检索结果-南通市图书馆

Deep Learning Based Energy Consumption Prediction on Internet of Things Environment

Intelligent Automation & Soft Computing 2023年第7期37卷 727-743页

作者： S.Balaji S.Karthik Department of ECE SRM Institute of Science and TechnologyVadapalani CampusVadapalani600026TamilnaduIndia

The creation of national energy strategy cannot proceed without accurate projections of future electricity consumption;this is because EC is intimately tied to other forms of energy,such as oil and natural *** the purpose of determining and bettering overall energy consumption,there is an urgent requirement for accurate monitoring and calculation of EC at the building level using cutting-edge technology such as data analytics and the internet of things(IoT).Soft computing is a subset of AI that tries to design procedures that are more accurate and reliable,and it has proven to be an effective tool for solving a number of issues that are associated with the use of *** use of soft computing for energy prediction is an essential part of the solution to these kinds of *** study presents an improved version of the Harris Hawks Optimization model by combining it with the IHHODL-ECP algorithm for use in Internet of Things *** IHHODL-ECP model that has been supplied acts as a useful instrument for the prediction of integrated energy *** order for the raw electrical data to be compatible with the subsequent processing in the IHHODL-ECP model,it is necessary to perform a preprocessing *** technique of prediction uses a combination of three different kinds of deep learning models,namely DNN,GRU,and *** addition to this,the IHHO algorithm is used as a technique for making adjustments to the *** experimental result analysis of the IHHODL-ECP model is carried out under a variety of different aspects,and the comparison inquiry highlighted the advantages of the IHHODL-ECP model over other present *** to the findings of the experiments conducted with an hourly time resolution,the IHHODL-ECP model obtained a MAPE value of 33.85,which was lower than those produced by the LR,LSTM,and CNN-LSTM models,which had MAPE values of 83.22,44.57,and 34.62 *** findings provided evidence of the IHHOD

关键词： Energy consumption forecasting models deep learning fusion models IoT environment gated recurrent unit artificial intelligence

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Deep-Ensemble Learning Method for Solar Resource Assessment of Complex Terrain Landscapes

引用

Energy Engineering 2024年第5期121卷 1329-1346页

作者： Lifeng Li Zaimin Yang Xiongping Yang Jiaming Li Qianyufan Zhou Ping Yang Energy Development Research Institute China Southern Power GridGuangzhou510000China Corporate Headquarters China Southern Power GridGuangzhou510000China Guangdong Green Energy Key Laboratory South China University of TechnologyGuangzhou510000China

As the global demand for renewable energy grows,solar energy is gaining attention as a clean,sustainable energy *** assessment of solar energy resources is crucial for the siting and design of photovoltaic power *** study proposes an integrated deep learning-based photovoltaic resource assessment *** learning and deep learning methods are fused for photovoltaic resource assessment for the first *** proposed method combines the random forest,gated recurrent unit,and long short-term memory to effectively improve the accuracy and reliability of photovoltaic resource *** proposed method has strong adaptability and high accuracy even in the photovoltaic resource assessment of complex terrain and *** experimental results show that the proposed method outperforms the comparison algorithm in all evaluation indexes,indicating that the proposed method has higher accuracy and reliability in photovoltaic resource assessment with improved generalization performance traditional single algorithm.

关键词： Photovoltaic resource assessment deep learning ensemble learning random forest gated recurrent unit long short-term memory

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Artificial Intelligence in Internet of Things System for Predicting Water Quality in Aquaculture Fishponds

引用

Computer Systems Science & Engineering 2023年第9期46卷 2861-2880页

作者： Po-Yuan Yang Yu-Cheng Liao Fu-I Chou Department of Intelligent Robotics National Pingtung UniversityPingtung900TaiwanChina Department of Electrical Engineering National Kaohsiung University of Science and TechnologyKaohsiung807TaiwanChina

Aquaculture has long been a critical economic sector in *** a key factor in aquaculture production efficiency is water quality,an effective means of monitoring the dissolved oxygen content(DOC)of aquaculture water is *** study developed an internet of things system for monitoring DOC by collecting essential data related to water *** intelligence technology was used to construct a water quality prediction model for use in a complete system for managing water *** aquaculture water quality depends on a continuous interaction among multiple factors,and the current state is correlated with the previous state,a model with time series is ***,this study used recurrent neural networks(RNNs)with sequential *** used RNNs such as long short-term memory model and gated recurrent unit(GRU)model have a memory function that appropriately retains previous results for use in processing current *** construct a suitable RNN model,this study used Taguchi method to optimize hyperparameters(including hidden layer neuron count,iteration count,batch size,learning rate,and dropout ratio).Additionally,optimization performance was also compared between 5-layer and 7-layer network *** experimental results revealed that the 7-layer GRU was more suitable for the application considered in this *** values obtained in tests of prediction performance were mean absolute percentage error of 3.7134%,root mean square error of 0.0638,and R-value of ***,thewater qualitymanagement system developed in this study can quickly provide practitioners with highly accurate data,which is essential for a timely response to water quality *** study was performed in collaboration with the Taiwan Industrial Technology Research Institute and a local fishery *** application of the system by the fishery company confirmed that the monitoring system is effective in improving the surviv

