Real-time Electricity Consumption Research Articles

A federated and transfer learning based approach for households load forecasting

The escalating global population and increased smart-devices demand have made electricity load estimation a significant challenge for relevant authorities. Even with advancements in Artificial Intelligence, pinpointing both immediate and long-term electricity requirements remain a challenging and complex task. Predicting electricity usage at the household level can offer insights into this trend, aiding in the development of efficient load management strategies. However, creating individual predictive models for every household is a resource-intensive task. The current research proposes a streamlined approach that merges clustering analysis with federated and transfer learning to craft household-level predictive models. Firstly, clustering analysis is employed to discern electricity usage patterns, forming cluster-specific datasets. Subsequently, a federated learning model is introduced to each identified cluster, ensuring that the foundational model is finely calibrated to the unique consumption patterns inherent to each group. To evaluate the effectiveness of our approach, we employed Root Mean Square Error and Mean Absolute Error as evaluation metrics. During the evaluation phase, the transfer learning approach is employed, fine-tuning our root model on 70% of the data and testing its performance on the remaining 30%. The robustness of the proposed methodology is validated using a comprehensive dataset detailing electricity usage across Australian households. When bench-marked against prevailing methodologies, our proposed model consistently demonstrates either closer or superior predictive accuracy, emphasizing its potential as a formidable tool in real-time electricity consumption forecasting.

A multi-step electricity prediction model for residential buildings based on ensemble Empirical Mode Decomposition technique

Residential electricity demand is increasing rapidly, constituting about a quarter of total energy consumption. Electricity demand prediction is one of the sustainable solutions to improve energy efficiency in real-world scenarios. The non-linear and non-stationary consumption patterns in residential buildings make electricity prediction more challenging. This paper proposes a multi-step prediction approach that first conducts cluster analysis to identify seasonal consumption patterns. Secondly, an improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method and autoencoder model has been deployed to remove irregular patterns, noise, and redundancy from electricity load time series. Finally, the Long Short-Term Memory (LSTM) model has been trained to predict electricity consumption by considering historical, seasonal, and temporal data dependencies. Further, experimental analysis has been conducted on real-time electricity consumption datasets of residential buildings. The comparative results reveal that the proposed multi-step model outperformed the existing state-of-the-art RF-LSTM-based prediction model and attained higher accuracy.

TEKSTİL ENDÜSTRİSİNDE DERİN ÖĞRENME KULLANARAK AŞIRI ELEKTRİK TÜKETİMİNİN ÖNLENMESİNE YÖNELİK BİR VAKA ÇALIŞMASI

Resources are the most critical input in the manufacturing industry therefore, resource consumption is an essential issue to be minimized. On the other hand, consumption depends on several parameters thus, it is difficult to estimate. Recently, Machine Learning (ML) and Deep Learning (DL) are powerful Artificial Intelligence (AI) subdomains for future prediction in any area. In this paper, a DL-supported electricity prediction method is designed for the textile industry as a case study in order to prevent resource over-consumption while the machines are in the standby state. This method provides dynamic consumption thresholds of electricity consumption by sliding window technique based Long-Short Term Memory (LSTM) model that helps the machines to interrupt manufacturing in their decision. These calculated thresholds are also compared with the results of Recurrent Neural Networks (RNN) and Gated Recurrent Units (GRU) as the other DL methods and Automated Regressive Integrated Moving Average (ARIMA) as a traditional method and then the results have been analyzed how close they are to real-time electricity consumption data at standby. According to the results, the LSTM model successfully predicts electricity consumption levels, sends an interrupt signal to Programmable Logic Controller (PLC) unit when the consumption levels reach the threshold and therefore prevents resource over-consumption.

