Power Consumption Information Research Articles

Design and implementation of terminal power information collection and control system based on low-code platform

Abstract As the number of power consumption collection terminals continues to increase, the business needs for remote signaling and telemetry data collection through interface services continue to expand. Different professional collection front-end openness levels and open standards are not unified, and each professional terminal can only be managed in a decentralized manner. As a result, the current power consumption information data collection capabilities are scattered. In general, active data collection and sensing in enterprises must rely on various collection services to carry out applications. The lack of unified operation and management mechanisms and tools leads to difficulties in the development of interfaces and the management of terminal equipment. A set of power consumption information collection service integration systems developed based on a low-code platform is proposed. In view of the different communication protocols of power terminals and the need to develop different interfaces, etc., the command issuance and message encapsulation of different power collection protocols is carried out, to provide customized collection services and improve unified access and management capabilities of terminals.

Application Research of Power Measurement Data Exchange Based on Uninterruptible Power Supply

With the maturity of the smart meter embedded system integration mode, the power consumption information can be fed back to the management center by the intelligent terminal through the power Internet of Things. The smart terminal adopts the narrowband data communication mode. However, the narrowband channel interval is hindered by interference signals, resulting in serious data packet loss. Therefore, a stable and efficient acquisition scheme is needed to ensure its smooth interaction. Based on the application of smart meter data communication, we analyze the power consumption optimization and carrier transmission optimization of the power Internet of Things and propose a data analysis method based on self-learning and external characteristics, which is used to calculate the offline execution strategy of smart meters when they encounter factors such as power outage or intrusion. A production task-driving rule is designed. The external characteristics of deep learning data of the power Internet of Things are collected. The accuracy of data classification is judged according to the parallel Internet of Things of a convolutional neural network and a long-term and short-term memory deep neural network. When dealing with high-dimensional original data, the data network is graded to reduce the work of feature selection and the difficulty of training. Moreover, we improve the autonomous execution ability of an embedded terminal device in an offline state. We also strengthen the ability of abnormal data detection and judgment learning to meet the requirements of efficient and stable operation of the power grid. Simulation results verify that compared with the current mainstream schemes, the performance of the proposed scheme is improved by more than 17%. Besides, the fault tolerance rate is enhanced by 29%, and the power consumption after optimization is reduced by 26.3% and 42.8%. These effects highlight the role of data processing in embedded systems. It solves the problem of abnormal data and energy loss affecting the accuracy of error estimation of electric energy meters, opens up a new way for data acquisition and processing of large-scale smart meters, and further improves the service level of information management of power systems.

LSTM Networks for Home Energy Efficiency

This study aims to develop and evaluate an LSTM neural network for predicting household energy consumption. To conduct the experiment, a testbed was created consisting of five common appliances, namely, a TV, air conditioner, fan, computer, and lamp, each connected to individual smart meters within a Home Energy Management System (HEMS). Additionally, a meter was installed on the distribution board to measure total consumption. Real-time data were collected at 15-min intervals for 30 days in a residence that represented urban energy consumption in Sincelejo, Sucre, inhabited by four people. This setup enabled the capture of detailed and specific energy consumption data, facilitating data analysis and validating the system before large-scale implementation. Using the detailed power consumption information of these devices, an LSTM model was trained to identify temporal connections in power usage. Proper data preparation, including normalisation and feature selection, was essential for the success of the model. The results showed that the LSTM model was effective in predicting energy consumption, achieving a mean squared error (MSE) of 0.0169. This study emphasises the importance of continued research on preferred predictive models and identifies areas for future research, such as the integration of additional contextual data and the development of practical applications for residential energy management. Additionally, it demonstrates the potential of LSTM models in smart-home energy management and serves as a solid foundation for future research in this field.

