Research on Interpolation Method for Missing Electricity Consumption Data

Missing value is one of the main factors that cause dirty data.Without high-quality data,there will be no reliable analysis results and precise decision-making.Therefore,the data warehouse needs to integrate high-quality data consistently.In the power system,the electricity consumption data of some large users cannot be normally collected resulting in missing data,which affects the calculation of power supply and eventually leads to a large error in the daily power line loss rate.For the problem of missing electricity consumption data,this study proposes a group method of data handling(GMDH)based data interpolation method in distribution power networks and applies it in the analysis of actually collected electricity data.First,the dependent and independent variables are defined from the original data,and the upper and lower limits of missing values are determined according to prior knowledge or existing data information.All missing data are randomly interpolated within the upper and lower limits.Then,the GMDH network is established to obtain the optimal complexity model,which is used to predict the missing data to replace the last imputed electricity consumption data.At last,this process is implemented iteratively until the missing values do not change.Under a relatively small noise level(α=0.25),the proposed approach achieves a maximum error of no more than 0.605%.Experimental findings demonstrate the efficacy and feasibility of the proposed approach,which realizes the transformation from incomplete data to complete data.Also,this proposed data interpolation approach provides a strong basis for the electricity theft diagnosis and metering fault analysis of electricity enterprises.

Analysis of Electricity Consumption Pattern Clustering and Electricity Consumption Behavior

Studying user electricity consumption behavior is crucial for understanding their power usage patterns.However,the traditional clustering methods fail to identify emerging types of electricity consumption behavior.To address this issue,this paper introduces a statistical analysis of clusters and evaluates the set of indicators for power usage patterns.The 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 vehicles.Finally,the proposed method is validated through experiments,where the Davies-Bouldin index and profile coefficient are calculated and compared.Experiments showed that the optimal number of clusters is 4.This 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.

Research on the optimization strategy of customers’electricity consumption based on big data

Current power systems face significant challenges in supporting large-scale access to new energy sources,and the potential of existing flexible resources needs to be fully explored from the power supply,grid,and customer perspectives.This paper proposes a multi-objective electricity consumption optimization strategy considering the correlation between equipment and electricity consumption.It constructs a multi-objective electricity consumption optimization model that considers the correlation between equipment and electricity consumption to maximize economy and comfort.The results show that the proposed method can accurately assess the potential for electricity consumption optimization and obtain an optimal multi-objective electricity consumption strategy based on customers’actual electricity consumption demand.

PoQ-Consensus Based Private Electricity Consumption Forecasting via Federated Learning

With the rapid development of artificial intelligence and computer technology,grid corporations have also begun to move towards comprehensive intelligence and informatization.However,data-based informatization can bring about the risk of privacy exposure of fine-grained information such as electricity consumption data.The modeling of electricity consumption data can help grid corporations to have a more thorough understanding of users’needs and their habits,providing better services for users.Nevertheless,users’electricity consumption data is sensitive and private.In order to achieve highly efficient analysis of massive private electricity consumption data without direct access,a blockchain-based federated learning method is proposed for users’electricity consumption forecasting in this paper.Specifically,a blockchain systemis established based on a proof of quality(PoQ)consensus mechanism,and a multilayer hybrid directional long short-term memory(MHD-LSTM)network model is trained for users’electricity consumption forecasting via the federal learning method.In this way,the model of the MHD-LSTM network is able to avoid suffering from severe security problems and can only share the network parameters without exchanging raw electricity consumption data,which is decentralized,secure and reliable.The experimental result shows that the proposed method has both effectiveness and high-accuracy under the premise of electricity consumption data’s privacy preservation,and can achieve better performance when compared to traditional long short-term memory(LSTM)and bidirectional LSTM(BLSTM).

RankXGB-Based Enterprise Credit Scoring by Electricity Consumption in Edge Computing Environment

With the rapid development of the internet of things(IoT),electricity consumption data can be captured and recorded in the IoT cloud center.This provides a credible data source for enterprise credit scoring,which is one of the most vital elements during the financial decision-making process.Accordingly,this paper proposes to use deep learning to train an enterprise credit scoring model by inputting the electricity consumption data.Instead 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 model.Due to the high computational cost and the vast amounts of data,we design an edge computing framework to reduce the latency of enterprise credit evaluation.Specially,we design a two-stage deep learning task architecture,including a cloud-based weak credit ranking and an edge-based credit score calculation.In 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 results.In 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 normalization.The experiments demonstrate that our method can achieve accurate enterprise credit evaluation quickly.

