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Journal of Systems Science and Systems Engineering 2024年第2期33卷 207-231页

作者： Wenhao Zhou Hailin Li Zhiwei Zhang College of Business Administration Huaqiao UniversityQuanzhou 362021China College of Business Administration Capital University of Economics and BusinessBeijing 100070China

Accurate and reasonable prediction of industrial electricity consumption is of great significance for promoting regional green transformation and optimizing the energy ***,the regional power system is complicated and uncertain,affected by multiple factors including climate,population and *** paper incorporates structure expansion,parameter optimization and rolling mechanism into a system forecasting framework,and designs a novel rolling and fractional-ordered grey system model to forecast the industrial electricity consumption,improving the accuracy of the traditional grey *** optimal fractional order is obtained by using the particle swarm optimization algorithm,which enhances the model ***,the proposed model is employed to forecast and analyze the changing trend of industrial electricity consumption in Fujian *** results show that industrial electricity consumption in Fujian will maintain an upward growth and it is expected to 186.312 billion kWh in *** with other seven benchmark prediction models,the proposed grey system model performs best in terms of both simulation and prediction performance metrics,providing scientific reference for regional energy planning and electricity market operation.

关键词： electricity consumption grey system theory prediction model fractional order

Analysis of electricity consumption Pattern Clustering and electricity consumption Behavior

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Energy Engineering 2024年第9期121卷 2639-2653页

作者： Liang Zhu Junyang Liu Chen Hu Yanli Zhi Yupeng Liu Power Supply Service Management Center State Grid Jiangxi Electric Power Corporation LimitedNanchang330077China State Grid Jiangxi Electric Power Corporation Limited Nanchang330077China College of Electrical and Information Engineering Hunan UniversityChangsha410082China

Studying user electricity consumption behavior is crucial for understanding their power usage ***,the traditional clustering methods fail to identify emerging types of electricity consumption *** address this issue,this paper introduces a statistical analysis of clusters and evaluates the set of indicators for power usage *** fuzzy C-means clustering algorithm is then used to analyze 6 months of electricity consumption data in 2017 from energy storage equipment,agricultural drainage irrigation,port shore power,and electric ***,the proposed method is validated through experiments,where the Davies-Bouldin index and profile coefficient are calculated and *** showed that the optimal number of clusters is *** study demonstrates the potential of using a fuzzy C-means clustering algorithmin identifying emerging types of electricity consumption behavior,which can help power system operators and policymakers to make informed decisions and improve energy efficiency.

关键词： electricity consumption clustering consumption behavior fuzzy C-means

RankXGB-Based Enterprise Credit Scoring by electricity consumption in Edge Computing Environment

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Computers, Materials & Continua 2023年第4期75卷 197-217页

作者： Qiuying Shen Wentao Zhang Mofei Song State Grid Suzhou Power Supply Company Suzhou215004China School of Computer Science and Engineering Southeast UniversityNanjing210000China

With the rapid development of the internet of things(IoT),electricity consumption data can be captured and recorded in the IoT cloud *** provides a credible data source for enterprise credit scoring,which is one of the most vital elements during the financial decision-making ***,this paper proposes to use deep learning to train an enterprise credit scoring model by inputting the electricity consumption *** of predicting the credit rating,our method can generate an absolute credit score by a novel deep ranking model–ranking extreme gradient boosting net(rankXGB).To boost the performance,the rankXGB model combines several weak ranking models into a strong *** to the high computational cost and the vast amounts of data,we design an edge computing framework to reduce the latency of enterprise credit ***,we design a two-stage deep learning task architecture,including a cloud-based weak credit ranking and an edge-based credit score *** the first stage,we send the electricity consumption data of the evaluated enterprise to the computing cloud server,where multiple weak-ranking networks are executed in parallel to produce multiple weak-ranking *** the second stage,the edge device fuses multiple ranking results generated in the cloud server to produce a more reliable ranking result,which is used to calculate an absolute credit score by score *** experiments demonstrate that our method can achieve accurate enterprise credit evaluation quickly.

关键词： electricity consumption enterprise credit scoring edge computing deep learning

Evaluating the effects of the COVID-19 pandemic on electricity consumption patterns in the residential,public,commercial and industrial sectors in Sweden

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Energy and AI 2023年第4期14卷 514-521页

作者： Vera van Zoest Karl Lindberg Fouad El Gohary Cajsa Bartusch Department of Information Technology Uppsala UniversityP.O.Box 337Uppsala 75105Sweden Department of Systems Science for Defence and Security Swedish Defence UniversityP.O.Box 27805Stockholm 11593Sweden Department of Civil and Industrial Engineering Uppsala UniversityP.O.Box 534Uppsala SE-75121Sweden

