Power Consumption Data Research Articles

Robust Estimation of Multivariate Time Series Data Based on Reduced Rank Model

ABSTRACTMultivariate time series analysis uncovers the intricate relationships among multiple variables, which plays a vital role in areas such as policy‐making and business decision‐making. This paper employs a reduced rank regression model to investigate a robust estimation method for multivariate time series data using an penalty. The goal is to achieve rapid parameter estimation while ensuring robustness in the analysis of time series data. This study provides a detailed description of the solution process and examines the theoretical properties of the proposed method. To evaluate its effectiveness, the proposed model is compared with full‐rank regression and the multivariate regression with covariance estimation (MRCE) method through simulations, as well as an analysis of the Sceaux household electric power consumption data. The results indicate that the proposed model performs well.

A novel approach to predict buildings load based on deep learning and non-intrusive load monitoring technique, toward smart building

Short-term load forecasting is critical in managing energy production, domestic consumption, and grid integration. Short-term load forecasting is an essential tool for managing and controlling the processes required to make buildings smarter. Short-term load forecasting at this level of the network is extremely difficult due to the high uncertainty in load demand. This study presents a novel approach for predicting building loads using deep learning and non-intrusive load monitoring (NILM) techniques, aimed at enhancing smart building energy management. The methodology involves the integration of Recurrent Neural Networks (RNNs) to effectively capture temporal patterns in power consumption data, which are critical for accurate load forecasting. The model uses a feature matrix created from power signals to improve its forecast accuracy. The results show that the suggested model greatly outperforms standard models, with an overall accuracy of 97.2 %. The model's robustness is proven through comprehensive experiments, which demonstrate higher performance in short-term load forecasts. Furthermore, a comparison with existing models demonstrates the proposed approach's efficacy in recognizing and predicting load fluctuations, making it a useful tool for smart grid applications.

DMPC-based control solution for mobile robots platoon based on ZigBee communication

The integration of mobile robots into various applications, operating collaboratively in groups known as platoons, presents challenges in communication, control, and synchronization. This paper introduces a control solution for mobile robot platoons based on a DMPC algorithm and the ZigBee protocol for V2V communication. This communication standard was selected for its low power consumption, cost-effectiveness, and reliable data transmission over short distances. Furthermore, a testbed and methodology for the experimental evaluation of the proposed control solution were developed. The results demonstrate the efficacy and feasibility of the proposed control and communication solutions.

Research on Anomaly Detection Model for Power Consumption Data Based on Time-Series Reconstruction

Research on Anomaly Detection Model for Power Consumption Data Based on Time-Series Reconstruction

Analisis Efektivitas Sistem Kendali Otomatis PJU Berbasis IoT Menggunakan Mikrokontroler ESP32 dengan Metode Regresi Linier

Public street lighting (PJU) is essential infrastructure for nighttime security. This research develops an automatic PJU control system based on IoT using light and motion sensors with an ESP32 microcontroller. The system allows for PJU lamp control via a website, providing high flexibility for users. The ESP32 microcontroller is connected to a PIR sensor to detect motion. When the sensor detects movement, the intensity of the PJU lamps is increased, and when no movement is detected, the light intensity is reduced to save energy. Users can control the PJU lamps from any internet-connected device. Testing results show an increase in energy efficiency, with an average power consumption reduction of 13.77 watts and an efficiency increase of 42.67%. An R-squared value of 0.629 indicates that the model is quite good at explaining the variability in power consumption data. This system can automatically turn on street lights and be monitored in real-time, And hopes to contribute to the development of smarter and more efficient street lighting systems.

