User Load Research Articles

Responsible, Automated Data Gathering for Timely Citizen Dashboard Provision During a Global Pandemic (COVID-19)

Creating a public understanding of the dynamics of a pandemic, such as COVID-19, is vital for introducing restrictive regulations. Gathering diverse data responsibly and sharing it with experts and citizens in a timely manner is challenging. This article reviews methodologies of COVID-19 dashboard design and discusses both technical and non-technical challenges associated. Advice and lessons learned from building a citizen-focused, automated county-precision dashboard for Germany are shared. Within four months, the web-based tool had 5 million unique visitors and 70 million sessions. Three developers set up the basic version in less than one week. Early on, data was screen scraped. An iterative process improved timeliness by adding more fine-grained data sources. A collaborative online table editor enabled near real-time corrections. Alerting was setup for errors, and statistics apply for sanity checking. Static site generation and a content delivery network help to serve large user loads in a timely manner. The flexible design allowed to iteratively integrate more complex statistics based on expert knowledge built on top of the collected data and secondary data sources such as ICU beds and citizen movement.

Machine Learning-Based Resource Allocation Strategy for Network Slicing in Vehicular Networks

The diversified service requirements in vehicular networks have stimulated the investigation to develop suitable technologies to satisfy the demands of vehicles. In this context, network slicing has been considered as one of the most promising architectural techniques to cater to the various strict service requirements. However, the unpredictability of the service traffic of each slice caused by the complex communication environments leads to a weak utilization of the allocated slicing resources. Thus, in this paper, we use Long Short-Term Memory- (LSTM-) based resource allocation to reduce the total system delay. Specially, we first formulated the radio resource allocation problem as a convex optimization problem to minimize system delay. Secondly, to further reduce delay, we design a Convolutional LSTM- (ConvLSTM-) based traffic prediction to predict traffic of complex slice services in vehicular networks, which is used in the resource allocation processing. And three types of traffic are considered, that is, SMS, phone, and web traffic. Finally, based on the predicted results, i.e., the traffic of each slice and user load distribution, we exploit the primal-dual interior-point method to explore the optimal slice weight of resources. Numerical results show that the average error rates of predicted SMS, phone, and web traffic are 25.0%, 12.4%, and 12.2%, respectively, and the total delay is significantly reduced, which verifies the accuracy of the traffic prediction and the effectiveness of the proposed strategy.

Examination of magnetic properties of the multi – Gap amorphous Fe78Si13B9 block cores intended for harmonic filters used in electric power systems

There is a permanent demand worldwide for the advanced low-losses soft magnetic materials intended for application in power electronics, including power electronic converters used to process various forms of the electrical energy into such that is best suited for the specific user loads or applications. This paper shows result of investigation of magnetic properties of amorphous multi-gap cores which were used to manufacture a prototype 27-multi-gaps core for the three-phase harmonic filter with a common yoke, with a rated power of 7.5 kW, operating voltage of 400 V and electric power frequency of 50 Hz. The main attention was focused on studying the effect of the chemical composition and core structure on the level of power losses in the core and temperature increase during operation for amorphous material in relation to the polycrystalline Fe-Si steel with 3 wt% Si core despite the low operating frequency of 50 Hz.

Optimal Design of Information Elements in Virtual Reality System Based on TOPSIS

Aiming at the problems of heavy cognitive load of users and uncoordinated allocation of design resources in virtual environment, this paper comprehensively analyzes the explicit expression of information in user interaction activities, builds SMCR model, and proposes an optimization model of virtual reality interface interaction system based on TOPSIS. Through the computer sensing of user actions, behaviors, tasks and other information in the virtual environment, the mapping relationship between resources and information is determined according to the resource allocation scheme based on SMCR mode, and encoded in the form of information representation; Based on TOPSIS, the importance of design resource elements is sorted. Through further refining the information representation, the sorting results are used as precipitated knowledge for scheme design to realize the optimal layout of interactive interface visual elements. The results show that the virtual reality interface system based on this model mobilizes the enthusiasm of users to actively interact and improves the usability and timeliness of human-computer interaction.