The COVID-19 pandemic has had drastic effects on societies around the *** to restrictions or recom-mendations,companies,industries and residents experienced changes in their routines and many people shifted to working from *** led to alterations in electricity consumption between sectors and changes in daily *** how various properties and features of load patterns in the electricity network were affected is important for forecasting the network’s ability to respond to sudden changes and shocks,and helping system operators improve network management and *** this study,we quantify the extent to which the COVID-19 pandemic has led to shifts in the electricity consumption patterns of different sectors in *** results show that working from home during the pandemic has led to an increase in the residential sector’s total consumption and changes in its consumption patterns,whereas there were only slight decreases in the industrial sector and relatively few changes in the public and commercial *** discuss the reasons for these changes,the effects that these changes will have on expected future electricity consumption patterns,as well as the effects on potential demand flexibility in a future where working from home has become the new norm.

关键词： electricity consumption COVID-19 Smart meter data Regression model Demand response

Research on electricity consumption Model of Library Building Based on Data Mining

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Energy Engineering 2022年第6期119卷 2407-2429页

作者： Jiaming Dou Hongyan Ma Rong Guo School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing102616China Institute of Distributed Energy Storage Safety Big Data Beijing University of Civil Engineering and ArchitectureBeijing102616China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing102616China

With the exponential development of Chinese population,the massive energy consumption of buildings has recently become an interest *** much research has been conducted on residential buildings,heating ventilation and air conditioning(HVAC),little research has been conducted on the relationship between student’s behavior,campus buildings,and their *** classical seasonal decomposition,hierarchical clustering,and apriori algorithm,this paper aims to provide an empirical model for consumption data in campus *** meter data from a library in Beijing,China,is adopted in this *** electricity consumption patterns are investigated on an hourly/daily/monthly *** to the monthly analysis,electricity consumption peaks each year around June and December due to teaching programs,social exams,and outdoor *** data analysis revealed a relatively stable consumption *** shows three different types of daily load *** data analysis demonstrated a high relationship between HVAC consumption and building total consumption,with a lift value of ***,links between temperature and subsystems were also *** a case study of library,this study provides a unique insight into campus electricity *** results could help to develop operational strategies for campus facilities.

关键词： electricity consumption data mining load profile campus building

The transaction behavior of cryptocurrency and electricity consumption

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Financial Innovation 2023年第1期9卷 1197-1214页

作者： Mingbo Zheng Gen‑Fu Feng Xinxin Zhao Chun‑Ping Chang School of Economics and Management Chang’an UniversityXi’an 710064ShaanxiPeople’s Republic of China School of Economics and Finance Xi’an Jiaotong UniversityXi’an 710061ShaanxiPeople’s Republic of China Department of Marketing Management Shih Chien UniversityKaohsiung 84550Taiwan

Rapidly increasing cryptocurrency prices have encouraged cryptocurrency miners to participate in cryptocurrency production,increasing network hashrates and electricity *** in network hashrates has further crowded out small cryptocurrency investors owing to the heightened costs of mining hardware and *** changes prompt cryptocurrency miners to become new investors,leading to cryptocurrency price *** potential bidirectional relationship between cryptocurrency price and electricity consumption remains ***,this research thus utilizes July 312015–July 122019 data from 13 cryptocurrencies to investigate the short-and long-run causal effects between cryptocurrency transaction and electricity ***,we consider structural breaks induced by external shocks through stationary analysis and comovement *** the examined time period,we found that the series of cryptocurrency transaction and electricity consumption gradually returns to mean convergence after undergoing daily shocks,with prices trending together with *** fluctuations exert both a temporary effect and permanent influence on electricity ***,owing to the computational power deployed to wherever high profit is found,transactions are vital determinants of electricity consumption.

关键词： Transaction behavior electricity consumption Cryptocurrency Comovement

EcoVis:visual analysis of industrial-level spatio-temporal correlations in electricity consumption

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Frontiers of Computer Science 2022年第2期16卷 98-108页

作者： Yong XIAO Kaihong ZHENG Supaporn LONAPALAWONG Wenjie LU Zexian CHEN Bin QIAN Tianye ZHANG Xin WANG Wei CHEN Electric Power Research Institute China Southern Power GridGuangzhou 510663China Digital Grid Research Institute China Southern Power GridGuangzhou 510663China The State Key Lab of CAD&CG Zhejiang UniversityHangzhou 310058China School of Computer Science Zhejiang UniversityHangzhou 310058China

Closely related to the economy,the analysis and management of electricity consumption has been widely *** approaches mainly focus on the prediction and anomaly detection of electricity consumption,which fails to reveal the in-depth relationships between electricity consumption and various factors such as industry,weather etc..In the meantime,the lack of analysis tools has increased the difficulty in analytical tasks such as correlation analysis and comparative *** this paper,we introduce EcoVis,a visual analysis system that supports the industrial-level spatio-temporal correlation analysis in the electricity consumption *** not only propose a novel approach to model spatio-temporal data into a graph structure for easier correlation analysis,but also introduce a novel visual representation to display the distributions of multiple instances in a single *** implement the system with the cooperation with domain *** are conducted to demonstrate the effectiveness of our method.