Soil moisture forecasting for precision irrigation management using real-time electricity consumption records

The installation of the smart electricity meters (SEMs) to the high-density pumping wells in the North China Plain (NCP) provided a method to control the groundwater withdraw. The record in real-time electricity consumption from SEMs also provided an alternative method to forecast soil moisture to make irrigation decisions to improve irrigation efficiency. The irrigation timing and the irrigation amount could be obtained on-line from the SEMs based on the coefficients obtained for electricity-to-water conversion (E-Wc). The real-time recordings in the irrigation timing and amounts were further used to forecast the next irrigation timing and amount based on the water rights per area of land, the lower limit of soil water contents for different crops at their different growing stages and online meteorological data. A smartphone app was developed based on this framework. The running of the app to simulate soil water contents based on the soil water balance equation was supported by databases on soil texture, crop coefficients, water rights, E-Wc and threshold soil water content values as well as the real-time collection of electricity consumption and meteorological data. The simulated real-time soil water contents were shown on the mobile phone screen and could be compared with the threshold values for making irrigation decisions for users. The accuracy in forecasting the soil water contents using the methods developed in this framework was tested under different irrigation schedules for winter wheat and summer maize during 2018–2019 and 2021–2022, and the app was tested for three locations starting from sowing winter wheat in October 2022 to April in 2023. The results showed that the app could be easily run, and precise real-time soil water contents were simulated. With the application of the pumping well renovation project in the NCP, most of the pumping wells were installed with the SEMs, and using SEMs for groundwater pumping control and irrigation decisions should be possible.

Anti-interference Technology of Multi-type Data Acquisition and Transmission Based on HPLC

With the development of the social economy, electricity consumption increases rapidly. A large amount of real-time electricity consumption data plays an important role in analyzing the power grid business. Monitoring power consumption performance through electricity consumption data is also an important indicator to improve power grid performance. By analyzing the characteristics of HPLC channels, such as capacitance characteristics, frequency loss characteristics, signal-to-noise ratio characteristics, line icing, and noise interference, the critical values of data transmission distortion caused by the characteristics of each channel were summarized. On this basis, it is concluded that suitable filters and amplifiers should be selected to improve the signal-to-noise ratio and the transmission quality of HPLC signals. Therefore, an anti-interference technology for data transmission based on HPLC is proposed. Finally, the application advantages and existing problems of HPLC technology in three-stream integration data acquisition and transmission are further explored.

P2P trading mode for real-time coupled electricity and carbon markets based on a new indicator green energy

The carbon trading market has introduced new opportunities for the low-carbon transformation of China's power industry. To realize real-time coupling of the electric–carbon market at the mechanism level, this study constructs a P2P trading mode that improves economic and environmental benefits through innovative mechanisms. First, we propose a novel indicator, Green Energy, which has multidimensional composite attributes and accurately characterizes the supply of clean and low-carbon energy. Green Energy is used as a matchmaking indicator in the P2P electricity market to increase the rate of renewable energy consumption and encourage emission reduction of fossil energy suppliers. Second, the carbon price is dynamically adjusted according to real-time electricity consumption information and it is transmitted to the electricity market through Green Energy. Third, blockchain cross-chain interoperability technology is introduced to establish a real-time data-sharing network for electricity and carbon co-trading. Moreover, a case study is conducted, and the simulation results show that the proposed method improves social welfare by over 18% compared to the traditional trading mode. And it can improve renewable energy utilization efficiency by 26.78% and reduce carbon emissions by more than 17%. This study provides a reference for the real-time coupled electricity and carbon markets.

Electric Vehicle Trip Information Inference Based on Time-Series Residential Electricity Consumption

Pure electric vehicles (EVs) become more and more popular in current automotive markets. With wide application of EVs in the market, more EVs will be charged in residential homes. An advanced metering infrastructure measures real-time electricity consumption of individual residential homes. However, the advanced metering infrastructure is exposed to attacks given the fact that its components (e.g., smart meters) are located at public places. Under this situation, electricity consumption information of an individual residential home with an EV can be easily stolen. In this paper, we propose an EV Trip Information Inference System (TIIS) to infer EV trip information (origin-destination of each driving trip in a day) of a residential home based on its real-time electricity consumption measurements in a day. To the best of our knowledge, this is the first driving privacy relevant work caused only by residential electricity consumption data. For a residential home with an EV, TIIS derives the relationship between trip numbers in a day and charging-starting time, and the relationship between total driving distance and total charging energy. To derive the EV trip information of a residential home, TIIS firstly extracts an EV charging profile from its electricity consumption data and identifies EV model type and trip numbers as well as total driving distance based on EV charging profiles. Then, TIIS infers EV trip information based on the identified information. We used electricity consumption data and EV driving data to evaluate inference performance of TIIS. The experimental results demonstrate TIIS has as high as 81% trip inference accuracy and works on residential homes with different EV model types.

