2022 First International Conference on Cyber-Energy Systems and Intelligent Energy (ICCSIE)最新文献

Load-Driven and Energy Consumption Conversion-based Enterprise Carbon Footprint Estimation Using Stacking Ensemble Learning 基于负荷驱动和能耗转换的叠加集成学习企业碳足迹估计

2022 First International Conference on Cyber-Energy Systems and Intelligent Energy (ICCSIE) Pub Date : 2023-01-14 DOI: 10.1109/ICCSIE55183.2023.10175215

Xie Yichao, Li Guangdi, Sun Qianxiang, Li Ziwen, Ma Hongyuan

{"title":"Load-Driven and Energy Consumption Conversion-based Enterprise Carbon Footprint Estimation Using Stacking Ensemble Learning","authors":"Xie Yichao, Li Guangdi, Sun Qianxiang, Li Ziwen, Ma Hongyuan","doi":"10.1109/ICCSIE55183.2023.10175215","DOIUrl":"https://doi.org/10.1109/ICCSIE55183.2023.10175215","url":null,"abstract":"Excessive carbon emissions have been established as the primary driving force behind global climate change, making the accurate prediction of carbon emissions crucial for addressing the imminent environmental crisis. The estimation of corporate carbon footprint (CCF) primarily relies on conventional annual carbon audits to determine a company’s carbon emissions. However, this approach may yield inaccurate results and inherently suffer from a one-year lag period. To address this challenge, our study presents a real-time CCF estimation method, introducing for the first time a fusion model based on Stacking ensemble learning. This model generates precise predictions regarding fossil energy consumption, subsequently calculating the corresponding direct carbon emissions. Indirect carbon emissions stem from the factory’s electricity consumption, which, when combined with direct carbon emissions, comprise the total corporate carbon emissions, ultimately enabling the estimation of the corporate carbon footprint. According to the results of empirical research, the proposed model exhibits a performance of 2.14% in Mean Absolute Percentage Error (MAPE) and 0.000513 in Root Mean Square Error (RMSE), metrics that significantly outperform other comparable predictive models.","PeriodicalId":391372,"journal":{"name":"2022 First International Conference on Cyber-Energy Systems and Intelligent Energy (ICCSIE)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121309636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Demand response intelligence recommendation based on knowledge graph and knowledge graph convolutional neural network 基于知识图和知识图卷积神经网络的需求响应智能推荐

2022 First International Conference on Cyber-Energy Systems and Intelligent Energy (ICCSIE) Pub Date : 2023-01-14 DOI: 10.1109/ICCSIE55183.2023.10175209

Shizhen Hu, Guangdi Li, Haoyi Wang, Hongyuan Ma, Ziwen Li

{"title":"Demand response intelligence recommendation based on knowledge graph and knowledge graph convolutional neural network","authors":"Shizhen Hu, Guangdi Li, Haoyi Wang, Hongyuan Ma, Ziwen Li","doi":"10.1109/ICCSIE55183.2023.10175209","DOIUrl":"https://doi.org/10.1109/ICCSIE55183.2023.10175209","url":null,"abstract":"A demand response intelligent recommendation model integrating knowledge graph and knowledge graph neural network(KGCN) is proposed to address the problems of cold start and sparsity of existing demand response intelligent recommendation algorithms. The structured triad of users’ electricity consumption is extracted from the users’ electricity consumption data set, followed by clustering users with similar electricity consumption behaviors through an improved clustering algorithm, and adding the clustering results to the knowledge graph together with the structured triad, using the KGCN model to embed the neighborhood entity information into the vector space to solve the data sparsity problem; meanwhile, using the prior knowledge in the graph to solve the cold start problem; to solve the To solve the recommendation lag problem, multi-hop propagation algorithm is introduced to reduce the set of candidate users and improve the recommendation efficiency. The results show that the intelligent recommendation model based on KGCN and knowledge graph can effectively solve the above problems and improve the indexes compared with the existing traditional algorithms.","PeriodicalId":391372,"journal":{"name":"2022 First International Conference on Cyber-Energy Systems and Intelligent Energy (ICCSIE)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124873030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on Construction and Application of Regulation of the Multiple Energy Systems Based on Knowledge Graph 基于知识图谱的多能源系统调控构建与应用研究

2022 First International Conference on Cyber-Energy Systems and Intelligent Energy (ICCSIE) Pub Date : 2023-01-14 DOI: 10.1109/ICCSIE55183.2023.10175243

