Power Big Data Research Articles

Power consumption state evaluation of important power customers based on AHP‐TOPSIS algorithm

AbstractIn the construction of a new power system, the identification and evaluation of power consumption status of power customers will become an important basis for them to participate in the emerging businesses such as demand response and virtual power plants. In order to ensure the power safety of important power customers, a new evaluation of power consumption status of important power customers based on the AHP (analytic hierarchy process)‐TOPSIS (technique for order preference by similarity to an ideal solution) algorithm is proposed by fully mining and applying the power big data. Firstly, a power consumption big data analysis platform based on the Hadoop architecture is built to provide a high‐performance platform support for big data analysis. Secondly, nine evaluation indexes are constructed from the three dimensions of voltage, load and synthesis, which objectively and scientifically describes the power consumption status of important power customers. Finally, the AHP‐TOPSIS algorithm is used to evaluate and analyse the voltage, load and comprehensive indicators respectively, thus, obtaining the evaluation values of three kinds of indicators. The power consumption status scores of important power customers are determined by the variable weight weighted summation. The rationality and feasibility of the method and algorithm are proved by example analysis and field verification. This method helps to promote the transformation from post fault emergency repair to warning beforehand. It has the multiple effects of ensuring safe power consumption, supporting accurate patrolling and active emergency repair serving.

Legitimacy and Limits of Administrative Power in the Big Data Era

The advent of the big data era has profoundly transformed the exercise of administrative power, enhancing the efficiency and accuracy of administrative decisions while simultaneously posing challenges to legitimacy and the potential for abuse of power. This study examines the impact of big data technology on administrative power, systematically analyzing the foundations and limits of administrative power's legitimacy. It explores how to achieve a balance between legitimacy and limits of administrative power in the context of big data. By examining the intersection of traditional theories of power legitimacy and big data technology, the study proposes strategies for enhancing transparency in administrative decisions, strengthening public participation, and promoting inter-departmental cooperation. It also looks forward to innovative paths for exercising administrative power in the future. The results of this study are significant for both administrative management practices and theoretical development in the big data era.

Algorithm for detecting abnormal electricity usage by customers based on electricity big data and deep learning

Abstract In order to optimize the effect of individuals’ aberrant power utilization detection and reinforce the recognition rate and accuracy of abnormal power consumption detection of electricity use, an aberrant power utilization identification algorithm based on electric power big data and deep learning is proposed. Taking the two-way dialogue between power users and the power company as the basic perspective, real-time monitoring and collection of power consumption dynamics of users is carried out, and the power big data collection framework is designed. The retrieval of aberrant power utilization detection indexes is completed. Based on the deep learning algorithm, the artificial neural network base operation medium is combined to quantitatively solve the aberrant power utilization indexes, completing the design of a user aberrant power utilization detection algorithm. Experimental results show that the accuracy of the designed algorithm is 0.963, the F1 value is 0.985, and the AUC value is 0.942, which can effectively identify the aberrant power utilization and has a better performance of abnormal power consumption identification and detection.

Temporal Refinement of Major Primary Air Pollutant Emissions Based on Electric Power Big Data: A Case of the Cement Industry in Tangshan City

Temporal Refinement of Major Primary Air Pollutant Emissions Based on Electric Power Big Data: A Case of the Cement Industry in Tangshan City

Data analysis method for power big data

This paper discusses a data quality analysis and evaluation mode based on power big data. Its basic content is to use the analysis platform template to conduct acquisition control and process rule analysis, and then use the data quality management module to call out the platform database system that realizes the inspection and analysis function, and retrieve and generate inspection data from the entity base. The analysis program will classify, count, sort, and sort the data, The final generated quantitative indicator data reflecting the project implementation status and data quality will be saved in the analysis result table. The analysis result report called from the middle platform can obtain a more detailed data quality analysis and evaluation report reflecting the data quality of various quantitative projects. This paper has improved the intelligent degree of information quality management and evaluation, realized the intelligent control of information service quality, adapted to the needs of large-scale information service management, and completed the quantitative research and evaluation of information completeness, timeliness, accuracy, accuracy and other important parameters.

Applying Machine Learning for Antibiotic Development and Prediction of Microbial Resistance.

