In order to improve the forecasting accuracy of the short-term power load,a forecasting method combining multi-algorithm & multi-model and online second learning was newly proposed.First,the input variables were selected by using mutual information and statistical information and a dataset was constructed.Then,multiple training sets were generated by performing diverse sampling with bootstrap on the original training set.Multiple models were obtained using different artificial intelligence and machine-learning algorithms.Finally,the offline second-learning method was improved.A new training set was generated using the actual load,and the multi-model forecasts for recent period within the forecasted time,which is trained by online second learning to obtain the final forecasting results.The load in Guangzhou,China was studied.Compared to the optimal single-model,single-algorithm & multi-model and multi-algorithm & single-model,Mean Absolute Percentage Error (MAPE) of the proposed model was reduced by 21.07％,7.64％ and 5.00％,respectively,in the daily total load forecasting,and by 16.02％,7.60％,and 13.14％,respectively,in the daily peak load forecasting.The experimental results show that the proposed method can improve the prediction accuracy of the power load,reduce costs,implement optimal scheduling management,and ensure security with early warnings in smart grids.%为了提高短期电力负荷预测精度,首次提出多算法多模型与在线第二次学习结合的预测方法.首先,利用互信息方法和统计方法对输入变量进行选择;然后,通过Bootstrap方法对数据集进行多样性采样,利用多个不同的人工智能算法和机器学习算法训练得到多个差异化较大的异构预测模型;最后,用每个待预测时刻最近一段时间的实际负荷值、第一次学习生成的多异构预测模型的负荷预测值构成新训练数据集,对新训练数据集进行在线第二次学习,得到最终预测结果.对中国广州市负荷进行预测研究,与最优单模型、单算法多模型和多算法单模型相比,在每日总负荷预测中,全年平均绝对百分误差(MAPE)分别下降了21.07％、7.64％和5.00％,在每日峰值负荷预测中,全年NAPE分别下降了16.02％、7.60％和13.14％.实验结果表明,推荐方法有效地提高了负荷预测精度,有利于智能电网实现节能降耗、调度精细化管理和电网安全预警.
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