Day-ahead Photovoltaic Power Forecasting Model Based on Spatio-temporal Local Attention Network and LSTM

SU Lianchao; FAN Jingyu; ZHI Detian; WU Kai

doi:10.13296/j.1001-1609.hva.2026.08.022

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Day-ahead Photovoltaic Power Forecasting Model Based on Spatio-temporal Local Attention Network and LSTM

Research & Analysis | 更新时间：2026-08-12

- Day-ahead Photovoltaic Power Forecasting Model Based on Spatio-temporal Local Attention Network and LSTM
- High Voltage Apparatus Vol. 62, Issue 8, Pages: 180-190(2026)
- 作者机构：
  
  国网潍坊供电公司，山东潍坊 261000
  国网山东省电力公司，济南 250013
  天津国投津能发电有限公司，天津 300450
  华北电力大学新能源电力系统国家重点实验室，北京 102206
- 作者简介：
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2026.08.022
  CLC：
- Received：22 February 2026，
  
  Revised：2026-04-26，
  
  Published：16 August 2026
- 稿件说明：
移动端阅览
SU Lianchao, FAN Jingyu, ZHI Detian, et al. Day-ahead Photovoltaic Power Forecasting Model Based on Spatio-temporal Local Attention Network and LSTM[J]. High Voltage Apparatus, 2026, 62(8): 180-190.
DOI：

SU Lianchao, FAN Jingyu, ZHI Detian, et al. Day-ahead Photovoltaic Power Forecasting Model Based on Spatio-temporal Local Attention Network and LSTM[J]. High Voltage Apparatus, 2026, 62(8): 180-190. DOI： 10.13296/j.1001-1609.hva.2026.08.022.

摘要

光伏功率随时间的波动具有不确定性和随机性，准确可靠的光伏功率预测对电网的实时调度和频率调节具有重要意义。为了提高光伏功率预测的准确性，提出一种结合时空局部注意力网络（spatio-temporal local attention network，STLAN）和长短时记忆网络（long short term memory，LSTM）的日前光伏发电功率预测模型（STLAN-LSTM）。所提STLAN-LSTM由双自注意力机制组成，通过时序自注意力机制（temporal selfattention，TSA）动态定位相似的历史光伏功率序列，同时采用空间自注意力机制（spatial self-attention，SSA）自动捕获相邻站点匹配的光伏功率序列。然后，通过改进的相似序列分数估计策略统计两个注意力通道的输出，使得模型侧重于日常特征中相似局部序列。最后，使用极限学习机（extreme learning machine，ELM）将注意力通道的输出转换为非线性空间，并输入至LSTM网络实现功率预测。在光伏电站测量数据上的实验结果表明，STLAN-LSTM的预测指标MAE、RMSE、MAPE值分别为1.26%、1.61%、23.21%，具有较高的预测准确性，为日前光伏发电功率预测提供有效的技术支持。

Abstract

The fluctuation of photovoltaic(PV)power over time is characterized by uncertainty and randomness. Accurate and reliable PV power forecasting is of great significance for real-time grid dispatch and frequency regulation. For improving the accuracy of PV power forecasting

a day-ahead PV power forecasting model STLAN-LSTM) based on Spatiotemporal Local Attention Network and Long Short-Term Memory(STLAN-LSTM)is proposed. The proposedSTLAN-LSTMis composed of a dual self-attention mechanism. First

the similar historical PV power sequences is dynamically located through the Temporal Self-Attention(TSA)and

at the same time

the spatial self-attention(SSA)mechanism is used to automatically capture matching photovolaic power sequence from adjacent stations. Then

an improved similarity sequenc score estimation strategy is used to aggregate the output of the two attention channels

enabling the model to fous on similar local sequence within the daily features. Finally

the outputs of the attention channels are transformed into a nonlinear space using Extreme Learning Machine(ELM)

which are then fed into an LSTM network to achieve power forecasting . Experimental results on measured data from PV power plants show that the the forecasting indicatorsMAE

RMSE

and MAPE of STLAN-SLTM are 1.26%

1.61%

and 23.21% respectively

indicating high forecasting accuracy and providing effective technical support and day-ahead PV power forecasting.

关键词

Keywords

references

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