基于灰色关联和麻雀搜索算法的电力负荷预测 .

In order to overcome the shortcomings of traditional machine learning algorithms, such as lack of generalization performance and difficulty in determining model parameters and structure, a combined algorithm based on grey correlation and sparrow search algorithm (SSA) was proposed to optimize the parameters of least square support vector machine. The projection principle is used to improve the traditional grey correlation similarity day selection algorithm, and the sparrows search algorithm is used to optimize the parameters of LSSVM. The short-term load forecasting based on the load data of a certain area in south China is compared with Lssvm and particle swarm optimization algorithm (PSO)-LSSVM. The results show that LSSVM optimized by sparrow search algorithm is more accurate, more accurate and more stable than traditional LSSVM and LSSVM optimized by PSO. [ABSTRACT FROM AUTHOR]