Antimicrobial resistance (AMR) poses a serious threat to human health worldwide. It is now more challenging than ever to introduce a potent antibiotic to the market considering rapid emergence of antimicrobial resistance, surpassing the rate of antibiotic drug discovery. Hence, new approaches need to be developed to accelerate the rate of drug discovery process and meet the demands for new antibiotics, while reducing the cost of their development. Machine learning holds immense promise of becoming a useful tool, especially since in the last two decades, exponential growth has occurred in computational power and biological big data analytics. Recent advancements in machine learning algorithms for drug discovery have provided significant clues for potential antibiotic classes. Apart from discovery of new scaffolds, the machine learning protocols will significantly impact prediction of AMR patterns and drug metabolism. In this review, we outline power of machine learning in antibiotic drug discovery, metabolic fate, and AMR prediction to support researchers engaged and interested in this field.

Simulation and Modelling in Cloud Computing Based Smart Grid Power Big Data Analysis Technology

Simulation and Modelling in Cloud Computing Based Smart Grid Power Big Data Analysis Technology

Research on Inventory Control Method Based on Demand Response of Power Big Data

The supply chain functions as a complex web, interconnecting various stakeholders such as suppliers, manufacturers, wholesalers, retailers, and ultimately, end consumers. Central to effective supply chain management is the meticulous handling of inventory, a critical factor influencing both cost efficiency and service excellence throughout the entire network. The management of inventory within this context extends beyond the confines of individual enterprises, bearing significance across the entirety of the supply chain. Consequently, achieving optimal performance necessitates a cohesive, holistic approach to management, aligning with the overarching objectives of the system. Through dynamic data analysis of multiple types of power materials, a dynamic inventory control model for power materials is constructed to achieve optimal adjustment of inventory management. Ultimately, a multi-granularity inventory control method based on big data analysis of power warehousing is constructed, which effectively improves inventory management efficiency and reduces logistics management costs for enterprises. Through big data analysis of power material warehousing, the characteristics of power material demand are excavated, and a classification method for power material demand is constructed to achieve an overall inventory control strategy for power materials. The implementation results show that the controlled inventory can better meet the changing demand, thereby improving inventory management efficiency. The multi-granularity inventory control method based on big data mining of warehousing combines inventory and multi-objective optimization theories, proves the applicability and feasibility of the proposed method, effectively improves inventory management efficiency, reduces logistics management costs for enterprises, and provides practical guidance and decision-making reference for improving the intensive management level of power production and maintenance materials.

Optimal allocation of customer energy storage based on power big data and improved LSTM load forecasting

This research explores the potential of energy storage investment with a focus on regional power users. An incentive-based demand response framework is constructed, emphasizing the aggregation of demand-side resources for active participation in the power market. Utilizing a data-driven approach, the improved Long Short Term Memory (LSTM) model is employed to predict customer behavior in response to incentives. The primary objective is to maximize the life cycle benefit while minimizing the payback period for users investing in energy storage. By harnessing big data analytics, suitable users for energy storage investment are identified and optimal capacity allocation is determined. Given the current energy storage parameters and dynamics of electricity pricing, boundary values for profitable user energy storage capacities are derived. This methodology filters users with capacities exceeding these thresholds, marking them as potential candidates for energy storage deployment. Furthermore, a measurement model for iron phosphate batteries is introduced. Analysis of adjustable resource capacity, duration, and benefits for potential users provides insights into optimal energy storage investment strategies. Integrating configured energy storage batteries with time-of-use tariffs substantially reduces energy storage costs, leading to enhanced economic efficiency.

Machine Learning Empowering Drug Discovery: Applications, Opportunities and Challenges.

Drug discovery plays a critical role in advancing human health by developing new medications and treatments to combat diseases. How to accelerate the pace and reduce the costs of new drug discovery has long been a key concern for the pharmaceutical industry. Fortunately, by leveraging advanced algorithms, computational power and biological big data, artificial intelligence (AI) technology, especially machine learning (ML), holds the promise of making the hunt for new drugs more efficient. Recently, the Transformer-based models that have achieved revolutionary breakthroughs in natural language processing have sparked a new era of their applications in drug discovery. Herein, we introduce the latest applications of ML in drug discovery, highlight the potential of advanced Transformer-based ML models, and discuss the future prospects and challenges in the field.

