Power Consumption Profile Research Articles

Securing IoT: Mitigating Sybil Flood Attacks with Bloom Filters and Hash Chains

In the evolving landscape of the Internet of Things (IoT), ensuring the security and integrity of data transmission remains a paramount challenge. Routing Protocol for Low-Power and Lossy Networks (RPL) is commonly utilized in IoT networks to facilitate efficient data routing. However, RPL networks are susceptible to various security threats, with Sybil and flood attacks being particularly detrimental. Sybil attacks involve malicious nodes generating multiple fake identities to disrupt network operations, while flood attacks overwhelm network resources by inundating them with excessive traffic. This paper proposes a novel mitigation strategy leveraging Bloom filters and hash chains to enhance the security of RPL-based IoT networks against sybil and flood attacks. Extensive simulation and performance analysis demonstrate that this solution significantly reduces the impact of sybil and flood attacks while maintaining a low power consumption profile and low computational overhead.

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.

Energy-efficient Vienna rectifier for electric vehicle battery charging stations

Global DC fast-charging infrastructure is being rapidly developed to accommodate the ever-increasing demand from the electric vehicle market. A major complication in the electric power system's process is the proliferation of ultra-fast charging (UFC) stations, which are known for their enormous power consumption and unpredictable, intermittent performance. By implementing grid-supportive features and ensuring an improved power consumption profile for the grid, installing regional energy storage can solve these challenges. This study presents a control approach for managing the grid-side AC/DC converters of rapid charging stations focused on future-oriented regulation. The paper primarily concentrates on various Vienna rectifier topologies. The technology, characteristics, benefits, and operational aspects of Vienna rectifier topologies are vital to improving the performance, efficiency, and grid integration of electric vehicle charging systems. Fast charging, grid stability, energy economy, and the smooth integration of electric vehicles into the electrical grid are all made possible by Vienna rectifiers. When used in battery energy storage systems (BESS) for electric vehicle charging infrastructure, Vienna rectifiers allow for effective discharge and charging of the batteries. The configurations and assessments of these converters are examined, assessed, and compared based on power output parameters, element count, power factor, THD, and efficiency. The Vienna rectifier has been identified as the best suitable DC fast-charging converter architecture for power levels exceeding 15 kW due to its exceptional efficiency, limited output voltage ripples, high power density, reduced current ripples, and reliable performance. Research on ways to improve electric vehicle charging infrastructure can be spurred by a better grasp of the Vienna rectifier's technological complexities and performance benefits, leading to greener and more efficient transportation options. As a result, the system's power density will eventually rise and elemental stress will decrease.

Power consumption profiles of the main fans at coal mines

Purpose. To determine the main indicators of the power consumption profiles of the main ventilation fans of coal mines under the condition of regulating their performance using controlled electric drive systems based on semiconductor elements. The methods. The methods of operations research were used to analyse technological processes in terms of compliance of the actual power consumption profiles of the main fans with their expected operating modes. The methods of mathematical statistics were used to determine the statistical parameters of the electrical loading diagrams of the main ventilation fans of coal mines. Findings. A statistical analysis of typical power consumption profiles of the main fans of coal mines with different electric drive systems has been carried out: a synchronous motor with a high-voltage frequency converter, an induction motor based on the asynchronous-thyristor cascade system, and a synchronous motor with ‘classical’ rotor excitation control without semiconductor elements. The feasibility of using adjustable electric drive systems to control the performance of main ventilation fans in terms of reducing active power consumption has been confirmed. However, at the same time, the consumption of a significant amount of reactive power is shown, which is inherent for systems based on semiconductor elements, regardless of the motor type (synchronous or asynchronous). Originality. The statistical analysis of the actual daily power consumption profiles of the main fans of coal mines with different controlled electric drive systems has shown that the standard deviation of the active load does not exceed 1% of the rated power of the drive motor and does not depend on its type (synchronous or asynchronous). Practical implementation. The practical value of the work is in determining the actual consumption of reactive power by the systems of controlled electric drive based on semiconductor elements. It shows its significant level, which should be provided by appropriate filter-compensating devices on the power supply buses of the motors of the main ventilation fans.

