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

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.