Big Data Anomaly Prediction Algorithm of Smart City Power Internet of Things Based on Parallel Random Forest

Abstract Conventional power data prediction algorithms easily lead to the loss of key power data in a complex wireless network environment. Therefore, a power big data anomaly prediction algorithm based on parallel random forest is proposed. According to the power big data anomaly prediction algorithm based on parallel random forest, a network power big data anomaly prediction algorithm platform is established, and based on the platform, key data features such as user address and power transmission packet structure are extracted according to the category of power users. According to the relationship between power shunt function value and power data unit density, the parameter value of the system and finally the reasonable anomaly prediction of power big data in wireless network are determined. Finally, filter the classified data through attribute reduction and gene expression programming algorithm to obtain the data to be encrypted and complete the research on the anomaly prediction algorithm of power big data. Experimental results show that the proposed algorithm has better prediction performance and can ensure better data prediction effect.

Analysis and Application of Big Data Feature Extraction Based on Improved k-means Algorithm

This paper addresses the challenges modelled by collecting and storing large volumes of big data, focusing on mitigating data errors. The primary goal is to propose and evaluate an enhanced K-means algorithm for big data applications. This research also aims to design an extensive energy data system to demonstrate the improved algorithm's practical utility in monitoring power equipment. The research begins with an in-depth analysis of the traditional K-means algorithm, culminating in the proposal of an improved version. Subsequently, the study outlines developing a comprehensive, extensive energy data system, encompassing architectural aspects such as data storage, mechanical layers, and data access structures. The research also involves the development of a power big data analysis platform, incorporating the improved algorithm for clustering and analyzing power equipment monitoring data. Experimental results reveal that the proposed improved K-means algorithm outperforms the traditional version, with significantly improved accuracy and reduced classification errors, achieving an error rate of less than one. The improved K-means algorithm showcased remarkable enhancements, achieving a meagre misclassification rate of just 0.08% while substantially boosting accuracy levels, consistently exceeding 95% across all datasets. Moreover, the power big data system developed in this study to meet practical requirements while enhancing storage and processing efficiency effectively.

Research on operation fault diagnosis algorithm of power grid equipment based on power big data

Research on operation fault diagnosis algorithm of power grid equipment based on power big data

Credit ability evaluation system of power selling enterprises based on big data

With the gradual relaxation of the electricity market access threshold, the development pattern of market-oriented electricity sales has brought great challenges to the power grid business development. It is of great significance to select the right electricity selling enterprises in the market-oriented electric power marketing mode to ensure the practical demand of electricity customers and the operating efficiency of power grid enterprises. First, the basic characteristics of power big data are analyzed. Secondly, it classifies and analyzes the important factors that affect the performance credit of electricity selling enterprises under the new electricity transformation environment. By normalizing different types of key index data and assigning index weights, a credit ability evaluation model of electricity selling enterprises based on matter-element extension-TOPSIS is established. Finally, taking eight electricity selling enterprises in a province as an example, the credit ability evaluation model is applied to analyze and calculate the performance ability of different electricity selling enterprises. The results prove the practicability of the evaluation system proposed in this paper.

Design and Implementation of Power Big Data Platform

In recent years, with the advancement of the national construction of smart grid and the development of power grid enterprise integration system, the traditional power data platform has some defect, such as insufficient scalability, repeated implementation of two sets of logic of off-line data warehouse and real-time data warehouse, highly correlated storage of data warehouse during computation, and difficulty in platform migration, which restrict the deeper mining and analysis of power big data. Therefore, this paper adopts a technical architecture incorporating distributed calculating, micro-service, flow-batch integration and lake-warehouse integration, combining the current popular Web front-end and back-end technologies such as Vue, SpringCloud and Flask, and utilizing big data components such as Hadoop, Flink, Hudi and Kafka as well as Docker containerization technology. We explore and build a power grid system operation oriented big data platform that assembles real-time computing, multi-source heterogeneous data storage, and develop APIs for authority management, information management, data analysis and visualization, machine learning and other data supporting services that assist power enterprises in managing electrical equipment and users’ power consumption.With the help of this platform, power enterprises can intelligently track the equipment status, view the offline and real-time visual charts to analyze the power consumption by users, so as to have better basis in the power dispatching, maintenance and scheme adjustment of electrical market.