关键词： Fishery gated recurrent unit hyperparameter optimization long short-term memory Taguchi method water quality prediction

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Deep LearningModel for Big Data Classification in Apache Spark Environment

引用

Intelligent Automation & Soft Computing 2023年第9期37卷 2537-2547页

作者： T.M.Nithya R.Umanesan T.Kalavathidevi C.Selvarathi A.Kavitha Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTrichyTamilnadu620009India Department of Information and Communication Engineering Anna UniversityChennaiTamilnadu600025India Department of Electronics and Instrumentation Engineering Kongu Engineering CollegeErodeTamilnadu638060India Department of Computer Science and Engineering M.Kumarasamy College of EngineeringKarurTamilnadu639113India Department of Electronics and Communication Engineering K.Ramakrishnan College of EngineeringTrichyTamilnadu620009India

Big data analytics is a popular research topic due to its applicability in various real time *** recent advent of machine learning and deep learning models can be applied to analyze big data with better *** big data involves numerous features and necessitates high computational time,feature selection methodologies using metaheuristic optimization algorithms can be adopted to choose optimum set of features and thereby improves the overall classiﬁcation *** study proposes a new sigmoid butterﬂy optimization method with an optimum gated recurrent unit(SBOA-OGRU)model for big data classiﬁcation in Apache *** SBOA-OGRU technique involves the design of SBOA based feature selection technique to choose an optimum subset of *** addition,OGRU based classiﬁcation model is employed to classify the big data into appropriate ***,the hyperparameter tuning of the GRU model takes place using Adam ***,the Apache Spark platform is applied for processing big data in an effective *** order to ensure the betterment of the SBOA-OGRU technique,a wide range of experiments were performed and the experimental results highlighted the supremacy of the SBOA-OGRU technique.

关键词： Big data apache spark classiﬁcation feature selection gated recurrent unit adam optimizer

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

EmotionIC: emotional inertia and contagion-driven dependency modeling for emotion recognition in conversation

引用

Science China(Information Sciences) 2024年第8期67卷 130-146页

作者： Yingjian LIU Jiang LI Xiaoping WANG Zhigang ZENG School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Key Laboratory of Brain-inspired Intelligent Systems Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control (Huazhong University of Science and Technology) Ministry of Education Institute of Artificial Intelligence Huazhong University of Science and Technology

Emotion recognition in conversation(ERC) has attracted growing attention in recent years as a result of the advancement and implementation of human-computer interface technologies. In this paper,we propose an emotional inertia and contagion-driven dependency modeling approach(EmotionIC) for ERC tasks. Our EmotionIC consists of three main components, i.e., identity masked multi-head attention(IMMHA), dialogue-based gated recurrent unit(DiaGRU), and skip-chain conditional random field(SkipCRF).Compared to previous ERC models, EmotionIC can model a conversation more thoroughly at both the feature-extraction and classification levels. The proposed model attempts to integrate the advantages of attention-and recurrence-based methods at the feature-extraction level. Specifically, IMMHA is applied to capture identity-based global contextual dependencies, while Dia GRU is utilized to extract speaker-and temporal-aware local contextual information. At the classification level, SkipCRF can explicitly mine complex emotional flows from higher-order neighboring utterances in the conversation. Experimental results show that our method can significantly outperform the state-of-the-art models on four benchmark *** ablation studies confirm that our modules can effectively model emotional inertia and contagion.

关键词： emotion recognition in conversation emotional inertia and contagion multi-head attention gated recurrent unit conditional random field

来源：

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Price prediction of power transformer materials based on CEEMD and GRU

引用

Global Energy Interconnection 2024年第2期7卷 217-227页

作者： Yan Huang Yufeng Hu Liangzheng Wu Shangyong Wen Zhengdong Wan Energy Development Research Institute China Southern Power GridGuangzhou 510623P.R.China China Southern Power Grid Guangzhou 510623P.R.China

The rapid growth of the Chinese economy has fueled the expansion of power *** transformers are key equipment in power grid projects,and their price changes have a significant impact on cost ***,the prices of power transformer materials manifest as nonsmooth and nonlinear ***,estimating the acquisition costs of power grid projects is difficult,hindering the normal operation of power engineering *** more accurately predict the price of power transformer materials,this study proposes a method based on complementary ensemble empirical mode decomposition(CEEMD)and gated recurrent unit(GRU)***,the CEEMD decomposed the price series into multiple intrinsic mode functions(IMFs).Multiple IMFs were clustered to obtain several aggregated sequences based on the sample entropy of each ***,an empirical wavelet transform(EWT)was applied to the aggregation sequence with a large sample entropy,and the multiple subsequences obtained from the decomposition were predicted by the GRU *** GRU model was used to directly predict the aggregation sequences with a small sample *** this study,we used authentic historical pricing data for power transformer materials to validate the proposed *** empirical findings demonstrated the efficacy of our method across both datasets,with mean absolute percentage errors(MAPEs)of less than 1%and 3%.This approach holds a significant reference value for future research in the field of power transformer material price prediction.

