# Stride-TCN for Energy Consumption Forecasting and Its Optimization
## 5 Conclusions  
This paper presents three contributions. First, we propose a lightweight TCN family with the stride mechanism; secondly, we introduce a new dataset about electrical energy consumption along with its benchmark; and thirdly, we search for a robust model based on Bayesian Optimization. The experiments have shown that our architecture achieves comparable results on small and medium datasets while significantly reducing model complexity compared to baselines. We argue that the performance on the large dataset is not high as expected because we limit the number of dilation layers which makes our model underfit. Importantly, our results provide evidence for the hypothesis that highly correlated time points are crucial for the forecasting task. Furthermore, we suggest that the stride factors should be trained alongside the model's parameters to make it adaptable to various datasets. This assumption might be addressed in future studies.  
Figure 4: Comparison between models with respect to performance and complexity. The comparison is conducted on the CNU dataset. The x-axis depicts MSE error, and the y-axis depicts the forecast horizontal at eight milestones (1, 12, 24, 36, 48, 60, 72, and 84 h). A circle represents a model whose color represents its category, and the circle radius describes its complexity.  
**Author Contributions:** Conceptualization, L.H.A. and J.Y.K.; methodology, L.H.A., G.H.Y. and D.T.V.; software, L.H.A.; validation, J.Y.K., G.H.Y. and J.C.Y.; formal analysis, D.T.V.; investigation, J.L. and J.C.Y.; resources, J.S.K.; data curation, L.H.A. and J.Y.K.; writing--original draft preparation, L.H.A.; writing--review and editing, J.Y.K., J.S.K. and J.C.Y.; visualization, L.H.A.; supervision, J.Y.K. and J.S.K.; project administration, J.C.Y., J.S.K. and J.Y.K.; funding acquisition, J.J.L., J.S.K. and J.Y.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Korea Electric Power Research Institute (KEPRI) grant funded by the Korea Electric Power Corporation (KEPCO) (No. R20 IA02). And this work was supported by the Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2021-0-02068, Artificial Intelligence Innovation Hub).  
Not applicable.  
**Data Availability Statement:** Individual household electric power consumption is available online at [[https://archive.ics.uci.edu/ml/datasets/individual+household+electric+power+consumption](https://archive.ics.uci.edu/ml/datasets/individual+household+electric+power+consumption)]([https://archive.ics.uci.edu/ml/datasets/individual+household+electric+power+consumption](https://archive.ics.uci.edu/ml/datasets/individual+household+electric+power+consumption)) (accessed on 17 July 2022). The energy consumption curves of 499 customers from Spain are available online at [[https://fordatis.fraunhofer.de/handle/fordatis/215](https://fordatis.fraunhofer.de/handle/fordatis/215)]([https://fordatis.fraunhofer.de/handle/fordatis/215](https://fordatis.fraunhofer.de/handle/fordatis/215)) (accessed on 17 July 2022). The CNU energy consumption is available online at [[https://github.com/andrewlee1807/tcns-with-nas/tree/main/Dataset/cnu-dataset](https://github.com/andrewlee1807/tcns-with-nas/tree/main/Dataset/cnu-dataset)]([https://github.com/andrewlee1807/tcns-with-nas/tree/main/Dataset/cnu-dataset](https://github.com/andrewlee1807/tcns-with-nas/tree/main/Dataset/cnu-dataset)) (accessed on 5 August 2022).  
The authors declare no conflict of interest.