Regional Load Forecasting Scheme for Security Outsourcing Computation

Smart grids generate an immense volume of load data. When analyzed using intelligent technologies, these data can significantly improve power load management, optimize energy distribution, and support green energy conservation and emissions reduction goals. However, in the process of data utilization, a pertinent issue arises regarding potential privacy leakage concerning both regional and individual user power load data. This paper addresses the scenario of outsourcing computational tasks for regional power load forecasting in smart grids, proposing a regional-level load forecasting solution based on secure outsourcing computation. Initially, the scheme designs a secure outsourcing training protocol to carry out model training tasks while ensuring data security. This protocol guarantees that sensitive information, including but not limited to individual power consumption data, remains comprehensively safeguarded throughout the entirety of the training process, effectively mitigating any potential risks of privacy infringements. Subsequently, a secure outsourcing online prediction protocol is devised, enabling efficient execution of prediction tasks while safeguarding data privacy. This protocol ensures that predictions can be made without compromising the privacy of individual or regional power load data. Ultimately, experimental analysis demonstrates that the proposed scheme meets the requirements of privacy, accuracy, and timeliness for outsourcing computational tasks of load forecasting in smart grids.

Enhancing Anomaly Detection in Critical Systems Using Household Appliance Power Consumption Data

Enhancing Anomaly Detection in Critical Systems Using Household Appliance Power Consumption Data

HYDROSAFE: A Hybrid Deterministic-Probabilistic Model for Synthetic Appliance Profiles Generation.

Realistic appliance power consumption data are essential for developing smart home energy management systems and the foundational algorithms that analyze such data. However, publicly available datasets are scarce and time-consuming to collect. To address this, we propose HYDROSAFE, a hybrid deterministic-probabilistic model designed to generate synthetic appliance power consumption profiles. HYDROSAFE employs the Median Difference Test (MDT) for profile characterization and the Density and Dynamic Time Warping based Spatial Clustering for appliance operation modes (DDTWSC) algorithm to cluster appliance usage according to the corresponding Appliance Operation Modes (AOMs). By integrating stochastic methods, such as white noise, switch-on surge, ripples, and edge position components, the model adds variability and realism to the generated profiles. Evaluation using a normalized DTW-distance matrix shows that HYDROSAFE achieves high fidelity, with an average DTW distance of ten samples at a 1Hz sampling frequency, demonstrating its effectiveness in producing synthetic datasets that closely mimic real-world data.

A pragmatic data processing system for large resistive sensor arrays.

Large resistive sensor arrays (RSAs) show great potential in tactile perception. However, the large number of sensors can result in great hardware overhead and bring difficulties for acquiring and processing mass data timely in transient measurement applications. This paper implements a field programmable gate array (FPGA)-based data processing system for a large RSA of 96 × 96, which shows good power consumption and high-speed wireless data update. For crosstalk-free measure, the zero potential method is improved with bus switches, leading to fewer operational amplifiers required and less negative power consumption. A real-time embedded data processing system is realized by FPGA for excellent parallel processing ability. A high-speed wireless transfer scheme with automatic regulated transfer size is proposed and realized by a wireless fidelity module, which allows timely data analysis at the remote end. Moreover, fault identification of RSAs fabricated by micro-electromechanical system technology is achieved. Tests carried out on a 32 × 32 RSA show that the total power consumption is 2209 mW, including 1261 mW of processors and 948 mW of readout circuits, corresponding to 2.15 mW/pixel. The total negative power consumption of 549 mW has been reduced by 50% compared with the zero potential method. The scanning speed is 400fps, and the wireless transfer speed is up to 120fps when the transceiver and receiver are 5m apart.

Effects of cross-linked polyethylene (XLPE) conductors in medium voltage power grids and load analysis

This study discusses the problems occurring in a microgrid established with XLPE conductors. The use of XLPE in electric grids has increased, causing problems such as voltage rise that leads to breakdowns in high-voltage equipment, increased capacitive power, and harmonic distortion. The Ferranti Effect also occurs, especially in these grids with no-load or low-load situations. Power demand in such microgrids varies depending on working days, holidays, and seasons. It is important to understand such grids' behavior and dynamic power demands for the continuity and quality of energy. Firstly, the grid was modeled in the Matlab/Simulink environment, and end-of-line and beginning-of-line voltages were simulated for three different load cases and compared with the measured grid parameters. Then, artificial intelligence-supported power forecasting and analysis were carried out using three years of power consumption data to analyze the grid's behavior and determine the energy demand in any period. Results show the existence of voltage rises at the end of the line in low load cases. Artificial intelligence algorithms have successfully predicted dynamic power demands. The study results are pioneering steps for autonomous grid management (e.g., anomaly detection) and sustainable smart grid designs that can decide which devices will be fed from the grid.

