基于周期信息增强的Informer光伏发电功率预测

doi:10.11930/j.issn.1004-9649.202306038

摘要/Abstract

摘要：

针对光伏发电功率预测方法难以捕捉层次时序信息导致预测精度提升受限的问题，提出基于周期信息增强的Informer光伏发电功率预测方法。首先，提取光伏发电功率序列标量投影、局部时间戳和全局时间戳建立周期信息增强的预测模型嵌入层；然后，通过Informer模型概率稀疏自注意力主动筛选光伏发电功率与特征变量间重要联系，采用卷积层和池化层对模型变量维度和网络参数进行自注意力蒸馏；最后通过解码层生成式机制进一步预测单序列和长序列发电功率。通过仿真验证，所提模型预测精度更高，能够对光伏发电功率进行长时间预测。

关键词: 光伏发电功率预测, 周期信息增强, 自注意力, 长序列预测

Abstract:

Aiming at the problem that the photovoltaic power generation power prediction method is difficult to capture hierarchical time series information, which results in the limited improvement of prediction accuracy. The paper proposes a photovoltaic power generation prediction method based on the fusion of hierarchical time series information and Informer model. First, photovoltaic power sequence scalar projection, local time stamp and global time stamp are extracted to establish a prediction model embedding layer of periodic information enhancement. Then, through the probability sparse self-attention of the Informer coding layer, the important connection between the photovoltaic power generation and the characteristic variables is actively screened. The convolutional layer and pooling layer are used to optimize the model variable dimensions and network parameters for self-attention distillation. Finally, one-step prediction of single-sequence and long-sequence power generation is realized through the generative mechanism of the decoding layer. Through simulation verification, the proposed model has higher prediction accuracy and can make long-term predictions of photovoltaic power generation.

Key words: photovoltaic power generation forecasting, cycle information enhancement, self-attention, long sequence time-series forecasting

张倩, 蒙飞, 李涛, 杨勇, 白鹭. 基于周期信息增强的Informer光伏发电功率预测[J]. 中国电力, 2023, 56(10): 186-193.

Qian ZHANG, Fei MENG, Tao LI, Yong YANG, Lu BAI. Informer Photovoltaic Power Generation Forecasting Based on Cycle Information Enhancement[J]. Electric Power, 2023, 56(10): 186-193.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202306038

https://www.electricpower.com.cn/CN/Y2023/V56/I10/186

图/表 10

图 1 Informer网络模型结构

Fig.1 Informer network model structure

图 2 Informer光伏发电功率预测模型训练过程

Fig.2 Training process of informer photovoltaic power generation forecasting model

表 1 各变量数据的波动区间

Table 1 The fluctuation range of each variable data

变量		变化区间
时		[1, 24]
月		[1, 12]
周		[1, 7]
日		[1, 31]
全射辐照度/(W·m^–2)		[1, 1206]
直射辐照度/(W·m^–2)		[37, 938]
散射辐照度/(W·m^–2)		[1, 1013]
光伏组件温度/℃		[6, 34.4]
组件环境温度/℃		[4.6, 37.7]
组件环境湿度/(g·m^–3)		[14.9, 97.7]
光伏发电功率/MW		[0.07, 108.91]

图 3 光伏发电功率预测效果

Fig.3 Forecast effect of photovoltaic power generation

图 4 模型预测效果对比

Fig.4 Comparison of models forecasting effect

表 2 不同模型预测误差对比

Table 2 Comparison of forecast errors of different models

预测方法	e_MAPE/%	e_RMSE/MW
RNN	38.51	8.03
LSTM	15.93	6.25
Transformer	7.28	5.19
Informer	5.13	2.75

表 3 模型长序列预测效果

Table 3 Long sequence time-series forecasting effect of model 单位：MW

Informer预测模型	5月2日	5月3日	5月4日
1	3.16	4.62	5.29
2	—	3.29	4.84
3	—	—	3.52

图 5 当前工况预测未来3天效果

Fig.5 Forecast effect in the next 3 days under current conditions

图 6 当前工况预测未来2天效果

Fig.6 Forecast effect in the next 2 days under current conditions

图 7 当前工况预测未来1天效果

Fig.7 Forecast effect in the next 1 day under current conditions

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