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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

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

electricity consumption and Economic Growth in the Beijing-Tianjin-Hebei Agglomeration of China

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Journal of Resources and Ecology 2016年第5期7卷 360-371页

作者：潘玉雪李海涛中国科学院地理科学与资源研究所陆地表层格局与模拟重点实验室北京100101

Nowadays, increased attention is being paid to the causal relationship between electricity consumption and economic growth. This paper attempts to examine the causal relationship between electricity consumption and economic growth for China＇s Beijing-Tianjin-Hebei agglomeration, using annual data covering the period 1982- 2008. In this study, unit root tests, the Johansen co-integration test, and the Granger causality test are applied. The empirical results indicate that the two series （electricity consumption and economic growth） of the three locales （Beijing, Tianjin, and Hebei） are non-stationary. But first differences of the two series are stationary. The results of the Johansen co-integration test indicate that electricity consumption and economic growth are co-integrated in Hebei and Tianjin while this is not the case in Beijing. The Granger causality test implies that there is causality running from electricity consumption to economic growth in all of the three locales. Causality running from economic growth to electricity consumption is found in Hebei and Beijing while this is not the case in Tianjin. This means that an increase in electricity consumption directly affects economic growth and that economic growth also stimulates further electricity consumption in Hebei and Beijing. But in Tianjin, an increase in electricity consumption directly affects economic growth while economic growth cannot affect electricity consumption. These findings can provide useful information for local governments of the three locales to formulate sustainable energy and economic policies. The study is of great significance for circular economy and building a resource-conserving society.

关键词： electricity consumption economic growth granger causality error correction modelling

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

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

Sensitivity to electricity consumption in urban business and commercial area buildings according to climatic change

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Journal of Central South University 2012年第3期19卷 770-776页

作者： LEE Kang-guk KIM Sung-bum HONG Won-hwa Research Center for Urban Affair Kyungil University School of Architecture Kyungpook National University

Recently, urban high temperature phenomenon has become a problem which results from human activities, the increase in energy consumption, and land-cover change in urban areas. As extremely hot weather caused by urban high temperature continues, demand for power is increased and results in the degradation of electricity reserves. The current trend in climate change, regardless of the summer and winter power demand, is likely to have much effect on the power demand. Thus, sensitivity to electricity consumption in urban areas due to climate change was researched. The results show that, 1） the basic unit of the sensitivity to electricity consumption in the target areas is 1.25-1.58W/（m2.℃）; 2） The maximum sensitivity is recorded at around 8：00 pm in the area crowded with commercial and business area. And in the business area, electricity consumption load is even from 9：00 am to 6：00 pm.

关键词： electricity consumption electricity load electricity reserves energy

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

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

Genetic algorithm-determined deep feedforward neural network architecture for predicting electricity consumption in real buildings

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能源与人工智能(英文) 2020年第2期2卷 83-100页

作者： X.J.Luo Lukumon O.Oyedele Anuoluwapo O.Ajayi Olugbenga O.Akinade Juan Manuel Davila Delgado Hakeem A.Owolabi Ashraf Ahmed Big Data Enterprise and Artificial Intelligence Laboratory(Big-DEAL) University of the West of England(UWE)Frenchay CampusBristolUnited Kingdom Department of Civil and Environmental Engineering Brunel University LondonLondonUnited Kindom

A genetic algorithm-determined deep feedforward neural network architecture(GA-DFNN)is proposed for both day-ahead hourly and week-ahead daily electricity consumption of a real-world campus building in the United *** to the comprehensive relationship between affecting factors and real-world building electricity consumption,the adoption of multiple hidden layers in the deep neural network(DFNN)algorithm would improve its prediction *** architecture of a DFNN model mainly refers to its quantity of hidden layers,quantity of neurons in the hidden layers,activation function in each layer and learning process to obtain the connecting *** optimal architecture of DFNN model was generally determined through a trial-and-error process,which is an exponential combinatorial problem and a tedious *** address this problem,genetic algorithm(GA)is adopted to automatically design an optimal architecture with improved generalization *** year and six months of measurement data from a campus building is used for training and testing the proposed GA-DFNN model,*** demonstrate the effectiveness of the proposed GA-DFNN prediction model,its prediction performance,including mean absolute percentage error,coefficient of determination,root mean square error and mean absolute error,was compared to the reference feedforward neural network models with single hidden layer,DFNN models with other architecture,random search determined DFNN model,long-short-term-memory model and temporal convolutional network *** comparison results show that the proposed GA-DFNN predictive model has superior performance than all the reference prediction models,demonstrating the optimization effectiveness of GA and the prediction effectiveness of DFNN model with multiple hidden layers and optimal architecture.

关键词： Prediction Deep learning Feedforward neural network Genetic algorithm electricity consumption