Attention Based Energy Demand Forecasting in Smart Grid Environments

The smart grid is a crucial aspect of the modern energy landscape, providing a reliable, efficient, and sustainable way of meeting the growing energy demands. However, the vast amounts of data generated by smart grid technology necessitate the development of advanced data processing and analysis techniques. In this paper, we propose an attention-based time series workflow that combines dilated convolution and attention mechanisms for time series forecasting in smart grid applications. This workflow extracts temporal features from time series data using dilated convolutions and emphasizes significant temporal points in the hidden states using attention mechanisms. Experimental evaluations showed up to an 8% better performance for energy demand forecasting compared to commonly used deep learning-based methods. Our workflow achieved this gain by requiring 1/3 of the training time other models took. We also improved performance by 42% in various domains, demonstrating the adaptability of our approach across different areas. This study may assist researchers in constructing accurate forecasting models for smart grid environments. Furthermore, it highlights that the attention-based approach can be employed to promote sustainable energy and optimize smart grid environments.