Wang Zhenyu, Xiong Junjie, Hu Baohua, Wang Kui, Li Jia, Rao Zhen

{"title":"Research on Construction and Application of Regulation of the Multiple Energy Systems Based on Knowledge Graph","authors":"Wang Zhenyu, Xiong Junjie, Hu Baohua, Wang Kui, Li Jia, Rao Zhen","doi":"10.1109/ICCSIE55183.2023.10175243","DOIUrl":"https://doi.org/10.1109/ICCSIE55183.2023.10175243","url":null,"abstract":"In this paper, A knowledge graph construction method for regulation of the multiple energy systems combining top-down and bottom-up is proposed. Firstly, define the schema layer of the graph from top to bottom; and then, use different deep learning models to perform knowledge extraction and knowledge fusion on the resource plan, and build the data layer of the graph from the bottom up: the TextCNN model is used to classify the text of the plan, and LR-CNN model is used to named entities recognition for the plan; on the basis of named entity recognition, BERT-BILSTM-CRF model is used to extract the relationship between the named entities. Next, extract the corresponding triples to realize the construction of knowledge graph. Finally, the graph database is used to store and visualize the knowledge graph, and a case of the application process of the knowledge graph is studied. Compared with the traditional text retrieval method, the proposed method improves the decision-making efficiency and decision-making accuracy of multiple energy systems staff and users.","PeriodicalId":391372,"journal":{"name":"2022 First International Conference on Cyber-Energy Systems and Intelligent Energy (ICCSIE)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125310937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data-Based Event-Triggered Control of Zero-Sum Games with Completely Unknown Dynamics 动态完全未知的零和博弈的基于数据的事件触发控制

2022 First International Conference on Cyber-Energy Systems and Intelligent Energy (ICCSIE) Pub Date : 2023-01-14 DOI: 10.1109/ICCSIE55183.2023.10175289

Yuling Liang, Jin Xing, Juan Zhang, Hanguang Su

引用次数: 0

Power Data-Carbon Emission Prediction Model Based On Stacking Ensemble And Hyperparameter Optimization With Cross-Validation Method 基于叠加集成和交叉验证超参数优化的电力数据-碳排放预测模型

2022 First International Conference on Cyber-Energy Systems and Intelligent Energy (ICCSIE) Pub Date : 2023-01-14 DOI: 10.1109/ICCSIE55183.2023.10175226

Zheng Peixiang, Lai Guoshu, Chen Wuxiao, Cai Yuqing, Hu Zeyan, Xu Chenguan, Yu Meng

{"title":"Power Data-Carbon Emission Prediction Model Based On Stacking Ensemble And Hyperparameter Optimization With Cross-Validation Method","authors":"Zheng Peixiang, Lai Guoshu, Chen Wuxiao, Cai Yuqing, Hu Zeyan, Xu Chenguan, Yu Meng","doi":"10.1109/ICCSIE55183.2023.10175226","DOIUrl":"https://doi.org/10.1109/ICCSIE55183.2023.10175226","url":null,"abstract":"At present, the ‘‘greenhouse effect’’ caused by energy and environmental pollution makes energy carbon emission become the focus of the society, and accurate carbon emission prediction for high emission enterprises is the premise for the realization of emission reduction targets. This paper presents a power data-carbon emission prediction model based on a stacking ensemble and its hyperparameter optimization with a Cross-Validation method. Firstly, on the basis of obtaining power data and corresponding carbon emission data samples, a feature selection method of Emission Factor-Grey Correlation analysis is proposed for data specification.Then, the first layer sub-model is constructed separately, and the optimization method based on cross validation is combined to train respectively.Finally, the results of multiple single models are integrated by Stacking. The simulation results show that the proposed Cross-Validation optimization method can effectively improve the generalization ability of the model, and the carbon emission prediction model can reduce the maximum prediction error and improve the average prediction accuracy, which is better than the prediction of a single model. In addition, the model takes electricity consumption as the only input of the prediction model involving enterprise production data, which solves the time lag problem of enterprise carbon emission data mainly relying on carbon verification work and the difficulty of obtaining industrial fossil energy consumption data.","PeriodicalId":391372,"journal":{"name":"2022 First International Conference on Cyber-Energy Systems and Intelligent Energy (ICCSIE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122056594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-time scale multi-virtual power plant two-layer coordination mechanism and operation 多时间尺度多虚拟电厂两层协调机制及运行

2022 First International Conference on Cyber-Energy Systems and Intelligent Energy (ICCSIE) Pub Date : 2023-01-14 DOI: 10.1109/ICCSIE55183.2023.10175239

