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.

Reliability evaluation of IGBT power module on electric vehicle using big data

There are challenges to the reliability evaluation for insulated gate bipolar transistors(IGBT)on electric vehicles,such as junction temperature measurement,computational and storage resources.In this paper,a junction temperature estimation approach based on neural network without additional cost is proposed and the lifetime calculation for IGBT using electric vehicle big data is performed.The direct current(DC)voltage,operation current,switching frequency,negative thermal coefficient thermistor(NTC)temperature and IGBT lifetime are inputs.And the junction temperature(T_(j))is output.With the rain flow counting method,the classified irregular temperatures are brought into the life model for the failure cycles.The fatigue accumulation method is then used to calculate the IGBT lifetime.To solve the limited computational and storage resources of electric vehicle controllers,the operation of IGBT lifetime calculation is running on a big data platform.The lifetime is then transmitted wirelessly to electric vehicles as input for neural network.Thus the junction temperature of IGBT under long-term operating conditions can be accurately estimated.A test platform of the motor controller combined with the vehicle big data server is built for the IGBT accelerated aging test.Subsequently,the IGBT lifetime predictions are derived from the junction temperature estimation by the neural network method and the thermal network method.The experiment shows that the lifetime prediction based on a neural network with big data demonstrates a higher accuracy than that of the thermal network,which improves the reliability evaluation of system.

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.

Design and Implementation of a Battery Big Data Platform Through Intelligent Connected Electric Vehicles

The development of a battery management algorithm is highly dependent on high-quality battery operation data,especially the data in extreme conditions such as low temperatures.The data in faults are also essential for failure and safety management research.This study developed a battery big data platform to realize vehicle operation,energy interaction and data management.First,we developed an electric vehicle with vehicle navigation and position detection and designed an environmental cabin that allows the vehicle to operate autonomously.Second,charging and heating systems based on wireless energy transfer were developed and equipped on the vehicle to investigate optimal charging and heating methods of the batteries in the vehicle.Third,the data transmission network was designed,a real-time monitoring interface was developed,and the self-developed battery management system was used to measure,collect,upload,and store battery operation data in real time.Finally,experimental validation was performed on the platform.Results demonstrate the efficiency and reliability of the platform.Battery state of charge estimation is used as an example to illustrate the availability of battery operation data.

Obtaining 2D Soil Resistance Profiles from the Integration of Electrical Resistivity Data and Standard Penetration Test (SPT) and Light Dynamic Penetrometer (DPL) Resistance Tests—Applications in Mass Movements Studies

In Brazil and various regions globally, the initiation of landslides is frequently associated with rainfall;yet the spatial arrangement of geological structures and stratification considerably influences landslide occurrences. The multifaceted nature of these influences makes the surveillance of mass movements a highly intricate task, requiring an understanding of numerous interdependent variables. Recent years have seen an emergence in scholarly research aimed at integrating geophysical and geotechnical methodologies. The conjoint examination of geophysical and geotechnical data offers an enhanced perspective into subsurface structures. Within this work, a methodology is proposed for the synchronous analysis of electrical resistivity geophysical data and geotechnical data, specifically those extracted from the Light Dynamic Penetrometer (DPL) and Standard Penetration Test (SPT). This study involved a linear fitting process to correlate resistivity with N10/SPT N-values from DPL/SPT soundings, culminating in a 2D profile of N10/SPT N-values predicated on electrical profiles. The findings of this research furnish invaluable insights into slope stability by allowing for a two-dimensional representation of penetration resistance properties. Through the synthesis of geophysical and geotechnical data, this project aims to augment the comprehension of subsurface conditions, with potential implications for refining landslide risk evaluations. This endeavor offers insight into the formulation of more effective and precise slope management protocols and disaster prevention strategies.