Techno-Economic Assessment of Solar–Grid–Battery Hybrid Energy Systems for Grid-Connected University Campuses in Kenya

This paper presents the techno-economic feasibility of using grid-connected PV hybrid systems to supply power in large grid-dependent academic institutions. The study was conducted using the administration building of Moi University in Kenya. The power consumption profile of the building was collected using a PCE-360 power analyzer. The peak load demand was found to be 60 kW. Using random variability constants of 4% for day-to-day and 4% time-step load variability, a peak demand of 70.58 kW was obtained, which was used in our simulation. The solar radiation and temperature data for this site were collected from the weather station of the university. The hybrid system was simulated using HOMER Pro software. It was found from the simulation results that the optimal system was the solar PV/grid without battery storage, which had a levelized cost of energy (LCOE) of KSH 8.78/kWh (USD 0.072), net present cost (NPC) of KSH 27,974,492 (USD 230,813), capital expenditure (CAPEX) of KSH 26,300,000 (USD 216,997), and a simple payback period (SPBP) of 5.08 years for a 25-year life span. This system, when compared to the existing grid, showed an 83.94% reduction in the annual electricity bill of the administration building. These results demonstrate a reduction in energy cost by a renewable energy fraction of 67.1%.

I Am an Earphone and I Can Hear My User’s Face: Facial Landmark Tracking Using Smart Earphones

This article presents EARFace , a system that shows the feasibility of tracking facial landmarks for 3D facial reconstruction using in-ear acoustic sensors embedded within smart earphones. This enables a number of applications in the areas of facial expression tracking, user interfaces, AR/VR applications, affective computing, and accessibility, among others. Although conventional vision-based solutions break down under poor lighting and occlusions, and also suffer from privacy concerns, earphone platforms are robust to ambient conditions while being privacy-preserving. In contrast to prior work on earable platforms that perform outer-ear sensing for facial motion tracking, EARFace shows the feasibility of completely in-ear sensing with a natural earphone form factor, thus enhancing the comfort levels of wearing. The core intuition exploited by EARFace is that the shape of the ear canal changes due to the movement of facial muscles during facial motion. EARFace tracks the changes in shape of the ear canal by measuring ultrasonic channel frequency response of the inner ear, ultimately resulting in tracking of the facial motion. A transformer-based machine learning model is designed to exploit spectral and temporal relationships in the ultrasonic channel frequency response data to predict the facial landmarks of the user with an accuracy of 1.83 mm. Using these predicted landmarks, a 3D graphical model of the face that replicates the precise facial motion of the user is then reconstructed. Domain adaptation is further performed by adapting the weights of layers using a group-wise and differential learning rate. This decreases the training overhead in EARFace . The transformer-based machine learning model runs on smart phone devices with a processing latency of 13 ms and an overall low power consumption profile. Finally, usability studies indicate higher levels of comforts of wearing EARFace ’s earphone platform in comparison with alternative form factors.

Comparison of hydraulic, pneumatic and electric linear actuation systems

Different applications or industries use different systems for linear actuation, such as hydraulic, pneumatic or electric. Electric systems are becoming increasingly popular and are already replacing hydraulic systems in various applications. These are known to be potentially harmful to the environment, as large amounts of fluid can be released into the environment in the event of a pipe burst or other accident. This paper presents the results of a comparison between hydraulic, pneumatic and electric systems under variable conditions but with similar loads in all three systems. The common feature of all three systems is the input power, which was limited to 1.1 kW. There was no hydraulic accumulator in the hydraulic system and no pressure vessel in the pneumatic system, so no stored energy could influence the system behaviour or results. The main difference between the systems studied was the profile of displacement and power consumption. The most consistent response and lowest power consumption were obtained with the electric system, although both hydraulic and pneumatic systems can achieve consistent response with some modifications.