Predictive Analysis of a Building’s Power Consumption Based on Digital Twin Platforms

Colleges and universities are large consumers of energy, with a huge potential for building energy efficiency, and need to reduce energy consumption to build a low-carbon, energy-saving campus. Predicting the energy consumption of campus buildings can help to accurately manage the electricity consumption of buildings and reduce the energy consumption of buildings. However, the electricity consumption of a building’s operation is affected by many factors, and it is difficult to establish a model for analysis and prediction. Therefore, in this study, the training building of the BIM education center on campus was selected as the research object, and a digital twin O&M platform was established by integrating IoT, digital twin technology (DDT), smart meter monitoring devices, and indoor environment monitoring devices. The O&M management platform can monitor real-time changes in indoor power consumption data and environmental parameters, and organize data on multiple influencing factors and power consumption. Following training, validation, and testing, the machine learning models (back propagation neural network, support vector model, and multiple linear regression model) were assessed and compared for accuracy. Following the multiple linear regression and support vector models, the backpropagation neural network model exhibited the highest accuracy. Consistent with the actual power consumption detection results in the BIM education center, the backpropagation neural network model produced results. Consequently, the BP model created in this study demonstrated its dependability and ability to forecast campus building power usage, assisting the university in organizing its energy supply and creating a campus that prioritizes conservation.

Three network design problems for community energy storage

AbstractIn this article, we develop novel mathematical models to optimize utilization of community energy storage (CES) by clustering prosumers and consumers into energy sharing communities/microgrids in the context of a smart city. Three different microgrid configurations are modeled using a unifying mixed‐integer linear programming formulation. These configurations represent three different business models, namely: the island model, the interconnected model, and the Energy Service Companies model. The proposed mathematical formulations determine the optimal households' aggregation as well as the location and sizing of CES. To overcome the computational challenges of treating operational decisions within a multi‐period decision making framework, we also propose a decomposition approach to accelerate the computational time needed to solve larger instances. We conduct a case study based on real power consumption, power generation, and location network data from Cambridge, MA. Our mathematical models and the underlying algorithmic framework can be used in operational and strategic planning studies on smart grids to incentivize the communitarian distributed renewable energy generation and to improve the self‐consumption and self‐sufficiency of the energy sharing community. The models are also targeted to policymakers of smart cities, utility companies, and Energy Service Companies as the proposed models support decision making on renewable energy related projects investments.

Calculation and optimization of China's power distortion under carbon peaking target

Based on Hahn & Metcalfe (2021), we deduced the theoretical framework and calculated China's power distortion index (PDI) using the city-level industrial and residential power consumption and price data. We further studied the relationship between PDI and carbon emissions, and optimized the PDI under the carbon peaking target through scenario analysis. We find that: (1) China's PDI showed an inverted “U” shape between 2006 and 2019. (2) On average, one-unit decrease in PDI could result in a decrease of 3.72 % in carbon emissions. (3) The scenario simulation indicates that to achieve the target PDI, the average industrial (residential) power price in 2030 needs to be reduced (increased) to 0.59 (0.58) yuan.

A comprehensive and secure scheme for privacy-preserving smart meter data aggregation in the smart grid

Smart meters play a crucial role in the functioning of the smart grid by rapidly collecting and transmitting power consumption data to electricity companies. However, the real-time nature of smart meter data poses privacy risks for customers. To address this concern, encrypted aggregation of smart meter power consumption has been widely employed to protect customer privacy. In this paper, we propose an innovative scheme designed specifically for smart grids to fulfill these requirements. Our scheme demonstrates superior performance compared to existing solutions in terms of communication cost, computation, and functionality features. The proposed authentication protocol not only enables the secure sharing of power consumption data but also satisfies various security requirements, including mutual authentication, anonymity, prevention of man-in-the-middle attacks, and more. Furthermore, our framework exhibits significantly lower computing, communication, and storage overhead compared to similar schemes in the context of smart grids. This highlights the comprehensive and secure nature of our suggested framework, surpassing other existing smart grid schemes in terms of overall effectiveness and reliability.