Tianfeng Chu, W. Zhang, Yan Lu, Y. Cai

{"title":"Multi-time scale multi-virtual power plant two-layer coordination mechanism and operation","authors":"Tianfeng Chu, W. Zhang, Yan Lu, Y. Cai","doi":"10.1109/ICCSIE55183.2023.10175239","DOIUrl":"https://doi.org/10.1109/ICCSIE55183.2023.10175239","url":null,"abstract":"Through advanced control strategies, virtual power plant (VPP) can effectively integrate distributed energy resources (DERs), break the geographical restrictions and provide possibility to improve the comprehensive energy efficiency of distributed energy. This paper considers the compatibility of virtual power plant and multi-agent structure, and establishes a virtual power plant control architecture based on multi-agent system. In view of the dual characteristics of load and power supply of virtual power plants, this paper gives its payment function as a power seller and power buyer, proposes a two-layer coordination mechaniser market, and establishes a double-layer optimization modelm when multi-virtual power plants participate in the pow for multi-virtual power plants. In order to reduce the influence of forecast uncertainty, a multi-time scale optimization model based on opportunity constraint planning is established. By the bi-level coordination mechanism of multiple VPPs and the multi-scale rolling scheduling, it can achieve economic optimization and maximize energy usage.","PeriodicalId":391372,"journal":{"name":"2022 First International Conference on Cyber-Energy Systems and Intelligent Energy (ICCSIE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130371724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modeling analysis of integrated energy systems considering time-sharing tariffs and carbon emissions 考虑分时电价和碳排放的综合能源系统建模分析

2022 First International Conference on Cyber-Energy Systems and Intelligent Energy (ICCSIE) Pub Date : 2023-01-14 DOI: 10.1109/ICCSIE55183.2023.10175230

Zhu Hao, Yang Lingxiao, Zhang Ning

引用次数: 0

A Direct Algorithm for the Stability and Hopf Bifurcation of A Logistic Differential Equation Versus Time Delay 一类时滞Logistic微分方程的稳定性和Hopf分岔的直接算法

2022 First International Conference on Cyber-Energy Systems and Intelligent Energy (ICCSIE) Pub Date : 2023-01-14 DOI: 10.1109/ICCSIE55183.2023.10175301

Tiao Cai, Lei Zhang, Hui-Long Jin

{"title":"A Direct Algorithm for the Stability and Hopf Bifurcation of A Logistic Differential Equation Versus Time Delay","authors":"Tiao Cai, Lei Zhang, Hui-Long Jin","doi":"10.1109/ICCSIE55183.2023.10175301","DOIUrl":"https://doi.org/10.1109/ICCSIE55183.2023.10175301","url":null,"abstract":"This paper investigates the local stability, the existence and the stability of Hopf bifurcation of typical logistic differential equation versus time delay. The local stability is considered within the framework of the $tau$ decomposition method, which involves the calculation of the PIR and the determination of the cross direction around it. And the complete and exact delay stable interval is given analytically. Subsequently, the nonlinear dynamics of bifurcating solutions are reviewed carefully by center manifold theorem and normal form theory. By the way, a new simple bilinear form is presented for reducing the computation of projection eigenvector according to the adjoint operator theory. In addition, the eigendecomposition strategy is more clearly characterized by the fact that it is applied to compute extensions of normal forms. And all the bifurcating parameters (Coefficients of normal form) are provided in the explicit expressions of the systems’ parameter, directly. At last, a direct algorithm is proposed to summarize the procedure for the computation of Hopf bifurcation. Typical example is introduced to show the correctness and effectiveness.","PeriodicalId":391372,"journal":{"name":"2022 First International Conference on Cyber-Energy Systems and Intelligent Energy (ICCSIE)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116277393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Controllable Structure Planning for Energy Internet 能源互联网可控结构规划

2022 First International Conference on Cyber-Energy Systems and Intelligent Energy (ICCSIE) Pub Date : 2023-01-14 DOI: 10.1109/ICCSIE55183.2023.10175258

Yushuai Li, T. Li, Yonghao Gui, D. Gao, Yan Zhang

引用次数: 0

A village integrated energy system operating in electricity market and hydrogen market 电力市场和氢能市场并网运行的乡村综合能源系统

2022 First International Conference on Cyber-Energy Systems and Intelligent Energy (ICCSIE) Pub Date : 2023-01-14 DOI: 10.1109/ICCSIE55183.2023.10175270

Hao Li, Wan Lv, Haoran Zhao, C. Chen

引用次数: 0