Semi-supervised learning with flexible threshold for non-intrusive load monitoring

Non-intrusive load monitoring (NILM) can obtain fine-grained power consumption information for individual appliances within the user without installing additional hardware sensors. With the rapid development of the deep learning model, many methods have been utilized to address NILM problems and have achieved enhanced appliance identification performance. However, supervised learning models require a substantial volume of annotated data to function effectively, which is time-consuming, laborious, and difficult to implement in real scenarios. In this paper, we propose a novel semi-supervised learning method that combines consistency regularization and pseudo-labels to help identification of appliances with limited labeled data and an abundance of unlabeled data. In addition, given the different learning difficulties of various appliance categories, for example, feature learning is more difficult for multi-state appliances than two-state appliances, the thresholds employed for different appliances are adjusted in a flexible way at each time step so that the informative unlabeled data and their pseudo-labels can be delivered. Experiments have been conducted on publicly available datasets, and the results indicate that the proposed method attains superior appliance identification performance compared to cutting-edge methods.

Design of short-term load forecasting method considering user behavior

In order to ensure optimize the short-term power load forecasting effect, load forecasting method design taking into account the user behavior is studied. Mining user power consumption behavior data from power user power consumption information acquisition system, and suppressing power consumption behavior data noise through empirical mode decomposition algorithm; Input the denoised user behavior data into the improved Relief algorithm, extract the user power consumption behavior characteristics, eliminate redundant features through correlation analysis, and cluster the user behavior characteristics using K-means clustering algorithm to reduce the dimension of the feature data and the complexity of the features, then input the clustered features into the online extreme learning machine (OS-ELM), load forecasting is realized through online learning. Experiments show that this method can accurately predict the power consumption of each type of users in different time periods, and its prediction results have a high interval coverage.

Design and Implementation of an SGX Based Electricity Information Collection and Management System

With the rapid growth of the number and scale of smart grid users, traditional data encryption transmission methods can no longer meet the performance requirements of data aggregation. In response, a power consumption information collection and management system based on SGX software protection extension is proposed. The system mainly consists of three parts: user electricity data acquisition terminal, SGX data security processing and distributed storage module on the chain, and data monitoring management display platform. The user electricity data collection terminal collects electricity data from various buildings, residences, rooms, and other smart meters, analyzes and uploads it. After calling the trusted function of SGX technology, it enters the security zone provided by SGX for data processing. Finally, the data security processing results and data are uploaded to the blockchain for storage. In order to visually display user electricity usage data, an intelligent monitoring platform for user electricity collection and management has been established. This system can reduce the workload of user electricity data collection, ensure the accuracy of data collection, and provide an efficient and highly reliable system platform for user electricity data management.

Novel AMI in Zigbee Satellite Network Based on Heterogeneous Wireless Sensor Network for Global Machine-to-Machine Connectivity

This study endeavored to enhance the efficiency and utility of microcomputer meters. In the past, their role was predominantly confined to remote meter reading, entailing high construction and communication transmission costs, coupled with subsequent maintenance and operational expenditures. These factors collectively impacted the enthusiasm of various stakeholders to invest in this realm. Hence, in alignment with the smart city development initiative, the natural gas industry has pioneered the establishment of an advanced metering infrastructure with heterogeneous wireless sensor networks (HWSNs) at its core. This visionary leap incorporates global machine-to-machine connectivity (G-M2MC) technology, interconnecting all facets of its operations, thereby positioning itself as a trailblazer within the industry. While advancing this endeavor, the project’s scheduling aligns with the enterprise’s sustainability goals in the early stages of digital transformation. This strategic allocation of resources is responsive to government policies and aspires to cultivate a digitally connected smart green energy hub, thereby expediting the transformation of the living environment. The objective is to provide a stable, secure, cost-effective, and reliable system that can be shared among peers. Furthermore, this study delved into the analysis of congestion avoidance in intelligent Zigbee satellite transport networks based on the HWSNs-GM2MC of non-synchronous satellite orbit system (NGSO) pivotal technologies, utilizing them to integrate the smart LNGas management system (SGMS). Concurrently, it developed application services through the smart meter application interface (SMAPI), distinct from conventional microcomputer meters. However, it is imperative to acknowledge that cloud computing, while processing sensitive data, grapples with issues of latency, privacy, efficiency, power consumption, and zero-trust security risk information management and ethical authority management capabilities in the defense of disaster relief responses.

