# GCN-Informer: A Novel Framework for Mid-Term Photovoltaic Power Forecasting
## 5 Conclusions  
The growing demand for precise medium and long-term forecasts of photovoltaic power generation has surged in tandem with the swift advancement of new energy generation technologies. In light of this pressing demand, this study introduces an innovative approach that integrates both GCN and Informer methodologies into the realm of photovoltaic power generation prediction. This pioneering endeavor is poised to meet the increasing need for dependable energy forecasting in the evolving landscape of new energy power generation.  
More precisely, the introduced approach is denoted as the GCN-Informer model. This model has been meticulously crafted to extract invaluable insights between successive time intervals, featuring both an encoder layer and a decoder layer. In the encoder layer, the incorporation of an attention distillation mechanism facilitates the coarse-grained capture of long-range dependencies among inputs, even in scenarios where memory resources are limited. On the other hand, the inclusion of a GCN module within the decoder layer bolsters the precision of feature extraction between adjacent time points. This strategic enhancement effectively addresses the issue of missing features in the original model, further elevating the overall performance and reliability of the GCN-Informer model.  
The experimental results suggest that the GCN-Informer model may offer advantages in terms of accuracy compared to traditional machine learning and deep learning approaches. However, further investigation and validation may be warranted. Through the strategic integration of GCN and Informer methodologies, our proposed model excels in precisely forecasting photovoltaic power generation for medium-term horizons. At the same time, while meeting the real-time requirements of photovoltaic power generation prediction, the extraction of hidden information of different features also ensures the prediction accuracy of the model. This accomplishment not only meets but also exceeds the elevated demands set forth by the burgeoning landscape of new energy power generation. The GCN-Informer model stands as a testament to the power of advanced techniques in shaping the future of energy forecasting, ensuring the reliable and efficient utilization of photovoltaic power in the ever-evolving energy sector.  
In future studies, we aim to develop an enhanced model for interval forecasting, leveraging the strengths of the GCN-Informer architecture. To better align with the realities of photovoltaic power generation in industrial and electrical sectors, we will introduce a tailored loss function. This custom metric will quantify the economic ramifications arising from discrepancies between predicted and actual power output, ultimately leading to more meaningful contributions in the field of photovoltaic forecasting.  
**Author Contributions:** Conceptualization, W.Z. and Z.L.; methodology, Z.L.; validation, Z.L.; formal analysis, Y.W.; investigation, Q.X.; resources, W.Z.; data curation, Y.W.; writing--review and editing, W.Z., Z.L., Y.W. and Q.X.; visualization, Z.L.; supervision, M.X.; project administration, W.Z. and M.X. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Science and Technology Project of SGCC, the Research and application of data-driven intraday forward-looking scheduling technology for key transmission channels, grant number 5108-202318054A-1-1-ZN.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. These data can be found here: [[https://github.com/Seb5](https://github.com/Seb5) Vet/MDPI]([https://github.com/Seb5](https://github.com/Seb5) Vet/MDPI) (accessed on 12 January 2024).  
**Conflicts of Interest:** The authors declare no conflict of interest.