基于萤火虫算法的短期电力负荷预测方法

近年来，电力行业快速发展，对电力负荷进行预测也越来越重要，其中短期负荷预测对于电力系统的调度和市场运行起到极其重要的作用，精准的电力负荷预测可以有效提高发电设备利用度。融合卡帕（Kappa）测度和萤火虫算法的进行选择性集成学习方法实现短期负荷预测，该方法首先使用自展法（bootstrap抽样）生成多个学习器，然后使用Kappa测度对学习器进行初步筛选，接着使用萤火虫算法从中选择部分差异度大、准确率高的学习器参与集成，其准确率相较于单个学习器而言，有着明显提升。选取2015?2016年武汉2家激光企业的日均负荷曲线作为研究对象，进行负荷预测，通过与其他预测方法进行对比，该方法的预测精度较高。

Short-Term Power Load Forecasting Method based on Glowworm Swarm Optimization Algorithm

With the rapid development of the power industry,the power load prediction is becoming more and more important in recent years,and short-term load prediction plays an extremely important role in dispatching and market operation of the power system.Power load prediction can effectively improve the utilization of power generation equipment.The selective ensemble learning method based on Kappa statistic and the glowworm swarm optimization algorithm(GSO)to forecast short-term load is proposed.This proposed method firstly generates multiple learners by bootstrap sampling,and then use glowworm swarm optimization algorithm to select some learners with large differences and high accuracy to participate in the integration.Compared with a single learner,the accuracy of the proposed method is significantly improved.The daily average load curves of two enterprises in Wuhan from 2015 to 2016 are used as a case study to carry out load forecasting.Comparing with other forecasting methods,the prediction accuracy of the proposed method is proved to be higher.