关键词： Power transformer material Price prediction Complementary ensemble empirical mode decomposition gated recurrent unit Empirical wavelet transform

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

SGT:Session-based Recommendation with GRU and Transformer

引用

Journal of Computer Science Research 2023年第2期5卷 37-51页

作者： Lingmei Wu Liqiang Zhang Xing Zhang Linli Jiang Chunmei Wu School of Mathematics&Computer Science Guangxi Science&Technology Normal UniversityLaibinGuangxi546199China

Session-based recommendation aims to predict user preferences based on anonymous behavior *** research on session-based recommendation systems has mainly focused on utilizing attention mechanisms on sequential patterns,which has achieved significant ***,most existing studies only consider individual items in a session and do not extract information from continuous items,which can easily lead to the loss of information on item transition ***,this paper proposes a session-based recommendation algorithm(SGT)based on gated recurrent unit(GRU)and Transformer,which captures user interests by learning continuous items in the current session and utilizes all item transitions on sessions in a more refined *** combining short-term sessions and long-term behavior,user dynamic preferences are *** experiments were conducted on three session-based recommendation datasets,and compared to the baseline methods,both the recall rate Recall@20 and the mean reciprocal rank MRR@20 of the SGT algorithm were improved,demonstrating the effectiveness of the SGT method.

关键词： Recommender system gated recurrent unit Transformer Session-based recommendation Graph neural networks

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Comparison of Two recurrent Neural Networks for Rainfall-Runoff Modeling in the Zou River Basin at Atchérigbé (Bénin)

引用

Journal of Geoscience and Environment Protection 2024年第9期12卷 167-181页

作者： Iboukoun Eliézer Biao Oscar Houessou Pierre Jérôme Zohou Adéchina Eric Alamou National School of Mathematical Engineering and Modeling National University of Sciences Technology Engineering and Mathematics Abomey Benin LaGEA National School of Public Works National University of Sciences Technology Engineering and Mathematics Abomey Benin International Chair of Mathematical Physics and Applications (ICMPA-UNESCO Chair) University of Abomey-Calavi Abomey-Calavi Bnin

Hydrological models are developed to simulate river flows over a watershed for many practical applications in the field of water resource management. The present paper compares the performance of two recurrent neural networks for rainfall-runoff modeling in the Zou River basin at Atchérigbé outlet. To this end, we used daily precipitation data over the period 1988-2010 as input of the models, such as the Long Short-Term Memory (LSTM) and recurrent Gate Networks (GRU) to simulate river discharge in the study area. The investigated models give good results in calibration (R2 = 0.888, NSE = 0.886, and RMSE = 0.42 for LSTM;R2 = 0.9, NSE = 0.9 and RMSE = 0.397 for GRU) and in validation (R2 = 0.865, NSE = 0.851, and RMSE = 0.329 for LSTM;R2 = 0.9, NSE = 0.865 and RMSE = 0.301 for GRU). This good performance of LSTM and GRU models confirms the importance of models based on machine learning in modeling hydrological phenomena for better decision-making.

关键词： Supervised Learning Modeling Zou Basin Long and Short-Term Memory gated recurrent unit Hyperparameters Optimization

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

An Adaptive Deep Learning Method Integrating Transfer Learning and Concept Drift Detection for Wind Power Forecasting

An Adaptive Deep Learning Method Integrating Transfer Learni...

引用

第43届中国控制会议

作者： Jingdi Wang Yundong Cao Huaikang Jin Shouyuan Fan Bin Wang Huaiping Jin Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Huaneng Renewables Co. Ltd.Yunnan Branch China Energy Engineering Group Yunnan Electric Power Design Institute

With the continuous advancement of new energy technologies,wind power is becoming an increasingly crucial source of clean *** to the stochastic and fluctuating nature of wind power data,there is a significant challenge known as concept drift,where the data distribution changes over *** poses a substantial obstacle to accurate prediction,leading to performance degradation of *** address this issue,an adaptive deep-learning wind power prediction method combining transfer learning and concept drift detection is *** method incorporates the concept of online transfer learning,transferring offline-trained model parameters to adapt quickly to new data distributions during the online ***,a combination of an online moving window and Kullback-Leibler(KL) divergence is employed to capture and analyze changes in the online predictor's output layer network *** facilitates a more precise detection of concept drift in wind power prediction,determining whether model updates are *** dynamically updating model parameters and effectively handling concept drift,the approach can maintain high accuracy and stability in wind power *** effectiveness and superiority of the proposed method are validated through the analysis of real-world wind power field data.

关键词： Wind Power Forecasting Concept Drift Transfer Learning Online Updating gated recurrent unit

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：