# Electric Load Forecasting Based on Deep Ensemble Learning
## 4 Conclusions and Future Work  
In summary, this paper introduces an innovative hybrid framework, namely CNN-BiLSTM-RF and DCC-EL, for anomaly detection in electricity load and short-to-mid-term electricity load forecasting. The proposed framework integrates the advantages of deep learning and ensemble learning. It employs CNN for feature extraction, BiLSTM to capture temporal dependencies, and improved RF for classification during the anomaly detection phase. Subsequently, the DCC-EL model is utilized to forecast future electricity load values, leveraging features extracted by the ensemble learning model. Experimental results indicate that compared to existing models, its RMSE and MSE in short-term and mid-term electricity load forecasting are reduced by 4.96% and 12.31%. This demonstrates that our model can more accurately capture and predict the complex patterns within electricity load data, providing a reliable foundation for energy management and grid planning. However, like any model, ours also has limitations and potential areas for improvement. Future work will focus on applying this framework to more intricate electric ity load-forecasting scenarios, exploring its robustness, and enhancing prediction accuracy by integrating more advanced technologies. We also anticipate developing strategies that allow our model to adapt to data changes over time, further improving its forecasting performance. Constructing the framework: A.W., Z.H. and X.Y. ; correction of errors and writing instructions: Q.Y. and J.W. ; data processing: A.W. and Z.W. ; paper writing: A.W., Z.H. and X.Y. All authors have read and agreed to the published version of the manuscript. Constructing 2022 Ningxia Autonomous Region Key Research and Development Plan (Talent Introduction Special) Project (2022 YCZX0013); Ningxia Key Research and Development Plan (Key Project) (2023 BDE02001); The 2022 University Research Platform \"Digital Agriculture Empowering Ningxia Rural Revitalization Innovation Team\" of North Minzu University (2022 PT_S10); The major key project of school-enterprise joint innovation in Yinchuan 2022 (2022 XQZD009). Not applicable. Constructing 2022  
* [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Power Load Forecasting Based on the Combined Model of LSTM and XGBoost. In Proceedings of the 2019 the International Conference on Pattern Recognition and Artificial Intelligence (PRAI'19). Association for Computing Machinery, Wenzhou, China, 26-28 August 2019; pp. 46-51. * [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON] Conv-AdaRNN: A Power Load Forecasting Method Based on CNN and AdaRNN. In Proceedings of the 2022 5 th International Conference on Hot Information-Centric Networking (HotlCN), Guangzhou, China, 24-26 November 2022; pp. 72-76. * [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON] A novel regression method for harmonic analysis of time series. _ISPRS J. Photogramm. Remote Sens._**2022**, _185_, 48-61. [CrossRef]
* [PERSON] (1995) [PERSON] A study of cross-validation and bootstrap for accuracy estimation and model selection. In _International Joint Conference on Artificial Intelligence_; Morgan Kaufmann Publishers Inc.: Burlington, MA, USA, 1995. * [PERSON] et al. (2012) [PERSON]; [PERSON]; [PERSON]; [PERSON] Load Forecasting Based on LVMD-DBFCM Load Curve Clustering and the CNN-IVIA-BLSTM Model. _Appl. Sci._**2023**, _13_, 7332. [CrossRef]
* [PERSON] et al. (2015) [PERSON]; [PERSON]; [PERSON] Deep learning. _Nature_**2015**, _521_, 436-444. * [PERSON] (2023) [PERSON] A New Global Pooling Method for Deep Neural Networks: Global Average of Top-K Max-Pooling. _Trait. Signal_**2023**, _40_, 577-587. [CrossRef]
* [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON] The performance of LSTM and BiLSTM in forecasting time series. In Proceedings of the 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA, 9-12 December 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 3285-3292. * [PERSON] (2001) [PERSON] Random Forests. _Mach.