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基于切换模型极限学习机的超短期负荷预测
引用本文:邓明丽,张晶.基于切换模型极限学习机的超短期负荷预测[J].电测与仪表,2015,52(13).
作者姓名:邓明丽  张晶
作者单位:国网四川省电力公司技能培训中心,成都,610065
摘    要:针对极限学习机算法中输出波动大与模型不稳定的问题,提出采用切换模型极限学习算法进行超短期电力负荷预测的方法。该算法通过切换模型准则,将建立的多个神经网络模型分为误差较小的保持模型和误差较大的更新模型两部分。保持模型无需进行在线更新,减低了模型输出的波动性;更新模型则需采取随机方法进行在线更新,使得训练误差达到最小,提高模型的泛化能力。通过对某地区电力负荷的预测仿真,结果表明了所提方法提高了预测速度,节省了计算时间,具有更佳的泛化能力和预测精度。

关 键 词:极限学习机  切换模型  负荷预测  更新模型  预测精度
收稿时间:2014/4/24 0:00:00
修稿时间:2014/4/24 0:00:00

Ultra-short term load forecasting based on switching model extreme learning machine
DENG Ming-li and ZHANG Jing.Ultra-short term load forecasting based on switching model extreme learning machine[J].Electrical Measurement & Instrumentation,2015,52(13).
Authors:DENG Ming-li and ZHANG Jing
Affiliation:State Grid Electric Power#$NBSCompany#$NBSSichuan#$NBSProvince skills#$NBStraining center,State Grid Electric Power#$NBSCompany#$NBSSichuan#$NBSProvince skills#$NBStraining center
Abstract:In this paper, large fluctuations in output instability problems with the model for extreme learning machine algorithm, the proposed learning algorithm using the limit switch model for ultra short term load forecasting method. Multiple neural network model of the algorithm by switching the model guidelines to be established to maintain the model into smaller error and error larger update the model in two parts. Keep the model without the need for online updates, thereby reducing volatility model output; update the model you need to take a random method for online updates, which makes the training error to a minimum, improve the generalization ability of the model. Finally, the power load forecast for an area of simulation, forecasting results show that the proposed method improves the prediction speed, save computing time, with better generalization and prediction accuracy.
Keywords:extreme learning machine  switching model  load forecasting  update the model  prediction accuracy
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