Design of Realtime Electricity Consumption Monitoring System Using Campus Web

In this study, an electricity consumption monitoring system that integrated with the campus web was developed. The system was designed using the Waterfall method approach. A realtime electricity consumption monitoring has been developed and has been implemented and can be accessed. ISO 50001 is a globally accepted standard for managing energy, providing technical and corporate management strategies to improve energy efficiency, reduce costs, and enhance environmental performance. Web technology that internet media can access can facilitate the implementation of transparent monitoring of electrical energy consumption.

IOT BASED REDUCTION OF ELECTRICITY THEFT

The Internet of Things (IoT) has emerged as a promising technology to address various challenges in different domains. One of the critical challenges in the power sector is the issue of electricity theft, which results in huge revenue losses for power utilities. To avoid such issues, the proposed system monitors the real-time electricity consumption of a household or commercial building using a current sensor and sends the data to a cloud- based IoT platform, ThingSpeak, using NodeMCU and WiFi. The platform analyzes the data and identifies any unusual electricity consumption patterns, which could be indicative of electricity theft. Keywords- IOT (Internet of Things), Electricity, EnergyMeter Coil, ESP8266, ThingSpeak.

FSDA: Flexible Subset Data Aggregation for Smart Grid

Data aggregation is a practical method to protect individual privacy while using data utility, which aggregates the real-time electricity consumption data in an area into a single value in smart grids. Based on traditional data aggregation, multisubset data aggregation can obtain data distribution characteristics rather than just the sum of data. However, almost all existing multisubset aggregation schemes do not pay attention to adjusting the size of subsets according to the data distribution, which limits the data utility. In this article, a flexible subset data aggregation (FSDA) scheme based on Paillier homomorphic encryption for smart grids is proposed. The proposed FSDA scheme can dynamically adjust the size of each subset and aggregate the electricity consumption data into the corresponding subsets while protecting individual privacy. It is proved that FSDA scheme can obtain the distribution of consumption data and resist various internal attacks aiming to expose the individual or aggregated data. Experimental results show that FSDA scheme has lower overhead compared with related schemes, which is practical for resource-constrained smart meter devices.

Retracted: Real-Time Water and Electricity Consumption Monitoring Using Machine Learning Techniques

This work studies the automatic classification of water consumption patterns and electrical devices, both supervised and unsupervised. This involves training machine learning algorithms to identify normal and abnormal water consumption patterns and differentiate between different types of electrical devices. We performed an unsupervised classification of consumer water patterns in direct and indirect ways. The first is to use the raw consumption patterns obtained directly from the server. The second one corresponds to the use of the sampled consumption patterns. This classification is performed using hierarchical bottom-up classification and a self-organizing map. A probabilistic analysis of daily water consumption is performed to extract the percentage of daily consumption with the most information. It enables us to identify water consumption patterns more quickly by reducing the number of data points for each daily pattern, allowing us to recognize and classify anomalous behaviors as soon as feasible. Then, the signatures of electrical devices are classified using three ML algorithms: multilayer perceptron (MLP), k nearest neighbor (KNN), and decision tree (DT). Furthermore, assembly approaches are also studied. These are based on the OAA (One Against All) principle, which presents one class against all other classes, and the ECOC (Error-Correcting Output Codes) philosophy, which allows the classification error to be corrected. According to the bias/variance trade-off, both techniques enhanced classification by ensuring accuracy and generalization. A more in-depth analysis of the properties of the electrical devices is handled with an esemplastic approach based on gradient-boosting decision trees. The features of electrical devices can be extracted using this analysis based on the nature of their harmonic signatures