Enhancing Manufacturing Efficiency at Telkom University: Advanced Automation of Bucket Conveyor and Hammer Mill Machine with PLC and HMI Trough the Waterfall Method

The abstract presents a study on the enhancement of manufacturing technology at Telkom University, specificallyaimed at improving the production of raw materials for charcoal briquettes and animal feed. Initially hindered by a lackof a sophisticated monitoring and control system, the existing production relied heavily on manual time calculations, leading to high risk of inaccuracies and errors. To address this, the study proposes a novel redesign of the control system for the hammer mill machine, particularly in the panel section, using the waterfall design methodology. Thisstrategic redesign has led to the implementation of an advanced automation system, which significantly streamlines the process by automatically displaying time and power consumption data on the HMI display. This enhancement notonly simplifies monitoring and control, but also substantially boosts efficiency. The results show a reduction in thehammer mill machine's operating time by 13 seconds, an increase in productivity by 48%, and reveal powerconsumption details for the motor and conveyor bucket. These results demonstrate the efficacy of the redesignedsystem in enhancing operational efficiency and monitoring precision in the manufacturing process.

Prediction of power consumption and its signals in sustainable turning of PH13-8Mo steel with different machine learning models

Due to extensive distribution and huge demand of energy efficient processes, the energy-saving of machining processes draws more and more attention, and a significant variety of methods have evolved to prognosis or optimise the energy consumption in machining operations. Similarly, the estimation of power consumption-cutting conditions relationships is of great importance for optimizing processing costs and for cleaner machining. Compared to traditional methods, machine learning (ML) approach is one of the effective analysis options to model machinability indicators such as cutting force, tool wear, power consumption and surface quality. In this study, PH13-8Mo stainless steel was machined with coated carbide inserts using primarily Dry, MQL, nano-Graphene + MQL, nano-hBN + MQL, Cryo, Cryo + MQL cutting environments. Power consumption and its signals during milling were measured and different machine learning models were applied to estimate the Pc. To develop the Pc model based on the ML algorithm, 70% of the power consumption data is reserved for training and 30% for testing. In all cutting environments, power consumption increased by an average of 3.14% as feed speed increased. The reduction in Pc compared to the dry cutting was calculated as an average of 2.2%, 3.17%, 2.57%, 4.88% and 5.45% for MQL, nano-Graphen + MQL, nano-hBN + MQL, Cryo, Cryo + MQL, respectively. It is seen that the developed prediction model can reflect the power consumption-parameter relationships at high accuracy.

Simulating appliance-level household electricity data: Accounting for residential behavior and usage patterns in China

Data scarcity presents a significant challenge for improving deep learning algorithms in Non-intrusive Load Disaggregation. These algorithms heavily rely on extensive appliance electricity consumption data, which is expensive to gather through on-site measurements. Therefore, generating simulated data has emerged as a viable alternative. However, existing approaches often randomly combine appliance power, failing to capture realistic household electricity usage patterns. To address this, this study investigates the dynamics of household electricity consumption throughout a 24-h day, considering factors such as household size, age, occupation, and education. Markov chains and Variational Auto-encoder are employed as a data generation model to simulate electrical power consumption data. Appliance power consumption data are aggregated to represent the total household load by incorporating resident electricity consumption behavior. Comparing the generated data with actual data from a self-measurement dataset, the Jensen-Shannon divergence, RMSE, and MAE are utilized to assess their similarity, and the Entropy and the Coefficient of Variation are utilized to evaluate randomness and volatility. The results indicate a high level of distribution similarity between the generated and actual data. Moreover, the observed variations show the model's capability to produce diverse and rich datasets. Overall, this research demonstrates the value of simulated data created as a low-cost method for Non-intrusive Load Disaggregation.

