| 姜建国,金方承,毕洪波.多策略改进蜣螂优化算法及其在光伏发电功率预测中的应用[J].电力需求侧管理,2024,26(6):101-106 |
| 多策略改进蜣螂优化算法及其在光伏发电功率预测中的应用 |
| Multi-strategy improved dung beetle optimization algorithm and its application in photovoltaic power generation power prediction |
| 投稿时间：2024-08-07 修订日期：2024-09-17 |
| DOI：10. 3969 / j. issn. 1009-1831. 2024. 06. 016 |
| 中文关键词: 光伏发电 功率预测 多策略改进 蜣螂优化算法 长短期记忆网络 |
| 英文关键词: photovoltaic power generation power prediction multi strategy improvement dung beetle optimization algorithm long shortterm memory network |
| 基金项目:黑龙江省自然科学基金（LH2022F005） |
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| 摘要点击次数: 24 |
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| 中文摘要: |
| 为了提高光伏发电功率预测的精确度,利用伯努利映射、鲸鱼优化算法的螺旋更新机制和最优个体自适应t分布3种策略改进标准蜣螂优化算法。通过在8种标准测试函数上进行验证,结果表明改进后的算法在收敛速度和寻优能力方面均有显著提升。进一步地,采用改进蜣螂优化算法优化长短期记忆网络模型（IDBO-LSTM）进行光伏发电功率预测,并与其他6种模型进行对比实验。预测结果表明,相较于其他模型,IDBO-LSTM在3种不同的天气类型下都展现出来更好的预测性能。与DBOLSTM模型相比,IDBO-LSTM的平均绝对误差率分别下降了0.08%、3.51%、4.02%。 |
| 英文摘要: |
| In order to improve the accuracy of photovoltaic power generation power prediction, the standard dung beetle optimization algorithm（DBO）was improved by using three strategies：Bernoulli mapping, the spiral update mechanism of whale optimization algorithm （WOA）and the optimal individual adaptive t-distribution. Through verification on 8 standard test functions, the results show that the improved algorithm has significant improvements in convergence speed and optimization ability. Furthermore, the improved dung beetle optimization algorithm was used to optimize the long short-term memory network model（IDBO-LSTM）for photovoltaic power generation power prediction, and compared with six other models. The prediction results show that IDBO-LSTM exhibits better prediction performance under 3 different weather types than other models. Compared with the DBO-LSTM model, the average absolute error rate（MAPE）of IDBOLSTM decreased by 0.08%, 3.51%, 4.02%, respectively. |
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