Retraction Notice: Real-Time Water and Electricity Consumption Monitoring Using Machine Learning Techniques

Retraction Notice: Real-Time Water and Electricity Consumption Monitoring Using Machine Learning Techniques

Research on Power IoT System Based City Block Air Pollutant Emission Prediction

In order to constantly improve city environmental air quality, it is necessary to accurately control the major pollutants emissions such as air fine particulate matter. By adopting the proposed iterative update framework of air pollutant emission inventory, combined with block-level real-time electricity consumption data acquired by the smart city power IoT, and utilizing station-level and hourly environmental air quality monitoring data in specific areas of Yuxi and Dali in Yunnan Province from 2020 to 2021, the iterative update of emission inventory and prediction of air pollutant emission are studied. The experimental results shows that the prediction of the monthly average major air pollutants emissions such as NO2/PM10/PM2.5 in specific neighbourhoods of the two cities mentioned above reaches the same accuracy level as using numerical simulation prediction methods, but the prediction computational power requirements are greatly reduced, making it more suitable for the application requirements of the power IoT. This study provides a new idea for improving the regulatory capacity of intelligent environment and achieving higher urban air quality based on the smart city power IoT.

High-Dimensional Energy Consumption Anomaly Detection: A Deep Learning-Based Method for Detecting Anomalies

With the increase of energy demand, energy wasteful behavior is inevitable. To reduce energy waste, it is crucial to understand users’ electricity consumption habits and detect abnormal usage behavior in a timely manner. This study proposes a high-dimensional energy consumption anomaly detection method based on deep learning. The method uses high-dimensional energy consumption related data to predict users’ electricity consumption in real time and for anomaly detection. The test results of the method on a publicly available dataset show that it can effectively detect abnormal electricity usage behavior of users. The results show that the method is useful in establishing a real-time anomaly detection system in buildings, helping building managers to identify abnormal electricity usage by users. In addition, users can also use the system to understand their electricity usage and reduce energy waste.

A Privacy-Aware Electricity Consumption Data Collection Model Based on Group Blind Signature

Blockchain gives a new method for distributed data ledgering. The smart grid obtains efficient two-way data transmission and information control. It effectively monitors and regulates the grid by collecting real-time electricity consumption data of users. However, online data collection brings privacy leakage. To solve the problem of privacy leakage in the electricity data collection in the smart grid, a privacy-aware electricity data collection model is proposed. Firstly, we propose a new group blind signature scheme by introducing the blind feature into the identity-based encryption method. Secondly, by applying the proposed group blind signature scheme to the electricity data collection process, we propose a privacy-aware electricity data collection model. The proposed model ensures the conditional anonymity and traceability of user identity and the privacy protection and unforgeability of electricity consumption data.

A Concealed Based Approach for Secure Transmission in Advanced Metering Infrastructure

The smart grid has an important subsystem known as the Advanced Metering Infrastructure (AMI), responsible for measuring customer consumption of electricity. The AMI subsystem has smart meters as one of the components and they play a vital role in enabling communication between the utility provider and consumers. Smart meters can report real-time electricity consumption readings of the consumer to the utility. Securing this communication link from attacks remain very important for the secure transmission of readings. Different methods or approaches have been developed in the past and most of the works have high computational overheads. This paper proposes a lightweight Concealed Based Security Scheme (CBSS) for secure transmission within the AMI, providing authentication, reducing the computational overheads and energy consumption during transmission. The CBSS method is compared with the AMI Data Communication Scheme (ADCS) which does not have authentication process. The network is built in the Network Simulator 2 environment, showing the communication between the nodes. Further security is provided using a simple encryption/decryption method of 2 random numbers. The contribution of this paper is the proposed lightweight method for the AMI that authenticates the transfer of data between smart meters and other components of the AMI system. The paper also contains a simulation evaluation for the chosen design parameters of the resemblance of AMI network. The simulations show an improvement of 5% in delivery ratio, 3% throughput and 4% on energy consumed when adding security to the network.

