| 黄学勤,杨鹏举,赵耀.基于强化人工蜂鸟算法的MTL-ATT-NHITS短期风电功率预测[J].电力需求侧管理,2026,28(2):29-36 |
| 基于强化人工蜂鸟算法的MTL-ATT-NHITS短期风电功率预测 |
| MTL-ATT-NHITS short-term wind power prediction based on enhanced artificial hummingbird algorithm |
| 投稿时间：2025-01-22 修订日期：2025-12-18 |
| DOI：10.3969/j.issn.1009-1831.2026.02.005 |
| 中文关键词: 风电功率预测 人工蜂鸟算法 注意力机制 多任务学习 |
| 英文关键词: wind power prediction artificial hummingbird algorithm attention mechanism multi-task learning |
| 基金项目:国家自然科学基金资助项目(52377111)；西藏自治区科技项目(XZ202401ZY0037) |
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| 中文摘要: |
| 随着智能微电网中分布式新能源的渗透率提升,需求侧资源调度管理对风电功率预测的精度提出了更高要求。为了应对这一挑战,提出了一种基于强化人工蜂鸟算法(enhanced artificial hummingbird algorithm, EAHA)优化的多任务学习(multi-task learning, MTL)-注意力机制(attention mechanism, ATT)-时间序列神经层次插值(neural hierarchical interpolation for time series, NHITS)短期风电功率预测模型。首先,构建了一个MTL框架下的NHITS预测模型,该模型同时考虑风速预测和风电功率预测两个相关任务,通过共享部分参数提高模型的泛化能力,引入ATT动态分配每个栈的输出权重,从而更有效地捕捉不同时间尺度上的关键特征。其次,为进一步优化预测模型的超参数,对传统AHA进行了优化,采用混沌序列初始化种群以丰富种群多样性,并引入交叉学习策略以优化个体间的信息交互,从而提高算法的全局搜索能力和收敛精度。最后,基于山西省某风电场的实际数据进行了算例分析,验证了所提方法的有效性。 |
| 英文摘要: |
| With the increasing penetration of distributed renewable energy in smart microgrids, the management of demand-side resource scheduling has imposed higher requirements on the accuracy of wind power prediction. To address this challenge, a short-term wind power prediction model based on enhanced artificial hummingbird algorithm (EAHA)-optimized multi-task learning (MTL)-attention mechanism (ATT)-neural hierarchical interpolation for time series (NHITS) is proposed. Firstly, an NHITS prediction model under the MTL framework is constructed, which simultaneously considers two related tasks: wind speed prediction and wind power prediction. By sharing partial parameters, the generalization ability of the model is enhanced. The introduction of the ATT mechanism dynamically allocates the output weights of each stack, thereby more effectively capturing key features at different time scales. Secondly, to further optimize the hyperparameters of the prediction model, the traditional AHA is enhanced. Chaotic sequences are utilized to initialize the population, enriching its diversity, and a crossover learning strategy is introduced to optimize information exchange among individuals, thereby improving the global search capability and convergence accuracy of the algorithm. Finally, the effectiveness of the proposed method is validated through case analysis based on actual data from a wind farm in Shanxi Province. |
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