Assessment of Current Smart House Solutions: The Case of Portugal

The world is facing many challenges to keep up with growing energy demand and greenhouse gas and pollutant emissions. A possible solution is to integrate renewable energy sources (RESs) into residences and create energy management systems (EMSs) that reduce consumption and relieve the main grid in critical hours. This paper is focused on analyzing different EMSs using solar photovoltaic (PV) modules and three energy storage systems (ESSs) to increase savings without affecting the user’s needs, with thirty-three power consumption profiles and three energy tariffs over a 25-year investment to compare which solution presents the best economic result in each of the 297 analyzed scenarios. The results show that the higher the overall daily consumption values, the greater the profits for all EMSs. The lowest demand profiles lead to the best self-consumption and self-sufficiency results. PV-only systems with a tri-hourly tariff have the best overall results. In the case of battery operation, each case should be carefully analyzed in order to choose the correct technologies and tariff. The results also allowed us to verify that even though having more PV modules generates more energy, it can make a project less economically desirable.

Characterizing load profile-based enterprise profiling under COVID-19 lockdown policy: A provincial case in China

The COVID-19 epidemic has led to devastating consequences worldwide due to strict social distancing measures, travel bans, city lockdowns, and other activity restrictions. Numerous lessons have been accumulated by various industries or sectors in the process of coping with COVID-19. In our paper, the actual electricity consumption of 1.145 million enterprises in a province of China is investigated in the normal period, the breakout period, and the recovery period. An artificial intelligence based enterprise profiling model is established to characterize power consumption profile geographically, temporally, and industrially. The recovery rate of enterprise power consumption is proposed as a key indicator representing the status of production resumption. Under lockdown measures, enterprise in different regions or sectors exhibits diversified responses in power consumption, the unsupervised learning model and the correlated coefficient extract the abstract characteristics and labels from various enterprise power profile. Ultimately, enterprises with similar electricity consumption characteristics are grouped and labeled to provide customized power services. Accurate enterprise profiling can effectively aid in facing with the challenges of insufficient orders, tight capital chains, and rising costs caused by the epidemic.

GraphPowerNet: Graph-based power consumption profiling for mobile phone applications

Mobile phone power analysis provides an approach to user behavior profiling. Existing work conducts the analysis on either a single module or total power consumption, and ignores the correlation between modules. However, such correlation is vital in capturing usage patterns. To this end, graph neural networks (GNNs) are leveraged to explore such relationships among different modules. Since GNNs rely on pre-defined graph structures for message propagation, they cannot be applied directly to the scenario where the graph structure needs to be discovered. To resolve this issue, an approach called GraphPowerNet is developed. Firstly, a pseudo label-acquisition mechanism is used to cluster power consumption data into multiple virtual usage patterns that serve as supervisory information. Next, a graph learning module is designed to automatically extract the correlations among mobile phone modules. Based on the learned graph and the power consumption data, a graph convolutional model is adopted to conduct graph classification. Finally, the power consumption data is profiled by both the classification result and the graph structure. Experimental results on real world data show that the F1-score of application usage behavior classification is higher than 95%. Moreover, interpretability analysis shows that the graph learned via GraphPowerNet has strong practical significance.

Design and implementation of a low-cost power logger device for specific demand profile analysis in demand-side management studies for smart grids

Demand-side management is vital for achieving a balance between electricity demand and generation, ensuring a reliable grid. Achieving this balance necessitates a deep understanding of consumption patterns and behaviors, making the analysis of demand profiles a critical component in the development of effective demand response programs. This study focuses on designing a cost-effective power logger device for collecting household power consumption profiles. The device features adjustable recording resolution, a user-computer interface for easy operation, long-term offline recording, compact size, and affordability. Design details of the proposed device are presented, and the prototype is tested to demonstrate its feasibility and reliability. The results, obtained over an extended period, are presented in this paper, enhancing the resources available for advancing machine learning studies, forecasting studies, and demand-side management research.

