Short-term Power Load Forecasting Based on Gate Recurrent Unit Network and Cloud Computing Platform

Abstract

Short-term power load forecasting plays a very important role in the entire smart grid system. The results of short-term power load forecasting have a great impact on the scheduling and production of power systems. Accurate and efficient short-term power load forecasting can help improve the safety and stability of power systems. Therefore, the design of the forecasting algorithm has always been a very core research direction in the field of power systems. Traditional forecasting methods cannot take into account both the time series and non-linear characteristics of the power load data when performing shortterm power load forecasting. To tackle this problem, we propose a short-term power load forecasting method based on Gate Recurrent Unit (GRU) to predict the power load. Moreover, given that the cloud computing platform can provide parallel computing capabilities and large-scale data storage capabilities, we build our model based on cloud computing methods. We conducted extensive experiments and compared our prediction results with traditional methods to demonstrate that our method is much more accurate and efficient.