Multistep Prediction Model for Photovoltaic Power Generation Based on Time Convolution and DLinear

[Objective] Photovoltaic power forecasting is a key technology to improve the efficiency of solar energy utilization and reduce operating costs. However， the traditional model has the problem of insufficient time-trend learning ability and error accumulation in multistep photovoltaic power prediction， which limits the improvement of prediction accuracy.［Methods］ This paper presents a multistep prediction model for photovoltaic power generation based on a temporal convolutional network （TCN） and DLinear combined model. First， it improves the complete ensemble empirical mode decomposition with adaptive noise and ICEEMDAN decomposes multivariate meteorological sequences to reveal their potential features and obtain multidimensional subsequences that make it easier to learn multiscale features. Second， the TCN is used to model the local time sequence information and mine short-time sequence features. Finally， DLinear decomposes the sequence into trend and residual components， learns multiscale features through linear networks， and directly outputs multistep （four-step prediction， 15 min per step） photovoltaic power prediction results. ［Results］ Experimental results show that each module of the proposed method can significantly improve the prediction performance of the model. Compared with ICEEMDAN-CNN-BiLSTM， Informer， and Autoformer， the normalized root mean square error （NRMSE） decreased by 22. 455%， 6. 139%， and 8. 504% on average， respectively， with obvious advantages.［Conclusions］ Through the improved ICEEMDAN decomposition and TCN-DLinear combined model， this study effectively solves the shortcomings of traditional methods in multistep prediction and significantly improves the accuracy and reliability of photovoltaic power prediction. The research results provide a new technical path for the accurate prediction of photovoltaic power generation， theoretical support for the efficient management and operation of solar power generation， and data support for the safe and stable operation of new power systems. In the future， the generalizability of this model under different meteorological conditions and geographical environments should be further explored to promote the development of solar power generation technology.