A novel strategy for simultaneous active/reactive power design and management using artificial intelligence techniques

Adequate capacity and strategy of an energy management system improve technical and economic characteristics, promote flexibility, and encourage higher penetration of distributed generation. This paper presents a new strategy for simultaneously carry out the optimal equipment design and the management of active and reactive power in smart electrical installations. The proposed optimization algorithm defines the capacity of the photovoltaic generation and the battery banks, the capacitor banks and substation configuration, in addition to selecting the optimal parameters of the battery energy and capacitor bank management strategy making the methodology adaptable to any consumption profile and energy billing format. The purpose of the algorithm is to reduce the global cost of energy over a 20-year time horizon using net present value as the metric, considering energy bill, investment, maintenance, and replacement costs. The energy tariff is composed of time-of-use scheme, demand cost, fines associated with low grid power factor, and feed-in remuneration. The approach also considers transformer losses and 3 typologies defined by the k-means technique used to represent the power consumption profile and meteorological data to reduce the computational effort. The Genetic Algorithm has been applied in a real case study whose project’s net present value was reduced by 28.15%, with an internal rate of return of 21.20% and a computation time of 15 s. The results show high algorithm performance with a reduction of 78.16% in energy and 16.43% in demand costs, virtually eliminating costs caused by low power factor and reducing the energy injected into the grid.

A Time Efficient Factorial Hidden Markov Model-Based Approach for Non-Intrusive Load Monitoring

Assessment of energy consumption behaviour plays an important role in designing demand reduction programs by utility companies. Knowledge of appliance activities in a household aids in conducting the energy consumption behaviour assessment for a community load. Non-intrusive load monitoring (NILM) is a tool that can help in identifying the appliance activities. In this paper, a Modified Factorial Hidden Markov Model (MFHMM) based NILM framework is proposed, which models dependencies among appliance operating states and differential appliance operating states by considering differential in power consumption profiles over time. All the appliances are modelled as individual load models using the Hidden Markov Model (HMM). The appliance operating states are obtained with the application of an iterative k-means clustering algorithm. The aggregated power consumption profile is divided into segments using an optimization-based change-point detection (CPD) algorithm. The NILM problem is solved for each of the segments, and the obtained solution is corrected based on the voltage profile at the aggregated load point. The approach of segmentation and efficient identification of appliance operating states make the model less time complex. Simulations are carried out on publicly available datasets named AMPds, REDD, and UK-DALE. The efficacy of the proposed framework over existing frameworks is evident from the simulation results.

Power consumption regulation of enterprises for reducing of the energy system peak loads

Purpose. To propose measures for a partial solution to the problem of evening peak loads ensuring in the energy system of Ukraine by regulating the power consumption modes of industrial enterprises. This approach will reduce the level of such loads and align the power consumption profile. The methods. The methods of operations research and analysis of technological processes are used. The operation modes of the Ukrainian operating coal mines were studied and their electrical loading diagrams, which are formed by the appropriate technological processes, were analyzed. Findings. A comparative analysis of electrical loading diagrams of the power system and power consumption profiles of industrial facilities and the residential services was performed. The analysis showed the similarity of the electrical loading diagrams of coal mines, which is due to the typical mode of their operation, namely the presence of three work shifts (for coal mining) and one repair shift (for equipment maintenance). This creates prerequisites for the use of such enterprises as consumers-regulators of active power. As a result of which ways of "mitigation" of the problem of peak loads ensuring of the power system were proposed. Originality. The patterns of formation of the electric loading daily profiles for coal mines, determined by the operating mode of enterprises, were established.A new approach is proposed, which consists in changing the daily operation mode of coal mines, namely, moving the repair shift to a time that coincides with the period of evening peak loads in the Ukrainianpower system. Practical implementation. The practical value of this work lies in reducing the level of power consumption during peak loads in the power system,what is currently a very urgent problem for Ukrainian energy complex. The proposed approach allows to use the normal operation mode of a coal mine with the preservation of all necessary technological processes as an effective consumer-regulator of active load.