The transaction behavior of cryptocurrency and electricity consumption

Rapidly increasing cryptocurrency prices have encouraged cryptocurrency miners to participate in cryptocurrency production,increasing network hashrates and electricity consumption.Growth in network hashrates has further crowded out small cryptocurrency investors owing to the heightened costs of mining hardware and electricity.These changes prompt cryptocurrency miners to become new investors,leading to cryptocurrency price increases.The potential bidirectional relationship between cryptocurrency price and electricity consumption remains unidentified.Hence,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 consumption.Particularly,we consider structural breaks induced by external shocks through stationary analysis and comovement relationships.Over 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 hashrates.Transaction fluctuations exert both a temporary effect and permanent influence on electricity consumption.Therefore,owing to the computational power deployed to wherever high profit is found,transactions are vital determinants of electricity consumption.

Analysis of users’ electricity consumption behavior based on ensemble clustering

Due to the increase in the number of smart meter devices,a power grid generates a large amount of data.Analyzing the data can help in understanding the users’electricity consumption behavior and demands;thus,enabling better service to be provided to them.Performing power load profile clustering is the basis for mining the users’electricity consumption behavior.By examining the complexity,randomness,and uncertainty of the users’electricity consumption behavior,this paper proposes an ensemble clustering method to analyze this behavior.First,principle component analysis(PCA)is used to reduce the dimensions of the data.Subsequently,the single clustering method is used,and the majority is selected for integrated clustering.As a result,the users’electricity consumption behavior is classified into different modes,and their characteristics are analyzed in detail.This paper examines the electricity power data of 19 real users in China for simulation purposes.This manuscript provides a thorough analysis along with suggestions for the users’weekly electricity consumption behavior.The results verify the effectiveness of the proposed method.

Analysis and Prediction of Regional Electricity Consumption Based on BP Neural Network

Electricity consumption forecasting is one of the most important tasks for power system workers,and plays an important role in regional power systems.Due to the difference in the trend of power load and the past in the new normal,the influencing factors are more diversified,which makes it more difficult to predict the current electricity consumption.In this paper,the grey system theory and BP neural network are combined to predict the annual electricity consumption in Jiangsu.According to the historical data of annual electricity consumption and the six factors affecting electricity consumption,the gray correlation analysis method is used to screen the important factors,and three factors with large correlation degree are selected as the input parameters of BP neural network.The power forecasting model uses nearly 18 years of data to train and validate the model.The results show that the gray correlation analysis and BP neural network method have higher accuracy in power consumption prediction,and the calculation is more convenient than traditional methods.

Electricity consumption propensity of different household members in apartment house

South Korea is an energy-guzzling country.Economic reasons in the country force its households to save more energy.Household energy consumption in South Korea has grown slow compared to other sectors and household energy consumption per capita is lower than the OECD (Organization for Economic Co-operation and Development) average.However,its per capita electricity use soared and expected to keep climbing mainly due to the increasing number of one-person household.To establish effective strategy against a possible electricity shortage,the amount of electricity energy consumption needs to be understood clearly first.We adopted both general survey and detailed survey for people living in apartment housings and collected data on electrical instrument use according to individual schedule.Based on these data,we tried to analyze electricity consumption patterns resulting from energy using activities at home and find out electricity using tendency according to each family member's characteristics in apartment housings.

Machine Learning Empowered Electricity Consumption Prediction

Electricity,being the most efficient secondary energy,contributes for a larger proportion of overall energy usage.Due to a lack of storage for energy resources,over supply will result in energy dissipation and substantial investment waste.Accurate electricity consumption prediction is vital because it allows for the preparation of potential power generation systems to satisfy the growing demands for electrical energy as well as:smart distributed grids,assessing the degree of socioeconomic growth,distributed system design,tariff plans,demand-side management,power generation planning,and providing electricity supply stability by balancing the amount of electricity produced and consumed.This paper proposes amedium-termprediction model that can predict electricity consumption for a given location in Saudi Arabia.Hence,this study implemented a standalone ArtificialNeuralNetwork(ANN)model and bagging ensemble for predicting total monthly electricity consumption in 18 locations across Saudi Arabia.The dataset used in this research is gathered exclusively from the Saudi Electric Company.The pre-processing phase included normalizing the data using min-max method and mapping the cyclical attribute to its sine and cosine facets.The number of neurons and learning rate of the standalone model were optimized using hyperparameter tuning.Finally,the standalone model was tested against the bagging ensemble using the optimized ANN.The bagging ensemble with an optimized ANN as the chosen classifier outperformed the standalone ANN model.The results for the proposed model produced 0.9116 Correlation Coefficient(CC),0.2836 Mean Absolute Percentage Error(MAPE),0.4578,Root Mean Squared Percentage Error(RMSPE),0.0298 MAE,and 0.069 Root Mean Squared Error(RMSE),respectively.

