Informer model with season-aware block for efficient long-term power time series forecasting

With the development of electricity spot markets, accurate electricity load forecasting enables power generation companies to supply the right amount of electricity, greatly avoiding power waste. As a result, time series forecasting in the field of power can bring great benefits. Previously, the Informer model successfully introduced the Transformer into long time series forecasting(LTSF) by proposing the ProbSparse self-attention mechanism, which solved the inherent problem of high memory complexity in self-attention. Recent research has further demonstrated the potential of the self-attention for mining complex dependencies. However, the limited amount of historical data has become one of the main challenges in applying deep learning techniques to power LSTF tasks. Previous researches often add a large number of time covariates to provide more information. In this paper, to address this issue, (i) we design a simple but effective Seasonaware Block to enhance the model's ability to mine artificial prior information in temporal covariates; (ii) we conduct experiments using the provincial power data of Zhejiang Province, China, from 2019 to 2022, and our model outperforms other models, achieving a 19 percent MSE relative improvement, which can help power generation companies reduce costs and further reduce prices to benefit consumers; (iii) we conduct ablation experiments to assess the efficacy of the Season-aware Block in extracting temporal periodic features. Furthermore, we elucidate the underlying reasons for the effectiveness of both the self-attention mechanism and the Seasonaware Block through visualization experiments through visualization techniques.