An Image Inpainting Approach to Short-Term Load Forecasting

In current power systems, electrical energy is generated whenever there is a demand for it. Therefore, load forecasting, which estimates the active load in advance, is imperative for power system planning and operations. Based on the time horizon, load forecasting is classified as very short-term (below one day), short-term (a day to two weeks), medium-term (two weeks to three years) and long-term (over three years). This paper focuses on the short-term forecasting. The complex multi-level seasonality of load series (e.g., the load in a given hour is not only dependent on load in the previous hour, but also on the previous day.s load in the same hour, and on the previous week's load in the same day-of-the-week and hour) makes this task challenging, especially when the load data is represented in 1d numerical series. However, in multi-channel images, the patterns in spatial neighbourhood of one channel and the patterns in the neighbourhood along the channel dimension are able to be captured by 3d image processing operations. Hence, this study proposes to transform electrical load data from 1d series to 3d images and transform the problem from future series forecasting to missing patch inpainting. Furthermore, it proposes a recurrent neural network to model the temporal trends in the series by convolutional operations on the spatial neighbourhood in the images. Experimental results demonstrate the effectiveness of the proposed method on two benchmarks and show the capability of inferring the future load from related series if there is a lack of history.

Automated summary

In current power systems, load forecasting is crucial for system planning and operations. This paper focuses on short-term load forecasting. The challenge arises from the complex multi-level seasonality of load series and the representation of load data in 1d numerical series. To address this, the study proposes a transformation of load data into 3d images and a recurrent neural network to model temporal trends. Experimental results demonstrate the effectiveness of the proposed method.