Accurate ultra-short-term photovoltaic(PV)power forecasting is crucial for mitigating variations caused by PV power generation and ensuring the stable and efficient operation of power grids.To capture intricate tempor...Accurate ultra-short-term photovoltaic(PV)power forecasting is crucial for mitigating variations caused by PV power generation and ensuring the stable and efficient operation of power grids.To capture intricate temporal relationships and enhance the precision of multi-step time forecast,this paper introduces an innovative approach for ultra-short-term photovoltaic(PV)power prediction,leveraging an enhanced Temporal Convolutional Neural Network(TCN)architecture and feature modeling.First,this study introduces a method employing the Spearman coefficient for meteorological feature filtration.Integrated with three-dimensional PV panel modeling,key factors influencing PV power generation are identified and prioritized.Second,the analysis of the correlation coefficient between astronomical features and PV power prediction demonstrates the theoretical substantiation for the practicality and essentiality of incorporating astronomical features.Third,an enhanced TCN model is introduced,augmenting the original TCN structure with a projection head layer to enhance its capacity for learning and expressing nonlinear features.Meanwhile,a new rolling timing network mechanism is constructed to guarantee the segmentation prediction of future long-time output sequences.Multiple experiments demonstrate the superior performance of the proposed forecasting method compared to existing models.The accuracy of PV power prediction in the next 4 hours,devoid of meteorological conditions,increases by 20.5%.Furthermore,incorporating shortwave radiation for predictions over 4 hours,2 hours,and 1 hour enhances accuracy by 11.1%,9.1%,and 8.8%,respectively.展开更多
基金supported by National Key Research and Development Program of China(Key Techniques of Adaptive Grid Integration and Active Synchronization for Extremely High Penetration Distributed Photovoltaic Power Generation,2022YFB2402900).
文摘Accurate ultra-short-term photovoltaic(PV)power forecasting is crucial for mitigating variations caused by PV power generation and ensuring the stable and efficient operation of power grids.To capture intricate temporal relationships and enhance the precision of multi-step time forecast,this paper introduces an innovative approach for ultra-short-term photovoltaic(PV)power prediction,leveraging an enhanced Temporal Convolutional Neural Network(TCN)architecture and feature modeling.First,this study introduces a method employing the Spearman coefficient for meteorological feature filtration.Integrated with three-dimensional PV panel modeling,key factors influencing PV power generation are identified and prioritized.Second,the analysis of the correlation coefficient between astronomical features and PV power prediction demonstrates the theoretical substantiation for the practicality and essentiality of incorporating astronomical features.Third,an enhanced TCN model is introduced,augmenting the original TCN structure with a projection head layer to enhance its capacity for learning and expressing nonlinear features.Meanwhile,a new rolling timing network mechanism is constructed to guarantee the segmentation prediction of future long-time output sequences.Multiple experiments demonstrate the superior performance of the proposed forecasting method compared to existing models.The accuracy of PV power prediction in the next 4 hours,devoid of meteorological conditions,increases by 20.5%.Furthermore,incorporating shortwave radiation for predictions over 4 hours,2 hours,and 1 hour enhances accuracy by 11.1%,9.1%,and 8.8%,respectively.