关键词： spatio-temporal data electricity consumption correlation analysis visual analysis visualization

Clustering-based probability distribution model for monthly residential building electricity consumption analysis

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Building Simulation 2021年第1期14卷 149-164页

作者： Jieyan Xu Xuyuan Kang Zheng Chen Da Yan Siyue Guo Yuan Jin Tianyi Hao Rongda Jia State Grid(Beijing)Integrated Energy Planning and D&R Institute Co. Ltd.Beijing100052China Building Energy Research Center School of ArchitectureTsinghua UniversityBeijing100084China Institute of Energy Environment and EconomyTsinghua UniversityBeijing100084China

electricity is now the major form of energy used in residential buildings and has seen a significant increase in usage over the past *** of the main features of electricity use in residential buildings is the diversity of total electricity consumption and use patterns among *** models may not be able to simulate and generate electricity use curves or reflect the variations *** fill this gap,this research simulates electricity use curves in residential buildings with a clustering-based probability distribution *** model extracts feature parameters to represent the electricity use level and patterns and then conducts a two-step cluster analysis to identify the distinctions of both electricity use levels and *** on the clustering results,probability distributions are fitted for all feature parameters within each *** model is then validated with three validation *** electricity consumption in households of the Jiangsu Province,China,was studied to test the performance of the ***,this paper discusses the application of this model under different spatial resolutions and analyzes the temporal-relevant model features.

关键词： cluster analysis probability distribution residential building electricity consumption

Data-driven sensitivity analysis and electricity consumption prediction for water source heat pump system using limited information

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Building Simulation 2021年第4期14卷 1005-1016页

作者： Shaobo Sun Huanxin Chen Department of Refrigeration&Cryogenics Engineering School of Energy and Power EngineeringHuazhong University of Science and TechnologyWuhanChina

The studies on predicting the energy consumption of air conditioning systems are meaningful to building energy conservation and management. Generally, the more comprehensive the building information is, the easier the prediction model can be developed. However, it is very difficult to get detailed information about existing/old buildings (information-poor buildings), it is a big challenge to predict the energy consumption accurately by limited information. This study aims to predict the electricity consumption of the water source heat pump system of an office building based on meteorological data. The key variables are selected by error analysis and sensitivity analysis, and the effects of each variable on the models’ prediction performance can be obtained. Besides, the prediction models are established by support vector regression algorithm and trained by the local meteorological data. The results show that the positive and negative variables can be identified, and these positive variables are responsible for more than 70% of the total importance. Moreover, the root mean square error falls to 4.6044 from 7.8227 and the relative square error falls to 0.1494 from 0.4313 when the negative inputs are removed. And the errors reduce further to 4.1160 and 0.1194 by parameter optimization.

关键词： water source heat pump electricity consumption support vector regression prediction sensitivity analysis parameter optimization

Projection of residential and commercial electricity consumption under SSPs in Jiangsu province, China

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Advances in Climate Change Research 2020年第2期11卷 131-140页

作者： ZHANG Mi CHENG Chin-Hsien MA Hong-Yun Key Laboratory of Meteorological Disaster Ministry of Education(KLME)Nanjing University of Information Science and TechnologyNanjing210044China Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD) Nanjing University of Information Science and TechnologyNanjing210044China Institute for Climate and Application Research(ICAR) Nanjing University of Information Science and TechnologyNanjing210044China

Future electricity consumption may increase due to climate change,but the amplitude depends on the interaction between many uncertain *** on the linear model and policy model,the residential and commercial electricity consumption in Jiangsu province are projected under the shared socioeconomic pathways(SSPs).The linear model considers climate and socioeconomic factors,and the policy model also takes policy factors into *** find that the cooling degree days(CDD)coefficient is about 3 times of heating degree days(HDD),which reflects that the cooling demand is much larger than heating,and also shows in the *** results of the policy model are generally lower than the linear model,which is the impact of policy *** example,the SSP1 and SSP2 of the policy model are 320 TW h and 241.6 TW h lower than the linear model in 2100,*** the end of the 21st century,the residential and commercial electricity consumption in Jiangsu province will reach 107.7–937.9 TW h per year,1.3–11.6 times of *** SSP1 scenario under the policy model is based on feasible assumptions,and can be used as the target scenario for policymakers to establish energy intensity reduction targets.

关键词： electricity consumption Residential and commercial Climate change Projection SSP