Detection of False-Reading in Smart Grid Net-Metering System

Malicious consumers may hack their smart meters (SMs) in the smart grid to report fake readings in order to make unlawful financial profits. Since the reported readings are used for energy management, the utility suffers significant financial losses as a result, and grid performance may suffer as a result. The net metering system, in which one SM is used to report the difference between the power consumed and the power generated, is the subject of this research, which is the first study to look into the issue. First, we process real power consumption and generation information to create a benign dataset for the net-metering system. The data was then examined, and time correlations between the net meter readings and correlations between the measurements and pertinent information from reliable sources, such as temperature, were discovered. Based on the data analysis, we suggest a single-pole double-throw detector to detect erroneous readings. In addition to data from reliable sources, our detector is trained on net meter readings from every customer in order to improve its performance by discovering correlations between them. We also create a relationship between the customer and the grid. Since the only billing system available nowadays is online, this project includes an app that may be used to solve a variety of problems, including electrical outages. Overall, with this, we improve customer and grid communication and simplify their lives.

Deep learning-based optimization method for detecting data anomalies in power usage detection devices

Abstract In this paper, the self-attention layer of a graph convolutional neural network is first constructed to output the important information in the network structure. The migration learning network model is established, and the sample data are preprocessed and trained sequentially. The final processing results are used as the initial data for abnormal power consumption detection. Introduce Bayes’ theorem to optimize the hyperparameters of the model. The optimized model is applied in the abnormal power consumption detection system to identify abnormal power consumption events and provide specific processing solutions. Through the detection of the system, it was found that the voltage of the test user dropped from a 100V cliff to about 20V in late November, which was determined by the system to be a power consumption abnormality, and, therefore, an operation and maintenance order was issued. The site survey revealed that the data was in line with the system detection. Calculating the power consumption information of another user, the phase voltage of this user stays around 85-100V, far below 150V, so the undercounting of power is verified for the user, and the amount of power that should be recovered is 201.22kW.

中国电力企业远程用电检查技术的应用现状及对策

With the rapid development of the power industry, remote power inspection technology has been widely used in the power industry. The application of remote power inspection technology is of great significance to the development of the power industry, so the power enterprises should strengthen the research and development and application of this technology to improve the safety and stability of the power system. Firstly, this paper briefly analyzes and expounds the technology and current situation of remote power consumption, and summarizes a series of problems existing in remote power consumption inspection, including low transmission efficiency of power consumption information caused by differences in communication equipment and technology, inaccurate power consumption inspection caused by the ability and quality of operators in practical operation, and inaccurate inspection caused by imperfect power consumption inspection equipment system. Finally, through the analysis of the above problems, the author puts forward the relevant problem-solving practice strategy. Electric power enterprises should strengthen the construction of information infrastructure, improve the efficiency of electricity information transmission, timely update the electricity inspection system, encourage the innovation and practical operation of the electricity system, and provide a more perfect development idea for the development of China’s electric power industry.

Benefit evaluation analysis of new energy power consumption information collection with basic steady-state harmonic characteristics

Abstract In this paper, firstly, the load monitoring characteristics of new energy power consumption are studied and analyzed in terms of both steady-state and transient characteristics, and suitable characteristic values are selected for different types as the basis of load decomposition. A comparative analysis of various types of load imprints is conducted to highlight the advantages of steady-state harmonic features as load monitoring feature data. Then, an evaluation model of the demand response potential of electricity information collection is established based on a multi-objective optimization algorithm, and technical and economic analysis of electricity information collection is carried out to evaluate the response potential of power companies collecting customer data. Finally, the situation and benefits of collecting electricity consumption information for new energy power are analyzed. The daily maximum load moments of typical users are all cut by more than 10%, the peak load portion cut by more than 97% is also more obvious, and the maximum moments of valley filling response are all more than 11%. D Power saved a total of 3.759 billion yuan in labor costs by reducing labor costs by 400 million yuan after adopting electricity consumption information collection. The indices of excellent and qualified management benefit and social benefit evaluation of electricity consumption information collection are above 0.8. Overall, the benefits of electricity consumption information collection for new energy power with basic steady-state harmonic characteristics are significant and effective.

