Short-term power load forecasting based on Least Squares Support Vector Machine optimized by Bare Bones Fireworks algorithm

摘要

Short-term load forecasting is a fundamental work in power system, which is significant for the control and dispatch of power system. This paper proposes a least squares support vector machine (LSSVM) algorithm optimized by bare bones fireworks algorithm (BBFWA) to enhance the accuracy of short-term power load forecasting. The forecasting model is based on least squares support vector machine. Then, the parameters of LSSVM are optimized by BBFWA. Compared to the other algorithms, the proposed method can forecast the short-term power load effectively and cost less time to optimize the parameters of LSSVM.