Blockchain and MEC-Assisted Reliable Billing Data Transmission over Electric Vehicular Network:An Actor–Critic RL Approach

Recently,electric vehicles(EVs)have been widely used under the call of green travel and environmental protection,and diverse requirements for charging are also increasing gradually.In order to ensure the authenticity and privacy of charging information interaction,blockchain technology is proposed and applied in charging station billing systems.However,there are some issues in blockchain itself,including lower computing efficiency of the nodes and higher energy consumption in the consensus process.To handle the above issues,in this paper,combining blockchain and mobile edge computing(MEC),we develop a reliable billing data transmission scheme to improve the computing capacity of nodes and reduce the energy consumption of the consensus process.By jointly optimizing the primary and replica nodes offloading decisions,block size and block interval,the transaction throughput of the blockchain system is maximized,as well as the latency and energy consumption of the system are minimized.Moreover,we formulate the joint optimization problem as a Markov decision process(MDP).To tackle the dynamic and continuity of the system state,the reinforcement learning(RL)is introduced to solve the MDP problem.Finally,simulation results demonstrate that the performance improvement of the proposed scheme through comparison with other existing schemes.

A data complementary method for thunderstorm point charge localization based on atmospheric electric field apparatus array group

Data loss or distortion causes adverse effects on the accuracy and stability of the thunderstorm point charge localization.To solve this problem,we propose a data complementary method based on the atmospheric electric field apparatus array group.The electric field component measurement model of the atmospheric electric field apparatus is established,and the orientation parameters of the thunderstorm point charge are defined.Based on the mirror method,the thunderstorm point charge coordinates are obtained by using the potential distribution formulas.To test the validity of the basic algorithm,the electric field component measurement error and the localization accuracy are studied.Besides the azimuth angle and the elevation angle,the localization parameters also include the distance from the apparatus to the thunderstorm cloud.Based on a primary electric field apparatus,we establish the array group of apparatuses.Based on this,the data measured by each apparatus is complementarily processed to regain the thunderstorm point charge position.The results show that,compared with the radar map data,this method can accurately reflect the location of the thunderstorm point charge,and has a better localization effect.Additionally,several observation results during thunderstorm weather have been presented.

Machine Learning Mapping of Soil Apparent Electrical Conductivity on a Research Farm in Mississippi

Open-source and free tools are readily available to the public to process data and assist producers in making management decisions related to agricultural landscapes. On-the-go soil sensors are being used as a proxy to develop digital soil maps because of the data they can collect and their ability to cover a large area quickly. Machine learning, a subcomponent of artificial intelligence, makes predictions from data. Intermixing open-source tools, on-the-go sensor technologies, and machine learning may improve Mississippi soil mapping and crop production. This study aimed to evaluate machine learning for mapping apparent soil electrical conductivity (ECa) collected with an on-the-go sensor system at two sites (i.e., MF2, MF9) on a research farm in Mississippi. Machine learning tools (support vector machine) incorporated in Smart-Map, an open-source application, were used to evaluate the sites and derive the apparent electrical conductivity maps. Autocorrelation of the shallow (ECas) and deep (ECad) readings was statistically significant at both locations (Moran’s I, p 0.001);however, the spatial correlation was greater at MF2. According to the leave-one-out cross-validation results, the best models were developed for ECas versus ECad. Spatial patterns were observed for the ECas and ECad readings in both fields. The patterns observed for the ECad readings were more distinct than the ECas measurements. The research results indicated that machine learning was valuable for deriving apparent electrical conductivity maps in two Mississippi fields. Location and depth played a role in the machine learner’s ability to develop maps.

Electrical Data Matrix Decomposition in Smart Grid

As the development of smart grid and energy internet, this leads to a significantincrease in the amount of data transmitted in real time. Due to the mismatch withcommunication networks that were not designed to carry high-speed and real time data,data losses and data quality degradation may happen constantly. For this problem,according to the strong spatial and temporal correlation of electricity data which isgenerated by human’s actions and feelings, we build a low-rank electricity data matrixwhere the row is time and the column is user. Inspired by matrix decomposition, we dividethe low-rank electricity data matrix into the multiply of two small matrices and use theknown data to approximate the low-rank electricity data matrix and recover the missedelectrical data. Based on the real electricity data, we analyze the low-rankness of theelectricity data matrix and perform the Matrix Decomposition-based method on the realdata. The experimental results verify the efficiency and efficiency of the proposed scheme.

