A k-nearest neighbor attentive deep autoregressive network for electricity consumption prediction

Electricity is vital in daily life and crucial for sustainable economic development. Accurate forecasting of energy consumption ensures efficient electricity system operation and supports strategic decision-making for energy distribution. Current time-series methods neglect the influence of neighboring regions’ electricity consumption and the varying impact levels caused by multiple factors on the target area. Therefore, we propose the k-nearest neighbor attentive deep autoregressive network (KNNA-DeepAR) model, which combines a k-nearest neighbor approach with an attentive deep autoregressive network, to achieve precise short-term electricity consumption predictions. By extracting informative features from historical time-series data, we incorporate electricity consumption data from the k regions closest to the target area as additional variables. Leveraging the attention mechanism, we assign varying weights to each variable to capture their interdependencies. Experimental results on a public dataset of electricity loads in fourteen U.S. regions demonstrate the superiority of our model. Compared to state-of-the-art time-series models, our model achieves higher predictive accuracy and exhibits significant potential as an effective approach for accurately predicting electricity consumption and other time-series tasks.