Distributed Photovoltaic Ultra-short-term Power Forecasting Method Based on Temporal Analog Matching Approach and Transformer Network Modeling

doi:10.11930/j.issn.1004-9649.202403112

Abstract

Abstract:

To address the challenge of low prediction accuracy of distributed photovoltaic (PV) power generation under sudden weather change scenarios due to the lack of meteorological data, this paper proposes a distributed PV ultra-short-term power prediction method based on temporal analog matching approach (TAMA) and Transformer network modeling. Firstly, the concept of similar time periods is extended from days to more flexible time periods, and a matching strategy integrating historical power and satellite remote sensing information is proposed to efficiently identify the most critical time periods of similar power for prediction without relying on meteorological data. Based on this, the powerful temporal modeling capability of the Transformer network is used to dynamically resolve the hidden correlations in multi-source similar time periods, and deeply mine the key features of power, thus providing more accurate ultra-short-term power prediction for distributed PV systems under sudden weather change conditions. Finally, the effectiveness of the proposed method is verified through actual distributed PV power generation data.

Key words: distributed PV power, similar time periods, transformer model, ultra-short-term power forecasting, satellite remote sensing information

Pengwei YANG, Liping ZHAO, Junfa CHEN, Zhao ZHEN, Fei WANG, Liming LI. Distributed Photovoltaic Ultra-short-term Power Forecasting Method Based on Temporal Analog Matching Approach and Transformer Network Modeling[J]. Electric Power, 2024, 57(12): 60-70.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202403112

https://www.electricpower.com.cn/EN/Y2024/V57/I12/60

Figures/Tables 17

Fig.1 Flowchart of proposed methodology

Fig.2 Flowchart of TAMA

Fig.3 Data flow process for a single batch

Fig.4 Transformer based forecasting model structure

Fig.5 Structure of model encoder

Fig.6 Structure of model decoder

Fig.7 K-Means clustering results for power generation in different time intervals

Fig.8 Process of similarity evaluation

Fig.9 Satellite remote sensing data MSSIM during matching time period and Pearson correlation coefficient during predicted time period

Fig.10 Comparison of cloud cover and power generation during the same time intervals

Fig.11 RMSE error under different sliding window length

Table 1 Comparison of RMSE errors of different forecasting models

方法	多数为晴天	多数为云蓬	多数为云密
LSTM	1.77	1.85	1.89
Transformer	1.66	1.67	1.71
TAMA+LSTM	1.64	1.76	1.78
TAMA +Transformer	1.53	1.58	1.68

Table 2 Comparison of MAE errors of different forecasting models

方法	多数为晴天	多数为云蓬	多数为云密
LSTM	1.41	1.48	1.52
Transformer	1.38	1.35	1.48
TAMA+LSTM	1.36	1.41	1.46
TAMA +Transformer	1.26	1.31	1.37

Fig.12 Forecasting results under rapid weather changes (mostly clear skies )

Fig.13 Forecasting results under rapid weather changes (mostly clear skies with few cloud)

Fig.14 Forecasting results under rapid weather changes (mostly clouded weather)

Fig.15 Forecasting accuracy for different methods

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