Research on Electricity Consumption Model of Library Building Based on Data Mining

With the exponential development of Chinese population,the massive energy consumption of buildings has recently become an interest subject.Although 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 subsystems.Using classical seasonal decomposition,hierarchical clustering,and apriori algorithm,this paper aims to provide an empirical model for consumption data in campus library.Smart meter data from a library in Beijing,China,is adopted in this paper.Building electricity consumption patterns are investigated on an hourly/daily/monthly basis.According to the monthly analysis,electricity consumption peaks each year around June and December due to teaching programs,social exams,and outdoor temperatures.Hourly data analysis revealed a relatively stable consumption pattern.It shows three different types of daily load profiles.Daily data analysis demonstrated a high relationship between HVAC consumption and building total consumption,with a lift value of 5.9.Furthermore,links between temperature and subsystems were also discovered.Through a case study of library,this study provides a unique insight into campus electricity use.The results could help to develop operational strategies for campus facilities.

A Novel Rolling and Fractional-ordered Grey System Model and Its Application for Predicting Industrial Electricity Consumption

Accurate and reasonable prediction of industrial electricity consumption is of great significance for promoting regional green transformation and optimizing the energy structure.However,the regional power system is complicated and uncertain,affected by multiple factors including climate,population and economy.This 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 models.The optimal fractional order is obtained by using the particle swarm optimization algorithm,which enhances the model adaptability.Then,the proposed model is employed to forecast and analyze the changing trend of industrial electricity consumption in Fujian province.Experimental results show that industrial electricity consumption in Fujian will maintain an upward growth and it is expected to 186.312 billion kWh in 2026.Compared 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.

Spatio-temporal Granularity Co-optimization Based Monthly Electricity Consumption Forecasting

Monthly electricity consumption forecasting(ECF)plays an important role in power system operation and electricity market trading.Widespread popularity of smart meters enables collection of fine-grained load data,which provides an opportunity for improvement of monthly ECF accuracy.In this letter,a spatio-temporal granularity co-optimization-based monthly ECF framework is proposed,which aims to find an optimal combination of temporal granularity and spatial clusters to improve monthly ECF accuracy.The framework is formulated as a nested bi-layer optimization problem.A grid search method combined with a greedy clustering method is proposed to solve the optimization problem.Superiority of the proposed method has been verified on a real smart meter dataset.

Total Electricity Consumption Forecasting Based on Temperature Composite Index and Mixed-Frequency Models

The total electricity consumption(TEC)can accurately reflect the operation of the national economy,and the forecasting of the TEC can help predict the economic development trend,as well as provide insights for the formulation of macro policies.Nowadays,high-frequency and massive multi-source data provide a new way to predict the TEC.In this paper,a"seasonal-cumulative temperature index"is constructed based on high-frequency temperature data,and a mixed-frequency prediction model based on multi-source big data(Mixed Data Sampling with Monthly Temperature and Daily Temperature index,MIDAS-MT-DT)is proposed.Experimental results show that the MIDAS-MT-DT model achieves higher prediction accuracy,and the"seasonal-cumulative temperature index"can improve prediction accuracy.

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

The COVID-19 pandemic has had drastic effects on societies around the world.Due to restrictions or recom-mendations,companies,industries and residents experienced changes in their routines and many people shifted to working from home.This led to alterations in electricity consumption between sectors and changes in daily patterns.Understanding 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 operation.In 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 Sweden.The 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 sectors.We 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.

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

Closely related to the economy,the analysis and management of electricity consumption has been widely studied.Conventional 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 analysis.In this paper,we introduce EcoVis,a visual analysis system that supports the industrial-level spatio-temporal correlation analysis in the electricity consumption data.We 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 map.We implement the system with the cooperation with domain experts.Experiments are conducted to demonstrate the effectiveness of our method.

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

Future electricity consumption may increase due to climate change,but the amplitude depends on the interaction between many uncertain mechanisms.Based 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 account.We 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 projection.The results of the policy model are generally lower than the linear model,which is the impact of policy factors.For 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,respectively.At 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 2010.The 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.