首页> 外文会议>International Conference on Intelligent Green Building and Smart Grid >Short-term micro-grid load forecast method based on EMD-KELM-EKF
【24h】

Short-term micro-grid load forecast method based on EMD-KELM-EKF

机译:基于EMD-KELM-EKF的微网短期负荷预测方法

获取原文

摘要

Short-term load forecasting is an important part of micro-grid economic dispatch, and the forecasting error would directly affect the economical efficiency of operation. With respect to large power grid environment, micro-grid is more difficult to realize the short-term load forecasting on the user side. This paper proposes a combined short-term load forecasting model based on Empirical Mode Decomposition (EMD), Extended Kalman Filter (EKF) and Extreme Learning Machine with Kernel (KELM). The time-series data of micro-grid load with high randomness is gradually decomposed into a number of Intrinsic Mode Function (IMF) components through EMD. Two typical different prediction models — EKF and KELM — are adopted to predict different kinds of IMF components. The model prediction accuracy, the stability of period updating and the calculation efficiency is verified through examples analysis of micro-grid of the user side with different types and capacity.
机译:短期负荷预测是微电网经济调度的重要组成部分,其预测误差将直接影响运营的经济效益。对于大型电网环境,微电网更难以在用户端实现短期负荷预测。本文提出了一种基于经验模式分解(EMD),扩展卡尔曼滤波器(EKF)和带有核的极限学习机(KELM)的组合短期负荷预测模型。具有高随机性的微电网负荷的时间序列数据通过EMD逐渐分解为许多固有模式函数(IMF)分量。采用两种典型的不同预测模型EKF和KELM来预测不同种类的IMF组件。通过不同类型和容量的用户侧微电网实例分析,验证了模型的预测精度,周期更新的稳定性和计算效率。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号