| 王云佳,马国真,夏静,彭毓图,邵华.基于BWO优化LSTM参数和超参数的短期电力负荷预测[J].电力需求侧管理,2026,28(1):93-99 |
| 基于BWO优化LSTM参数和超参数的短期电力负荷预测 |
| Short-term power load forecasting based on BWO optimization of LSTM parameters and hyperparameters |
| 投稿时间：2025-08-29 修订日期：2025-10-28 |
| DOI：10.3969/j.issn.1009-1831.2026.01.013 |
| 中文关键词: 短期电力负荷预测 白鲸优化算法 长短期记忆网络模型 平均绝对误差百分比 均方根误差 |
| 英文关键词: short-term power load forecasting beluga whale optimization long short-term memory mean absolute percentage error root mean square error |
| 基金项目:国网河北省电力有限公司科技项目(HZHTKJXM2023-03)；国家自然科学基金地区科学项目基金(72164026) |
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| 中文摘要: |
| 针对智能电网对电力负荷预测精度的高要求,同时考虑到传统的被优化的长短期记忆网络模型(long short-term memory, LSTM)在短期电力负荷预测结果不稳定的问题,提出一种基于白鲸优化算法(beluga whale optimization,BWO)和LSTM的短期电力负荷预测模型。该模型首先将LSTM的超参数作为白鲸的位置,以历史负荷、日类型、天气因素作为数据集,以LSTM在训练集上的预测误差作为BWO的适应度。然后,利用BWO对最适合训练集的LSTM模型的参数和超参数进行寻优,并根据最优的LSTM模型对某地区的电力负荷数据进行预测分析。研究结果表明,BWO-LSTM预测结果的平均绝对误差百分比和均方根误差更小,预测精度更高,且预测结果更稳定,可作为短期电力负荷预测的可靠工具,可以为电力系统安全稳定运行提供有力支撑。 |
| 英文摘要: |
| A short-term power load forecasting model based on the beluga whale optimization (BWO) algorithm and long short-term memory network (LSTM) is proposed to address the high demand for accuracy in power load forecasting in smart grids, while also considering the instability of traditional optimized LSTM models in short-term power load forecasting. The model first uses the hyperparameters of LSTM as the location of the beluga whale, historical load, day type, and weather factors as the dataset, and the prediction error of LSTM on the training set as the fitness of BWO. Then, BWO is used to optimize the parameters and hyperparameters of the LSTM model that is most suitable for the training set, and predict and analyze the power load data of a certain region based on the optimal LSTM model. The research results indicate that the average absolute error percentage and root mean square error of BWO-LSTM prediction results are smaller, the prediction accuracy is higher, and the prediction results are more stable. It can be used as a reliable tool for short-term power load forecasting and provide strong support for the safe and stable operation of the power system. |
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