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为提高电力系统短期电力负荷预测的准确性，提出一种基于混合蜣螂优化(hybrid dung beetle optimization, HDBO)算法优化长短期记忆(long short-term memory, LSTM)网络的预测模型(HDBO-LSTM)。首先，为克服原始蜣螂优化(dung beetle optimization, DBO)算法全局搜索能力较弱且易陷入局部最优的问题，在原始DBO算法的基础上引入随机对立学习策略、哈里斯鹰优化算法、逐维高斯变异策略和动态处理机制，以此形成HDBO算法，增强算法的搜索能力和收敛速度，并通过10个基准函数的对比实验验证HDBO算法的搜索性能；其次，采用HDBO算法优化LSTM网络的超参数，以减小随机超参数对负荷预测精度的影响。使用电工数学建模竞赛的电力负荷数据集对模型进行评估，结果显示，在数据集内随机选取的七天预测任务中，HDBO-LSTM模型的均方根误差(RMSE)、平均绝对百分比误差(MAPE)、平均绝对误差(MAE)和变异系数(COV)较原始LSTM模型分别降低了22.38%、16.33%、19.16%和22.52%,该模型能够有效提升短期电力负荷预测的准确性。
Abstract：To improve the accuracy of short-term power load forecasting in the power system, a prediction model(HDBO-LSTM) based on hybrid dung beetle optimization(HDBO) algorithm to optimize long short-term memory(LSTM) network is proposed.Firstly, to solve the problem of weak global search ability and susceptibility to local optima in the original dung beetle optimization(DBO) algorithm, the HDBO algorithm is formed by introducing random opposition learning strategy, Harris hawks optimization algorithm, dimension-by-dimension Gaussian variation strategy, and dynamic boundary handling mechanism based on the original DBO algorithm.This enhances the search ability and convergence speed of the algorithm, and the search performance of the HDBO algorithm is verified through comparative experiments with 10 benchmark functions.Secondly, the HDBO algorithm is used to optimize the hyperparameters of the LSTM network, in order to reduce the impact of random hyperparameter selection on the accuracy of load forecasting.Finally, the model is evaluated by using the power load dataset from the electrical mathematics modeling competition.The results show that compared to the original LSTM model, the HDBO-LSTM model reduces root mean square error(RMSE),mean absolute percentage error(MAPE),mean absolute error(MAE),and coefficient of variation(COV) by 22.38%,16.33%,19.16%,and 22.52%,respectively, in the randomly selected seven day prediction task within the dataset.This model can effectively improve the accuracy of short-term power load forecasting.
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基本信息:
DOI：
中图分类号:TP18;TM715
引用信息:
[1]蔡春雷,刘微,任腾腾.基于HDBO-LSTM的短期电力负荷预测方法[J].沈阳理工大学学报,2025,44(05):21-28.
基金信息:
辽宁省教育厅高等学校基本科研项目(JYTMS20230189); 沈阳理工大学引进高层次人才科研支持计划项目(1010147001131)