RSSI-adaptive local dual-mode communication for the Internet of Things in power distribution

Abstract The Internet of Things (IoT)’s rapid development is placing new demands on communication technology for power distribution. This paper proposes the application scheme of RSSI adaptive and dual-mode communication technology in the power distribution area, realizes the dual-mode networking technology adopted to improve communication quality, and verifies the reliability and superiority of the dual-mode communication technology scheme through analysis and application. The RSSI adaptive dual-mode communication module is designed, the RSSI model algorithm is modified, and the logical topology of the communication network and the data processing technology based on compressed sensing are analyzed. We focus on analyzing the performance of dual-mode communication networks for power consumption information collection and the information reception rate and real-time performance of MQTT protocol mapping for power distribution IoT. The average transmission delay was tested to be 198.5ms for QoS=0, 478.0ms for QoS=1, and 786.3ms for QoS=2 without encryption. When using SASL/TLS for transmission encryption, the average transmission delay is 992.7ms for QoS=0, 1203.6ms for QoS=1, and 1461.1ms for QoS=2.

Adaptive Load Balancing for Dual-Mode Communication Networks in the Power Internet of Things

As an important part of the power Internet of Things, the dual-mode communication network that combines the high-speed power line carrier (HPLC) mode and high-speed radio frequency (HRF) mode is one of the hot directions in current research. Since non-uniform transmission demands for power consumption information can lead to link congestion among nodes, improving the network load-balancing performance becomes a critical issue. Therefore, this paper proposes a load-balancing routing algorithm for dual-mode communication networks, which is achieved in dual-mode communication networks by adding alternate paths and proxy coordinator (PCO) node election mechanism. Simulation results show that the proposed algorithm achieves the load-balanced distribution of power consumption information transmission. The proposed scheme reduces the delay and packet loss rate, as well as improving the throughput of dual-mode communication compared to existing routing algorithms.

MCPAP: A MSIS-based conditional privacy-preserving authentication protocol for smart grids

The smart grid is considered a promising second-generation power grid because electricity service companies can use it to store energy more securely and deliver energy more efficiently. To protect the reliability of power consumption information and the privacy of users’ identity information in a smart grid, many conditional privacy-preserving authentication protocols have been proposed in the last decade. However, these protocols are vulnerable to attacks from the quantum computer because of the large integer factorization problem or the discrete logarithm problem embedded in these protocols. To solve this problem, a security-enhanced conditional privacy-preserving authentication protocol is designed in this paper by using the module small integer solution problem. Besides, we show that our protocol has superior security in the random oracle model. The potential utility of this protocol in the smart grid is also demonstrated through our detailed performance analysis. Moreover, our protocol can use the batch verification method to improve its efficiency significantly.

Demand Side Control for Energy Saving in Renewable Energy Resources Using Deep Learning Optimization

Convolutional neural networks a type of deep learning technology, are used for forecasting future power usage. The mean absolute error, mean square error, root mean square error, and mean constant percentage error are used to evaluate the performance of the models. These metrics are used to rank the models. Among the models tested, the CNN stack demonstrates the highest precision in estimating energy consumption and solar power output, with a mean absolute error of 0.015% points, a root mean square error of 0.23% points, and an average absolute percentage deviation of 1.71% points. However, for wind turbine (WT) energy generation, the recurrent neural network proves for most accurate model, achieving 0.070 as the median absolute percentage error is 2.65, Both the root of the mean square error and the average absolute error are 0.38. The training and validation data utilized in this study comprise the International Renewable Energy Agency’s data on solar power production, WT power generation, and the “ensue” dataset, which includes hourly power consumption information from the Pennsylvania, New Jersey, and Maryland interconnection.

Research on Intelligent Terminal Based on Distribution Multi-service Integration under The Dual-carbon Target

The distribution network is used to distribute electric energy in the power system, which can reasonably allocate resources according to the power demand of different users. Intelligent terminals for distribution multi-service integration mainly include a distribution switch monitoring module, distribution transformer monitoring module, concentrator, gateway, and main controller. Firstly, the current, voltage, and power of electricity are collected by setting monitoring points in the distribution network and installing current transformers, voltage transformers, and energy meters. The collected data are processed by the distribution transformer monitoring module to improve the effectiveness of the data. Finally, the processed data are sent remotely through the main controller and gateway. By designing a four-in-one intelligent terminal device for distribution multi-service integration, it can monitor power equipment and collect power consumption information of different users, further excavating the potential value of power data, which has practical significance for the improvement of energy utilization efficiency.

