摘要
在智慧城市中,准确的住宅负荷预测是实现电力供需平衡和降低资源浪费的关键.为了提升对城市住宅区负荷预测的精度,构建了一种由长短期记忆网络(LSTM)和集成学习相结合的短期负荷预测模型LSTM-AdaBoost.该模型以露点(空气中的水蒸气凝结成水珠的温度)、历史负荷、周类型等特征作为数据输入;然后将具备时序记忆功能的LSTM网络作为集成学习的基学习器;最后用AdaBoost集成算法对基学习器进行加权组合得到强学习器.实验结果表明,LSTM-AdaBoost集成模型相较于LSTM网络、支持向量机(SVM)和CART决策树等单一预测方法具有更高的预测精度.
In smart cities,accurate residential load forecasting is the key to achieving a balance between power supply and demand and reducing resource waste.In order to improve the accuracy of load forecasting of urban residential districts,a short-term load forecasting model LSTM-AdaBoost,which combines long and short-term memory network(LSTM)and ensemble learning,is proposed.The model uses dew point(the temperature at which water vapor in the air condenses into water droplets),historical load,week type and other characteristics as data input;then the LSTM network with timing memory function is used as the base learner for integrated learning;finally,the ensemble AdaBoost algorithm performs a weighted combination of the base learner to obtain a strong learner.Experimental results show that the integrated LSTM-AdaBoost model has higher forecast accuracy than single forecasting methods such as LSTM network,support vector machine(SVM)and CART decision tree.
作者
李龙祥
彭晨
李军
王雨嫣
鲁荣波
LI Longxiang;PENG Chen;LI Jun;WANG Yuyan;LU Rongbo(College of Information Science and Engineering, Jishou University, Jishou 416000, Hunan China;College of Mathematics and Statistics, Jishou University, Jishou 416000, Hunan China;Huaihua University, Huaihua 418008, Hunan China)
出处
《吉首大学学报（自然科学版）》
CAS
2021年第6期30-35,共6页
Journal of Jishou University(Natural Sciences Edition)
基金
国家自然科学基金青年科学基金资助项目(62006095)
湖南省教育厅优秀青年项目(20B470)
国家级创新创业训练项目(S202010531027)。