Metode Fuzzy Subtractive Clustering Dalam Pengelompokkan Penggunaan Energi Listrik Rumah Tangga

The use of electricity in household sector has increased, especially during the Covid-19 pandemic. The large number of activities carried out in home such as Work from Home, online schools, and online businesses caused difficulty to monitor the electricity consumption. The absence of electricity usage provisions affects the electricity monitoring process. Hence it takes a real time monitoring application of electricity consumption. Fuzzy subtractive clustering is an unsupervised method to form the number and center of clusters according to data conditions. This method serves to classify the household electricity users with the parameters used, is the amount of usage in rupiah and electric power. The grouping results from this method help users to monitoring electricity consumption in real time. The output describes the level of high, medium and low user electricity consumption. Based on the test results, the best Silhouette Coefficient value is 0.8322535 and three clusters are formed, with an accept ratio is 0.5, a reject ratio of 0.15, a radius of 1.7 and a squash factor of 0.5 hence a high level of use is obtained with an average value of the number of uses in IDR 655,993, power 2757 VA, medium level 240,553, 1071 VA and low level 46,479, 675 VA

A Dynamic Membership Group-Based Multiple-Data Aggregation Scheme for Smart Grid

In the smart grid, meters report their real-time electricity consumption data to a utility supplier, and the utility supplier can adjust its supply accordingly. However, adversaries can infer users' privacy behaviors based on publicly transferred real-time electricity consumption data. Data aggregation schemes protect users' privacy from being leaked. We find two major problems are unsolved: 1) meter failure problem and 2) dynamic membership problem. To solve these problems, we designed a dynamic membership group-based multiple-data aggregation scheme. First, a group-based key establishment scheme is proposed, meters are divided into groups, meters in a group build keys to encrypt their data, the meter failure problem is alleviated. If one group has broken meters, the other groups will not be affected. Second, the dynamic join, dynamic leave, and meter replacement techniques are proposed, and the dynamic membership is achieved by allowing meters to update their keys. The simulation results show a meter's computation cost and communication cost are the minima among the related works, which makes the proposed scheme more suitable for the IoT scenario. Besides, we designed a data encoding method and a data retrieve method, we designed two attacks: 1) “bilinear map pairing attack” and 2) “zero attack.”

Implementation of The Internet of Things for Monitoring The Company's Electrical Power Consumption

This research aims to design an electric panel monitoring system using the Internet of Things technology in company buildings so that consumers can monitor real-time electricity consumption. The energy consumption monitoring method that we propose uses PM2100 by implementing a real-time monitoring function of the power consumption of a 3-phase electric panel. The monitoring system implementation results show that the value is very close to measuring the digital multimeter measuring instrument. The monitoring system produces a current measurement accuracy of 97.38% with an error of 2.62%, while the 3-phase voltage measurement error is 0.616%. This system design helps companies obtain information faster to be considered data to improve efficiency in the Company.

Sequence-to-Point Learning Based on Temporal Convolutional Networks for Nonintrusive Load Monitoring

Nonintrusive load monitoring (NILM) is performed by monitoring the total electricity consumption data from the customer’s meter and decomposing it with a load decomposition algorithm to get real-time electricity consumption data for individual appliances. Nonintrusive load decomposition is a single-channel blind source separation problem, which is difficult to implement because of the inherently unidentifiable problem. Recent studies have shown that deep learning is gradually becoming the mainstream approach to solve the NILM problem owing to the availability of data, computing power, and deep network training algorithm models. Among them, the sequence-to-point (seq2point) (Zhang) load decomposition model achieves state-of-the-art prediction because it concentrates on the representation ability of the network at one point. In this article, we optimize the structure of the network model based on the seq2point model, use a temporal convolutional network (TCN) with a more flexible perceptual field of view to train the load decomposition model, and replace the traditional activation function Relu with the activation function GELU. Finally, we test the algorithm in the public dataset UK-DALE and select the mean absolute error and F-score as the main evaluation indexes of the algorithm performance. Comparing the decomposition results of the two algorithms for the same period dataset, the results show that the temporal convolutional network model has substantially improved the performance index of load decomposition while reducing the model parameters.