基于改进注意力机制的时间卷积网络-长短期记忆网络短期电力负荷预测

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Abstract To fully explore the effective temporal information contained in power load data and improve the accuracy of short term power load prediction, this article proposes a power load forecasting model based on an improved attention mechanism for the temporal convolutional network (TCN)-long short term memory (LSTM) network. Firstly, the temporal data is input into the TCN model to extract temporal features. Then, the extracted temporal features are combined with non temporal to be input into the LSTM model for training. Finally, Bayesian optimization method is used for hyperparameter optimization to get the best parameters in TCN-LSTM. An attention mechanism improved by multi-layer perceptron (MLP) is introduced to reduce the loss of historical information and strengthen the influence of important information, completing short term load forecasting. By comparing the predictive performance of various deep learning models, it is verified that the model proposed in this article has higher accuracy in short term power load forecasting.

Key words： short term power load forecasting improved attention mechanism Bayesian optimization multi-layer perceptron (MLP) time convolutional network (TCN) long short term memory (LSTM) network

Received: 02 April 2024

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	LIU Wei
	WANG Hongzhi

Cite this article:

LIU Wei,WANG Hongzhi. Short term power load forecasting based on temporal convolutional network-long short term memory and improved attention mechanism[J]. Electrical Engineering, 2024, 25(10): 8-14.

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https://dqjs.cesmedia.cn/EN/Y2024/V25/I10/8

[1] 王琛, 王颖, 郑涛, 等. 基于ResNet-LSTM网络和注意力机制的综合能源系统多元负荷预测[J]. 电工技术学报, 2022, 37(7): 1789-1799.
[2] 陈轩伟. 基于BP-QR模型的负荷区间预测[J]. 电气技术, 2022, 23(4): 14-17, 24.
[3] 徐扬, 张紫涛. 基于遗传模拟退火算法改进BP神经网络的中长期电力负荷预测[J]. 电气技术, 2021, 22(9): 70-76.
[4] 周彬彬, 黄嘉昕, 耿冉冉, 等. 基于灰色-ARIMA耦合模型的中国发电量短期预测[J]. 科技和产业, 2022, 22(12): 382-387.
[5] 欧阳福莲, 王俊, 周杭霞. 基于改进迁移学习和多尺度CNN-BiLSTM-Attention的短期电力负荷预测方法[J]. 电力系统保护与控制, 2023, 51(2): 132-140.
[6] 赵兵, 王增平, 纪维佳, 等. 基于注意力机制的CNN-GRU短期电力负荷预测方法[J]. 电网技术, 2019, 43(12): 4370-4376.
[7] 魏健, 赵红涛, 刘敦楠, 等. 基于注意力机制的CNN-LSTM短期电力负荷预测方法[J]. 华北电力大学学报(自然科学版), 2021, 48(1): 42-47.
[8] 陈振宇, 刘金波, 李晨, 等. 基于LSTM与XGBoost组合模型的超短期电力负荷预测[J]. 电网技术, 2020, 44(2): 614-620.
[9] 陈简, 张惊雷. 基于注意力机制的TCN-BiGRU模型短期风电功率预测[J/OL]. 天津理工大学学报, 1-6 [2024-03-31]. http://kns.cnki.net/kcms/detail/12.1374.N.20240328.1409.057.html.
[10] 缪凯. 基于贝叶斯优化算法的长短期记忆网络短期负荷预测[D]. 北京: 华北电力大学, 2021.
[11] 曹少奇. 基于注意力机制融合LSTM的短期电力负荷预测算法研究[D]. 北京: 华北电力大学, 2022.
[12] 赵洪山, 吴雨晨, 温开云, 等. 基于时空注意力机制的台区多用户短期负荷预测[J]. 电工技术学报, 2024, 39(7): 2104-2115.
[13] 潘锦业, 王苗苗, 阚威, 等. 基于Adam优化算法和长短期记忆神经网络的锂离子电池荷电状态估计方法[J]. 电气技术, 2022, 23(4): 25-30, 36.
[14] 万庆祝, 于佳松, 佟庆彬, 等. 基于Bo-BiLSTM网络的IGBT老化失效预测方法[J]. 电气技术, 2024, 25(3): 1-10.
[15] 王步华, 朱劭璇, 熊浩清, 等. 基于长短期记忆神经网络的检修态电网暂态稳定评估方法[J]. 电气技术, 2023, 24(1): 29-35, 43.
[16] 任建吉, 位慧慧, 邹卓霖, 等. 基于CNN-BiLSTM- Attention的超短期电力负荷预测[J]. 电力系统保护与控制, 2022, 50(8): 108-116.
[17] 程子家. 大规模多层感知器神经网络的研究与应用[D]. 长春: 吉林大学, 2016.
[18] 邹智, 吴铁洲, 张晓星, 等. 基于贝叶斯优化CNN- BiGRU混合神经网络的短期负荷预测[J]. 高电压技术, 2022, 48(10): 3935-3945.
[19] 龚丹丹. 基于VMD-ICOA-BiLSTM混合模型的日前电价预测[J]. 电气技术, 2023, 24(11): 28-34.
[20] 江晶晶, 窦真兰, 杨海涛, 等. 贫乏信息下基于深度迁移学习的智慧建筑负荷预测方法[J]. 电气技术, 2022, 23(5): 55-61, 72.