A biomass-based polygeneration system for a historical building: A techno-economic and environmental analysis

Combined cooling, heating, and power systems (CCHP) are considered one of the most efficient energy systems since they have a less detrimental environmental impact, and therefore sustainable energy savings, and eventually less CO2 emissions. An even higher advantage is possible when a renewable energy fuel source (e.g., Biomass) is used. The proposed study presents the energy modeling of a 169 kWe capacity, biomass-based CCHP system retrofitted to a historical building, located in Perugia, Italy. Two well-known simulation software’s, ASPEN Plus and TRNSYS, are purposely integrated. In the first phase of the study, a wood biomass combustion-based externally fired gas turbine (EFGT) along with an Organic Rankine Cycle (ORC) system is modeled in Aspen Plus. In the later phase, a historical building “Sant’ Apollinare” model, and the remaining CCHP system are developed in TRNSYS. The transient system is modeled such that it fulfills the peak thermal energy demands of the retrofitted building. The main components of the system are a heat exchanger (HE), storage tanks (TK), a single-stage LiBr-H2O absorption chiller (ACH), and balance of plant (BOP) components. A heat exchanger recovers heat from the gas cycle, which is then utilized for running an ORC, heating and cooling of the end-user building, biomass drying, and domestic hot water (DHW) supply. A storage tank supplies direct heat to the end-user in winter and drives the ACH in summer. The results of the model provide data on the performance parameters of different components of the CCHP system such as temperature, power consumption, and energy profiles that allow evaluating the overall energy performance of the CCHP system. Finally, a feasibility analysis of the system is carried out on the basis of economic indexes such as simple pay back (SPB) period, net present value (NPV) and profitability index (PI), for two different case scenarios assumed for fuel (e.g., biomass) supply cost. The SPB is 2.93 years, when biomass is obtained for free. Whereas, if biomass is purchased from the market the SPB becomes 7.15 years. From an environmental perspective, the plant ensures a decrease of 632 tons of CO2 emissions annually.

An efficient P2P energy trading platform based on evolutionary games for prosumers in a community

This paper proposes a game-theoretic community energy management model that encourages energy trading between members of a community. The members of the community are classified as consumers and vendors. These roles are interchangeable based on the day-ahead power consumption profile. An evolutionary game is proposed to model the consumer dynamics in selecting the vendor. The vendors can select the consumer to whom they wish to sell the power by setting prices that attract consumers. Therefore, a game exists between the vendors in which competitive prices are set to lure the consumers. The game converges at a Nash equilibrium. The results of the algorithm are the preference of consumers to each vendor and the price of power set by each vendor. The interaction between various entities is modeled by a Stackelberg game. The proposed model is tested in a community in Sydney with renewable energy generators and battery storage systems. Results show the cost savings for the consumers and the profits made by the vendors. The convergence of the models shows the effectiveness of energy trading within the community for financial gains and energy conservation. The proposed model show that profits can be increased and the burden on the grid could be reduced if demand response strategies are adopted and coupled with storage systems.

Voltage constraint-oriented management of low carbon technologies in a large-scale distribution network

With the rising concerns about climate change and technological developments, the deployment of low carbon technologies (LCT) is gaining importance for reliable and sustainable power system operation. To decarbonize the heat and transportation sectors, LCTs such as heat pumps (HP) and electric vehicles (EV) are integrated into the power system from the low voltage (LV) distribution network, where predominantly end-users are connected. However, the increasing penetration of EVs and HPs, together with distributed photovoltaic (PV) systems on the demand side, can cause technical challenges in LV distribution networks, such as under/over voltages at the far end of the feeders. Therefore, the hosting capacity of a grid for LCTs is limited due to network constraints such as line ampacity, bus voltage, and frequency. In this paper, voltage-constrained management approaches for LCTs are proposed to improve the grid hosting capacity in a real large-scale distribution network. Moreover, HPs, PVs, battery energy storage systems (BESS), and EVs as well as their vehicle-to-grid (V2G) feature are evaluated as LCTs. The uncertain behavior of these technologies is taken into consideration for more realistic analyses. In addition, the charging and discharging interactions of BESSs are controlled by following the power consumption profiles from the economic point of view. Furthermore, the hourly carbon data is used to control BESSs from an emission-sensitive aspect. To demonstrate the effectiveness of the proposed algorithms, a series of tests is conducted on a real distribution network model. The results show that the hosting capacity of the considered LV network can be increased from 35% to 50% using the proposed algorithms. The promising results obtained in this study pave the way for future active LCT management studies to improve hosting capacity in urban networks, and pilot demonstrations in the field.

