LFAS: An electricity load forecasting framework assisted by cooperative multi-task learning-based spike occurrence prediction

The accurate power load forecasting is beneficial in reasonably arranging the power supply load, and greatly promoting the safety and smooth operation of smart grid. However, on one hand, high volatility exists in electricity load, especially, in which a few spike loads suddenly occurred has significantly affected the accuracy of short term load forecasting. On the other hand, most existing forecasting schemes follows one-size-fits-all paradigm, i.e., train and utilize a unique model to forecast the whole loads, irrespective of the spike or normal loads. Based on the above observation, we propose a novel short-term load forecasting scheme assisted by cooopeative Multi-task learning (MTL)-based spike occurrence prediction, LFAS, which is composed of two stages. At the first stage, a MTL-based spike occurrence prediction is explicitly proposed to forecast whether the future loads would be spike or not. Then, instead of one-size-fits-all scheme, the second stage intentionally selects the suitable data pre-processing technologies and deep neural network (DNN) prediction models for respectively forecasting spike and normal loads. The experimental results on the Europe electricity load dataset from ENTSO-E Transparency Platform demonstrate that our proposed scheme increases the accuracy of spike occurrence prediction, significantly improves both the forecasting accuracy of spike and normal electricity loads, in comparison with the existing one-size-fits-all load forecasting schemes.