Profiling side-channel attacks based on CNN model fusion

The deep learning algorithm analyzes the power consumption, electromagnetic, and other side-channel information leaked by hardware devices, which has powerful attack capability. However, there is also the problem of high difficulty in hyperparameter tuning. To this end, we propose a side-channel attack scheme using convolutional neural network (CNN) model fusion. First, N sets of hyperparameters were randomly selected in the search space, and the CNN model was trained using the side-channel dataset to obtain N base models. Secondly, the high-dimensional features in the middle layer of the base model were merged to increase the amount of effective information and enhance the generalization ability of the neural network model, and a new fusion model was constructed by training. Finally, the fusion model was used to predict the output probability of DPA Contest v4, ASCAD, AES_HD, and AES_RD public side-channel datasets, analyze the guessing entropy of each key to recover the key, and evaluate the attack efficiency of the scheme. The experimental results show that the CNN model fusion method can effectively improve the side-channel attack capability and reduce the difficulty of hyperparameter tuning.

Non-intrusive load monitoring through coupling sequence matrix reconstruction and cross stage partial network

Non-intrusive load monitoring can obtain the operation state of single device from the total power sequence. To achieve high-precision non-intrusive load monitoring, this study proposes a novel method by coupling sequence matrix reconstruction and cross stage partial network. The timing variation information hidden in the total power sequence is derived through sequence matrix transformation and matrix dimension rising, forming a three-dimensional matrix. Then, this matrix is sent into a cross stage partial network to estimate the device power consumption. Especially, the established cross stage partial network adopts a feature concatenation strategy, greatly reducing the network parameters. Unlike traditional neural network methods, the proposed method makes full utilization of the timing variation information of total power consumption. Experiments are carried out on the load monitoring platform to verify the method feasibility. Results show that the proposed method can non-intrusively decompose the device power consumption, with strong robustness and generalization ability.

Application of attention mechanism enhanced neural network in non-invasive load monitoring of industrial power data

The non-intrusive appliance load monitoring (NILM) decomposes the total power consumption of a power system into its contributing appliances. Previous studies only considered using the total power consumption information of appliances to decompose the load consumption. Besides the total electricity consumption, there is also important information such as current, voltage, and time in the total electricity consumption data, which can be used to analyze the load consumption information. Therefore, we proposed a sequence-to-sequence network enhanced by an attention mechanism, which effectively integrated the external features besides the total electricity consumption in grid data. Finally, we applied and evaluated the proposed model on the electricity consumption data of a gas station with 12 appliances, and our model achieved a 90.5% accuracy in load decomposition. Our solution provides a new solution on the application of NILM in the industrial field and helps to manage energy more rationally.

Emerging Memtransistors for Neuromorphic System Applications: A Review.

The von Neumann architecture with separate memory and processing presents a serious challenge in terms of device integration, power consumption, and real-time information processing. Inspired by the human brain that has highly parallel computing and adaptive learning capabilities, memtransistors are proposed to be developed in order to meet the requirement of artificial intelligence, which can continuously sense the objects, store and process the complex signal, and demonstrate an "all-in-one" low power array. The channel materials of memtransistors include a range of materials, such as two-dimensional (2D) materials, graphene, black phosphorus (BP), carbon nanotubes (CNT), and indium gallium zinc oxide (IGZO). Ferroelectric materials such as P(VDF-TrFE), chalcogenide (PZT), HfxZr1-xO2(HZO), In2Se3, and the electrolyte ion are used as the gate dielectric to mediate artificial synapses. In this review, emergent technology using memtransistors with different materials, diverse device fabrications to improve the integrated storage, and the calculation performance are demonstrated. The different neuromorphic behaviors and the corresponding mechanisms in various materials including organic materials and semiconductor materials are analyzed. Finally, the current challenges and future perspectives for the development of memtransistors in neuromorphic system applications are presented.