为了提高电力市场环境下短期负荷预测精度,利用互信息法和电价负荷曲线验证电价与负荷的关系,考虑电价对负荷预测的影响,从而提出一种基于Attention-LSTM(attention long short-term memory,Attention-LSTM)网络的短期负荷预测模型。首...为了提高电力市场环境下短期负荷预测精度,利用互信息法和电价负荷曲线验证电价与负荷的关系,考虑电价对负荷预测的影响,从而提出一种基于Attention-LSTM(attention long short-term memory,Attention-LSTM)网络的短期负荷预测模型。首先将考虑电价波动因素的特征向量从输入层放入LSTM模型隐藏层中进行训练,然后将训练后得到的特征向量作为Attention层的输入,生成权重向量,最后将特征向量和权重向量合并得到新的向量,通过全连接层的训练得到预测结果值。运用江苏某地市数据进行实验验证,结果表明所提方法具有更高的负荷预测精度。展开更多
Machine reading comprehension has been a research focus in natural language processing and intelligence engineering.However,there is a lack of models and datasets for the MRC tasks in the anti-terrorism domain.Moreove...Machine reading comprehension has been a research focus in natural language processing and intelligence engineering.However,there is a lack of models and datasets for the MRC tasks in the anti-terrorism domain.Moreover,current research lacks the ability to embed accurate background knowledge and provide precise answers.To address these two problems,this paper first builds a text corpus and testbed that focuses on the anti-terrorism domain in a semi-automatic manner.Then,it proposes a knowledge-based machine reading comprehension model that fuses domain-related triples from a large-scale encyclopedic knowledge base to enhance the semantics of the text.To eliminate knowledge noise that could lead to semantic deviation,this paper uses a mixed mutual ttention mechanism among questions,passages,and knowledge triples to select the most relevant triples before embedding their semantics into the sentences.Experiment results indicate that the proposed approach can achieve a 70.70%EM value and an 87.91%F1 score,with a 4.23%and 3.35%improvement over existing methods,respectively.展开更多
文摘为了提高电力市场环境下短期负荷预测精度,利用互信息法和电价负荷曲线验证电价与负荷的关系,考虑电价对负荷预测的影响,从而提出一种基于Attention-LSTM(attention long short-term memory,Attention-LSTM)网络的短期负荷预测模型。首先将考虑电价波动因素的特征向量从输入层放入LSTM模型隐藏层中进行训练,然后将训练后得到的特征向量作为Attention层的输入,生成权重向量,最后将特征向量和权重向量合并得到新的向量,通过全连接层的训练得到预测结果值。运用江苏某地市数据进行实验验证,结果表明所提方法具有更高的负荷预测精度。
基金National key research and development program(2020AAA0108500)National Natural Science Foundation of China Project(No.U1836118)Key Laboratory of Rich Media Digital Publishing,Content Organization and Knowledge Service(No.:ZD2022-10/05).
文摘Machine reading comprehension has been a research focus in natural language processing and intelligence engineering.However,there is a lack of models and datasets for the MRC tasks in the anti-terrorism domain.Moreover,current research lacks the ability to embed accurate background knowledge and provide precise answers.To address these two problems,this paper first builds a text corpus and testbed that focuses on the anti-terrorism domain in a semi-automatic manner.Then,it proposes a knowledge-based machine reading comprehension model that fuses domain-related triples from a large-scale encyclopedic knowledge base to enhance the semantics of the text.To eliminate knowledge noise that could lead to semantic deviation,this paper uses a mixed mutual ttention mechanism among questions,passages,and knowledge triples to select the most relevant triples before embedding their semantics into the sentences.Experiment results indicate that the proposed approach can achieve a 70.70%EM value and an 87.91%F1 score,with a 4.23%and 3.35%improvement over existing methods,respectively.
