基于改进人工鱼群-蛙跳算法优化LSSVM参数短期负荷预测

短期电力负荷预测在电力系统安全调度、经济运行方面起到关键作用。在用最小二乘支持向量机进行负荷预测时,参数选择将直接影响预测精度。为了提高LSSVM负荷预测精度,文中提出一种基于Levy变异自适应视野人工鱼群-蛙跳算法对LSSVM进行参数优化的方法。以某县负荷、天气等历史数据对LSSVM进行训练,建立LAVAFSA-SFLA-LSSVM、AFSA-LSSVM、LAFSA-SFLA-LSSVM共3种预测模型,对该地区某日24 h的电力负荷进行预测。算例结果表明,LAVAFSA-SFLA-LSSVM预测精度比AFSA-LSSVM和LAFSA-SFLA-LSSVM更高,预测误差更小。

Parameters Selection for LSSVM Based on Artificial Fish Swarm-Shuffled Frog Jump Algorithms Optimization in Short-Term Load Forecasting

Short-term load forecasting plays a key role in safe dispatching and economic operation of power system.The parameters of the LSSVM directly affect the prediction effect during the load forecasting accuracy. In order to improve LSSVM load prediction accuracy, a method based on levy adaptive vision artificial fish swarm-shuffled frog leaping algorithm for parameter optimization of LSSVM is proposed. LSSVM is trained by historical data such as load and weather in a certain area. The LAFSA-SFLA-LSSVM forecasting model, the LAVAFSA-SFLA-LSSVM forecasting model and the AFSA-LSSVM forecasting model are established for power load forecasting in a certain area within 24 hours of a certain day. The results show that the accuracy of the LAVAFSA-SFLA-LSSVM forecasting model is higher than the AFSA-LSSVM forecasting model and the LAFSA-SFLA-LSSVM forecasting model, and the prediction error is smaller.