handling complex load data and are difficult to meet actual needs. This paper proposes a load forecasting method based on the Quantum Particle Swarm Optimization(QPSO) algorithm and the Long Short-Term Memory(LSTM) network. Firstly, the load data is pre-processed, including normalization and dataset division. Then, the LSTM model is used to learn the time-series characteristics of the load data. At the same time, the QPSO algorithm is adopted to optimize the hyperparameters of the LSTM model to improve the prediction performance of the model. Through practical example analysis, it is shown that this method can effectively improve the accuracy and stability of load forecasting. Compared with traditional methods, it performs better in handling complex load data and can provide a more reliable decision-making basis for the planning and operation of the power system.
Keywords
Load Forecasting,Long Short-Term Memory , Quantum-behaved Particle Swarm Optimization
[1]. Qian Yushu, Kong Yuting, Huang Cong. Review of Research on Power Load Forecasting[J]. Sichuan Electric Power Technology, 2023, 46(04): 3-43+58.
[2]. Li Xiaoliang, Li Ziping, Liu Jiazheng. Research on Medium-and Long - Term Load Forecasting in a Certain Area Based on Tim -Series Analysis[J]. Modern Industrial Economy and Informationization, 2024, 14(07): 282-283+287.
[3]. Wang Yanling, Wu Mengkai. Medium - and Long - Term Load Forecasting Model Based on Partial Least Squares Regression under the New Economic Normal[J]. Electric Power Automation Equipment, 2018, 38(03): 13-139.
[4]. Ma Yangyang, Zhang Jiangong, Chen Yu, et al. Analysis of Load Forecasting for Distribution Network Planning Based on Improved Grey Model[J]. Qinghai Electric Power, 2024, 43(02): 48-52+64.
[5]. Jiang Haiyang, Zhou Ruibing, Wang Shuogang, et al. Short - Term Load Forecasting of the Power Grid in Hohhot Area Based on Big Data and BP Neural Network[J]. Power Big Data, 2020, 23(11): 47-54.
Cite this article
Li,W. (2025). Load Forecasting Method Based on QPSO-LSTM. Applied and Computational Engineering,142,18-22.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of MSS 2025 Symposium: Automation and Smart Technologies in Petroleum Engineering
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