## 5 Conclusions  
A power load forecasting framework integrating QGCT and BiLSTM is proposed in this paper. The technical advantages of the method are verified through a systematic empirical study, and the main conclusions are as follows:  
(1) The Granger causality testing mechanism based on quantile regression framework can effectively identify the association between meteorological factors and load. The method  
Figure 4: Distribution of load forecast errors due to changes in climate characteristics at seasonal scales.  
screens out the core set of influencing factors with stable causal relationships as inputs to the prediction model through significance test to improve the accuracy of prediction.  
(2) Improvement effects of feature screening on load forecasting model performance are quantitatively demonstrated by the results of the error assessment system. The comparative study validates the critical role of causal feature selection in load forecasting. The average reduction in the error metrics reached 34.96% and the correction in MBE reached 35.53%.  
(3) The sensitivity characteristics of load forecasting to key meteorological parameters at seasonal scales are revealed, providing a theoretical basis for the establishment of load forecasting models with seasonal dependence.  
This paper provides theoretical support and practical tools for intelligent operation of power systems through mechanism explanation and quantitative analysis. Meanwhile, this study focuses on a single region with limited time series. Future work could expand to multi-year, multi-regional datasets, integrate probabilistic forecasting, and investigate advanced architectures such as transformers and explore multi-regional data synergy and long-term predictive modeling of extreme climate events to address the energy management challenges in application domains such as utility companies and smart grids.  
Conceptualization, X.H., N.X. and Z.L.; methodology, X.H. and N.X.; software, X.H. and N.X.; validation, N.X., X.W. and Z.L.; formal analysis, L.L. and Y.C.; investigation, X.H. and N.X.; resources, L.L. and Z.L.; data curation, X.H., N.X. and X.W.; writing--original draft preparation, X.H. and N.X.; writing--review and editing, N.X. and Z.L.; visualization, Y.C. and X.W.; supervision, Z.L.; project administration, X.H. and Z.L.; funding acquisition, X.H., L.L. and Y.C. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Science and Technology Project of Power Economic Research Institute of State Grid Fujian Electric Power Co., Ltd. (No. B3130N24000D).  
Not applicable.  
The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author.  
We would like to thank the Power Economic Research Institute of State Grid Fujian Electric Power Company and Zhejiang University for providing support.  
Authors [PERSON], [PERSON] and [PERSON] were employed by Power Economic Research Institute of State Grid Fujian Electric Power Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.