| 冯崇峰,孟春旅,冯积家.基于混合深度学习的电力负荷预测模型[J].电力需求侧管理,2024,26(6):94-100 |
| 基于混合深度学习的电力负荷预测模型 |
| Electrical load forecasting model based on hybrid deep learning |
| 投稿时间：2024-08-16 修订日期：2024-09-23 |
| DOI：10. 3969 / j. issn. 1009-1831. 2024. 06. 015 |
| 中文关键词: 电力数据 特征选择 集成经验模态分解 深度信念网络 长短期记忆网络 |
| 英文关键词: electricity data feature selection integrated empirical mode decomposition deep belief network long short-term memory network |
| 基金项目:中国南方电网有限责任公司创新项目（090000KK52222004） |
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| 摘要点击次数: 25 |
| 全文下载次数: 7 |
| 中文摘要: |
| 针对目前电力数据维度高、特征复杂、干扰严重等挑战,提出了一种混合电力客户用电负荷预测模型。基于集成经验模态分解模型对电力用户用电特征进行分解,从而根据过零率将特征分为高频分量和低频分量。基于多目标进化-深度信念网络对低频分量进行处理,从而准确预测整体变化趋势。基于改进的长短时记忆网络对高频分量进行处理,有效提升了处理复杂非线性局部行为的能力,确保了高频负荷预测的精确性。基于叠加规则对负荷预测进行重构,细化了局部波动的预测,显著提高了模型的整体性能。实验结果表明,与KNN、BPNN、RNN、LSTM等模型相比,所提模型平均绝对百分比误差平均值均降低。该模型具备较优的负荷预测精度,可对配电网安全运行管理及服务质量提升提供一定借鉴。 |
| 英文摘要: |
| A hybrid model for power customer load forecasting has been introduced to tackle the challenges posed by the high dimensionality, complex features, and significant interference present in current power data. Utilizing the integrated empirical mode decomposition model, electricity consumption characteristics of power users are decomposed, separating the features into high-frequency and low-frequency components based on the zero crossing rate. Employing a multi-objective evolution-deep belief network, the low-frequency components are processed to accurately forecast the overall trends. Utilizing an enhanced long short-term memory network, the high-frequency components are processed, significantly improving the capability to handle complex nonlinear local behaviors and ensuring precise high-frequency load forecasting. Utilizing the superposition rule, the load forecasting is reconstructed to refine predictions of local fluctuations, markedly enhancing the model’s overall performance. Experimental results indicate that, compared to models such as KNN, BPNN, RNN, and LSTM, the proposed model achieves an average reduction in the mean absolute percentage error. This model demonstrates superior load forecasting accuracy and can offer insights for enhancing the safe operation and service quality of distribution networks. |
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