Short-term load forecasting model of GRU based on improved sparrow search algorithm optimization

Abstract

Accurate prediction of short-term power load can ensure the safety of power system and reduce the cost of power generation. Aiming at the problems of large fluctuation, strong randomness and difficulty in peak value forecasting, a short-term load forecasting method of gated recurrent unit (GRU) based on improved sparrow search algorithm optimization is proposed. Firstly, In the process of load forecasting, the influence of temperature, humidity, electricity price and other factors on load forecasting is considered, and they are taken as input variables of the forecasting model. Secondly, the improved Levy Sparrow Search Algorithm (LSSA) is used to optimize the parameters of GRU network and fully mine the characteristic information of load data, thus improving the forecasting performance of the model. Finally, the load power of a place in Australia is analyzed as an example, and compared with similar forecasting algorithms, the results show that the forecasting effect of this method is better than other methods, and the forecasting accuracy of shortterm load is effectively improved.