High frequency measurement of carbon emissions based on power big data: A case study of Chinese Qinghai province

In this paper, we build the electricity‑carbon model with techniques of frequency transformation, and statistical modeling. With macro statistical data released by Statistics Bureau of Qinghai Province and high-frequency power big data provided by State Grid Qinghai Electric Power Company, based on the electricity‑carbon model we apply the inventory method to measure the monthly carbon emissions of Qinghai Province and its prefectural-level cities, as well as its industry, construction, and other five industries. Additionally, we apply the same method to measure the emission reduction contribution of green power by using the data of proportion of “green power”. The results show that first there is a “double imbalanced” phenomenon for the distribution of Qinghai's carbon emissions, which means that the distribution of carbon emissions of Qinghai's prefectural-level cities is imbalanced and the distribution of carbon emissions of Qinghai's industries is also imbalanced. Second, the emission reduction effect of “green power” is significant. And the quantity of its reduction accounts for 54 % of Qinghai's total emission. Third, in comparison with the seven institutions' relative error rate which is about 7 % for measuring China's carbon emissions, our results are reliable.

The multi-dimensional power big data mining based on improved grey clustering algorithm

In order to overcome the problems of the traditional power big data mining methods, such as the low integrity of data mining and the long time-consuming of data mining, this paper realizes multi-dimensional power big data mining by improving the grey clustering algorithm. Firstly, a relay multi hop network is established to collect power big data through the collector; Secondly, Lagrange interpolation method is used to fill the missing data of power data mining; Standardized processing of power consumption data; Finally, according to the grey theory and FCM clustering algorithm, the multi-dimensional power big data mining is realized. The experimental results show that the integrity of power big data mining in this method is up to 0.996, the mining time is not more than 3.05 s, and the mining integrity is up to 0.992, which indicates that this method can effectively improve the effect of power big data mining.

Power user portrait model based on random forest

AbstractWith the vigorous development of the energy Internet, all kinds of user information data are increasing day by day. How to comprehensively and deeply mine the effective information of users, develop a model to predict the behaviour characteristics of big data users, distinguish customer relationships, and provide an accurate basis for the next behaviour of users for various platforms have become one of the research hotspots of big data analysis of user behaviour. The data is sampled according to the feature vector of power user. The portrait mining of power user is conducted, and the user screening and analysis are conducted by using the measure of decision tree node purity in the model. The decision tree variable of the up–down stopping rule is generated. Then the results of the model and the Logistics model are tested and analysed, which can effectively predict the behaviour of power user. The proposed user strategy based on the characteristics of power consumption behaviour is analysed to verify the effectiveness of the scheme. The example shows that the model has a strong ability to distinguish and good stability than the traditional Logistics model, which can effectively predict the user's behaviour in advance, reduce user complaints, and help enterprises and users to form a long‐term mechanism of mutual benefit and reciprocity, which has a strong practical significance. This paper analyses the panorama of users through power big data technology and proposes a maturity model to evaluate the priority of users' electricity consumption. It emphasises the use of resources and methods provided in the power big data technology package to solve the practical problems of users' electricity consumption, and helps power companies to avoid market risks and improve service levels, which has strong practical significance.

Research on the Fusion of Power Big Data Technology and Power System Simulation Calculation

Power big data is the practice of big data concepts, methods, and technologies in the power industry. It is composed of structured data and unstructured data. It mainly involves power generation, transmission, transformation, and distribution. Power big data can reflect the macro-economy in real time, promote economic and social development, and improve the internal management level and economic benefits of the power industry enterprises. The integration of power big data technology and power system simulation calculation can update system analysis in real-time and provide new perspectives and methods for system analysis.

Power Big Data Analysis Platform Design Based on Hadoop

To better analyze and process power data to obtain effective information, a power big data analysis scheme based on Hadoop architecture is proposed. We analyze the cloud computing environment Hadoop distributed platform to obtain massive data of large-scale distributed power system. According to the characteristics of intelligent power consumption data, common data mining algorithm modules such as parallel classification algorithm and parallel real-time clustering algorithm are designed, and the implementation of clustering algorithm with different principles is further analyzed. Then, HBase is adopted to access data in a distributed way, and MapReduce is used to realize the visual management of power GIS data. The experimental results show that the parallel processing method of power big data based on Hadoop has high efficiency and good scalability, and the algorithm has good identification ability for massive data in the cluster mode.