Energy Consumption Monitoring System Based on IoT for Residential Rooftops

This work aims to create a web-based real-time monitoring system for electrical energy consumption inside a specific residence. This electrical energy is generated from a micro-CPV system lying on the roof of this residence. The micro-CPV is composed of a Fresnel lens as the main optical element, a spherical lens as the secondary optical element, and a multi-junction solar cell. A tiny photovoltaic concentrator system with a geometric concentration ratio of 100× is analyzed in the first part of this study, while the second part is designed to monitor the electricity generated by the micro-CPV system. An ESP8266 controller chipset is used to build the sensing peripheral node, which controls a relay and a PZEM-004T current sensor. As a result, the optical element used has approximately 83% optical efficiency, with an acceptance angle of 1.5°. Regarding the monitoring system, the architecture demonstrates the ability of the system to monitor current and energy consumption in real time using a computer or smartphone and a web server specially designed to continuously update the power consumption profile in a specific smart home environment. The whole electric power consumption monitoring system generally worked well. The monitoring system is configured to provide excellent accuracy for a 0.6% hit.

Feasibility study of using cogeneration plants at Kuzbass coal mines

The paper considers the problem of reducing greenhouse gas emissions in the process of coal mining during the coal mine methane utilization in power supply systems. An algorithm to form recommendations for the implementation of CMM generation is presented. A simulation model for one of the Kuzbass coal mines was developed in the PowerFactory software application. The simulation model considers the uneven nature of the power consumption of mining equipment. As a result of modeling, daily power consumption profiles and voltage levels in the coal mine power supply system were determined before and after the implementation of the proposed measures. Based on the results, the technical and economic effects was estimated, which consisted in reducing the direct and indirect carbon footprint, electricity and capacity fees. It has been established that the cost of carbon dioxide emission quotas significantly affects the investment attractiveness of cogeneration projects. Based on the results, recommendations are given to stimulate the development of small generation in coal mines.

Efficient deep generative model for short-term household load forecasting using non-intrusive load monitoring

Home energy management systems (HEMS) enable key strategies and methods to improve residential efficiency and energy utilization. To make informed decisions, HEMS depend on energy monitoring and forecasting systems. However, short-term load forecasting (STLF) at the household level is challenging due to the high uncertainty in load demand caused by the difficulty of expecting customer behavior. Traditional solutions only use household load data at low temporal resolution and cannot adequately consider the users’ power consumption profile, limiting prediction accuracy. This paper addresses these issues and proposes a two-stage approach for the household STLF that leverages past and future appliance load data. In the first stage, the past and future appliance load data is estimated from the aggregate load measurement through a non-intrusive load monitoring (NILM) approach based on a deep generative model using variational autoencoders (VAE). This granular information is then augmented through a novel household load predictive model. We implement six configurations combining three stage-1 NILM models with two stage-2 predictive models based on VAE and temporal convolutional networks. Given the volatile nature of residential loads, identifying the running appliances and their consumption benefits the household STLF. The proposed approach is designed to leverage short-term appliance load forecasts rather than being limited to historical features, which is a key advantage, especially when multi-state appliances are used. The proposed approach is compared to state-of-the-art approaches on the UK-DALE and REFIT datasets and yields competitive results with improvements of 16% and 19% on average for the MAE and MAPE metrics, respectively.