Intelligent Energy Utilization Analysis Using IUA-SMD Model Based Optimization Technique for Smart Metering Data

Smart metering has gained considerable attention as a research focus due to its reliability and energy-efficient nature compared to traditional electromechanical metering systems. Existing methods primarily focus on data management,rather than emphasizing efficiency. Accurate prediction of electricity consumption is crucial for enabling intelligent grid operations,including resource planning and demandsupply balancing. Smart metering solutions offer users the benefits of effectively interpreting their energy utilization and optimizing costs. Motivated by this,this paper presents an Intelligent Energy Utilization Analysis using Smart Metering Data(IUA-SMD)model to determine energy consumption patterns. The proposed IUA-SMD model comprises three major processes:data Pre-processing,feature extraction,and classification,with parameter optimization. We employ the extreme learning machine(ELM)based classification approach within the IUA-SMD model to derive optimal energy utilization labels. Additionally,we apply the shell game optimization(SGO)algorithm to enhance the classification efficiency of the ELM by optimizing its parameters. The effectiveness of the IUA-SMD model is evaluated using an extensive dataset of smart metering data,and the results are analyzed in terms of accuracy and mean square error(MSE). The proposed model demonstrates superior performance,achieving a maximum accuracy of65.917% and a minimum MSE of0.096. These results highlight the potential of the IUA-SMD model for enabling efficient energy utilization through intelligent analysis of smart metering data.

A Modified Back/Forward Sweep Method Based on the Electricity Consumption Data

With the development of distribution automation system, the centralized meter reading system has been adopted more and more extensively, which provides real-time electricity consumption data of end-users, and consequently lays foundation for operating condition on-line analysis of distribution network. In this paper, a modified back/forward sweep method, which directly uses real-time electricity consumption data acquired from the centralized meter reading system, is proposedto realize voltage analysis based on 24-hour electricity consumption data of a typical transformer district. Furthermore, the calculated line losses are verified through data collected from the energy metering of the distribution transformer, illustrating that the proposed method can be applied in analyzing voltage level and discovering unknown energy losses, which will lay foundation for on-line analysis, calculation and monitoring of power distribution network.

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.

Applications of Data Mining Theory in Electrical Engineering

In this paper, we adopt a novel applied approach to fault analysis based on data mining theory. In our researches, global information will be introduced into the electric power system, we are using mainly cluster analysis technology of data mining theory to resolve quickly and exactly detection of fault components and fault sections, and finally accomplish fault analysis. The main technical contributions and innovations in this paper include, introducing global information into electrical engineering, developing a new application to fault analysis in electrical engineering. Data mining theory is defined as the process of automatically extracting valid, novel, potentially useful and ultimately comprehensive information from large databases. It has been widely utilized in both academic and applied scientific researches in which the data sets are generated by experiments. Data mining theory will contribute a lot in the study of electrical engineering.

Application of Federated Learning Algorithm Based on K-Means in Electric Power Data

Accurate electricity forecasting is the key basis for guiding the power sector to arrange operation plans and guaranteeing the profitability of electric power companies.However,with the increasing demand of enterprises and departments for data security,the phenomenon of“Isolated Data Island”becomes more and more serious,resulting in the accuracy loss of the traditional electricity prediction model.Federated learning,as an emerging artificial intelligence technology,is designed to ensure data privacy while carrying out efficient machine learning,which provides a new way to solve the problem of“Isolated Data Island”in terms of electricity forecasting.Nonetheless,due to the popularity of smart meters,the collected electricity data presents the characteristics of uneven distribution and huge data volume,so it is difficult to apply the electric quantity prediction model generated only by federated learning in practice.To solve this problem,a clustering federated learning method(C-FL)is proposed to protect data privacy while improving the accuracy of power prediction.Firstly,C-FL uses K-means algorithm to cluster power data locally in power enterprises,and then builds accurate power forecasting models for each class of power data combined with other local clients through federated learning.A large number of experimental results show that the clustering federated learning method proposed in this paper is superior to the existing federated learning models in terms of the accuracy of electric power forecasting.

Mining Rules from Electrical Load Time Series Data Set

The mining of the rules from the electrical load time series data which are collected from the EMS (Energy Management System) is discussed. The data from the EMS are too huge and sophisticated to be understood and used by the power system engineer, while useful information is hidden in the electrical load data. The authors discuss the use of fuzzy linguistic summary as data mining method to induce the rules from the electrical load time series. The data preprocessing techniques are also discussed in the paper.