NFIM: A Neural Frequency Interpolation Model for Grid Load Time Series Forecasting

The grid load forecasting plays a crucial role in the steady operation management of smart grid system. However., the drastic and irregular fluctuations of grid load make the accurate forecasting difficult to achieve, and most of the existing models are unable to learn the complex data patterns of load data. In this work, we propose a novel framework named Neural Frequency Interpolation Model (NFIM) to tackle these challenges. NFIM is composed of 3 stacks, with each stack using a pooling layer and a multilayer perceptron (MLP) to extract feature representations from the input sequence, which are then decoded by different basis decoder modules. Such a structure helps the model learn the deep patterns of grid load data, such as pattern in frequency domain and multi-scale resolution, thereby achieving high-performance forecasting. Extensive experimental results on real-world dataset verifies that our model outperforms state-of-art deep learning models on grid load forecasting tasks.