The COVID-19 pandemic's impact on user consumption: A shift towards very efficient load forecasting

The COVID-19 pandemic has prompted significant shifts in energy consumption patterns, necessitating a departure from traditional data analysis methods. Accurate energy forecasting models play a crucial role in various domains of planning and energy management. It is possible to predict the demand for electricity consumption using a variety of techniques. Fundamentally, this research comprises two main components. The first phase assesses the influence of the COVID-19 pandemic on user consumption patterns through time series analysis. The subsequent segment is dedicated to long-term load forecasting, employing both statistical methods termed as Holt-Winters and Prophet algorithms and an Artificial Neural Network approach known as Long Short-Term Memory. These approaches aim to predict electricity demand amid the diverse and intricate context of the COVID-19 pandemic. It utilizes both conventional time series forecasting methods and advanced Artificial Intelligence models to enhance the accuracy of load forecasting. Since rapid societal and economic shifts are unpredictable during a pandemic, methods based on Artificial Intelligence are becoming extremely important due to its high precision. Though, it is problematic to conduct load prediction with high accuracy because of influencing factors like climatic, socioeconomic, and seasonal aspects. Finally, using hourly-driven power consumption data from Houston, Texas, USA, the Long Short-Term Memory model outperforms the Holt-Winters and Prophet models in a test of generalizability and accuracy. It is possible to verify the accuracy of experimental findings by analyzing the resulting constraints and influential factors.

A privacy-preserving distributed energy management framework based on vertical federated learning-based smart data cleaning for smart home electricity data

Nonintrusive load monitoring (NILM) is a part of home energy management systems ((H)EMSs), which can advance the systems to leverage and use gathered electricity data (load data) to achieve cost-effective load monitoring for efficient (residential) demand-side management (DSM). Load data monitored by existing (H)EMSs, whose load monitoring is based on intrusive load monitoring, can be used as a preliminary stage for NILM to be accelerated in energy management for the improvement of its practicality. However, they can be polluted. For example, power-consumption data gathered by appliance-level smart plugs may have an incorrect appliance label (a mislabeled appliance ID). Polluted data must be preprocessed through a data cleaning process before they are leveraged and used. Data-cleaning approaches that improve data quality should be developed considering a decentralized paradigm, because a centralized data-cleaning paradigm cannot be applied to future edge-based IoT applications. Additionally, preventing data leakage is paramount in data management for numerous field applications. This study develops a privacy-preserving distributed energy management framework based on vertical federated learning for smart data cleaning as a demonstrative application of smart home electricity data toward distributed NILM. The framework developed in this study with the advent of AI methodology can achieve smart data cleaning for further distributed NILM to be accelerated for its practical applications; its feasibility and effectiveness have been verified experimentally.

Research and Development of an Embedded Multi-Channel Sensor Data Acquisition Device for Reservoir Dams

The embedded system of intelligent reservoir dam achieves the integration and efficient utilization of water conservancy dam system data through multi-channel data collection and analysis calculated by computer technology, CNC system, and neural network. Compared with traditional data collection and processing methods, both timeliness and accuracy have been greatly improved. This study aims to develop a multi-channel sensor data acquisition device for reservoir dams based on embedded system technology. This device can collect real-time and efficient data from sensors in various parts of the dam, ensuring the safe operation of the reservoir dam. By using advanced embedded system technology, this device has advantages such as low power consumption, high stability, and real-time data transmission. The Analytic Hierarchy Process (AHP) was used to study the embedded multi-channel sensor data acquisition device for reservoir dams in multiple directions and factors. The AHP method provides an effective means for problem decision-making in complex situations. Referring to the AHP method, the factors that affect reservoir dams can be divided into different levels. Compare the importance of two random factors in each level to obtain a specific quantitative expression of the relative important factors on a scale. Then repeat this step to obtain the weight ranking for different levels. At the same time, the device monitors key parameters such as temperature, humidity, displacement, and pressure in various parts of the dam through multiple sensors, providing strong support for early warning and decision-making of reservoir dams. The results of this study have important practical significance and application value for improving the safety and stability of reservoir dams.