Electricity price decision-making method of electricity selling company based on multi-objective optimization and Min-Max Regret Theory

In the electricity selling market, the long-term goal of electricity selling companies is to maximize the revenue, but the strategies adopted in the short term have been different. Some electricity selling companies pay more attention to the revenue, while some electricity selling companies tend to reduce the sales price so as to agent more electricity. Based on the mixed integer programming (MIP) formula, this paper proposes a multi-objective mathematical programming (MMP) model, which considers maximizing the retailer’s profit and minimizing the sales price to customers. The Pareto optimal solution is generated by the normal boundary intersection method. At the same time, considering the risk of electricity selling market faced by electricity selling companies, we use the theory of maximum and minimum regret to evaluate the risk of electricity selling companies in the decision-making of volume and price, and find the optimal solution of risk. Finally, through case analysis, it is proved that through reasonable electricity quantity and price decision, the seller can find the optimal decision to balance the two risks of market price fluctuation and user load fluctuation

Research on Indoor Comprehensive Sensor Layout Method Based on Grid Method

With the development of key technologies for environment change perception, accurate perception of user load status becomes possible. Sensing the user’s load status can reduce the transformation of the medium-voltage power grid, save the investment cost of the equipment for supporting the construction of the power grid, and improve the investment efficiency of the power grid. In this paper, based on the consideration of the user’s indoor scene, space area, flexible device distribution and building structure, etc., with the goal of maximizing the credibility of the indoor perception network, an optimal networked indoor sensor layout model is established. The weight of each sensing grid is calculated based on the sawing mapping, and the layout method with the highest trust is selected as the optimal indoor comprehensive sensor layout scheme. Finally, based on the case of an actual house, the effectiveness and correctness of the proposed model and method in indoor sensor layout are verified.

Seagull

Microsoft Azure is dedicated to guarantee high quality of service to its customers, in particular, during periods of high customer activity, while controlling cost. We employ a Data Science (DS) driven solution to predict user load and leverage these predictions to optimize resource allocation. To this end, we built the Seagull infrastructure that processes per-server telemetry, validates the data, trains and deploys ML models. The models are used to predict customer load per server (24h into the future), and optimize service operations. Seagull continually re-evaluates accuracy of predictions, fallback to previously known good models and triggers alerts as appropriate. We deployed this infrastructure in production for PostgreSQL and MySQL servers across all Azure regions, and applied it to the problem of scheduling server backups during low-load time. This minimizes interference with user-induced load and improves customer experience.

User Power Behavior Similarity Clustering Based on Unsupervised Extreme Learning Machine Algorithm

Background: Advanced Metering Infrastructure (AMI) for the smart grid is growing rapidly which results in the exponential growth of data collected and transmitted in the device. By clustering this data, it can give the electricity company a better understanding of the personalized and differentiated needs of the user. Objective: The existing clustering algorithms for processing data generally have some problems, such as insufficient data utilization, high computational complexity and low accuracy of behavior recognition. Methods: In order to improve the clustering accuracy, this paper proposes a new clustering method based on the electrical behavior of the user. Starting with the analysis of user load characteristics, the user electricity data samples were constructed. The daily load characteristic curve was extracted through improved extreme learning machine clustering algorithm and effective index criteria. Moreover, clustering analysis was carried out for different users from industrial areas, commercial areas and residential areas. The improved extreme learning machine algorithm, also called Unsupervised Extreme Learning Machine (US-ELM), is an extension and improvement of the original Extreme Learning Machine (ELM), which realizes the unsupervised clustering task on the basis of the original ELM. Results: Four different data sets have been experimented and compared with other commonly used clustering algorithms by MATLAB programming. The experimental results show that the US-ELM algorithm has higher accuracy in processing power data. Conclusion: The unsupervised ELM algorithm can greatly reduce the time consumption and improve the effectiveness of clustering.

A 3D Computer Vision-Guided Robotic Companion for Non-Contact Human Assistance and Rehabilitation.

With the rapid aging of the U.S. population, the mobility impairment is becoming a more and more challenging issue that affects a large number of individuals. The research presented in this paper aims at helping the mobility-challenged individuals with a novel robotic companion, which is a walker-type mobile robot capable of accompanying the human user and keeping user at the center for protection and possible power assistance. The robotic companion is equipped with a 3D computer vision system, which provides a unique capability of sensing the human-robot relative position/orientation without physical contact or the need for wearable sensors. As such, the robotic companion enables the user to walk freely with minimum disturbance to his/her normal gait, relieving the user from the physical and cognitive loads associated with the use of traditional assistive devices. For the development of the robotic companion, the authors designed and fabricated a low-cost, differentially steered mobile robotic platform, and also developed a unique image processing system to extract the position/orientation information from the 3D camera-captured images. Furthermore, an advanced motion control system was developed for the robotic companion, which provides novel solutions to the unique challenges such as sway reduction and noise reduction in digital differentiation. To quantify the performance, component and system-level experimentation was conducted, and the results demonstrated that robotic companion and its key components function as desired and the system is expected to reduce the user load and improve the user mobility in real-world scenarios.

Modeling and optimization for distributed microgrid based on Modelica language

Penetration rates of intermittent renewables increase in smart grid due to environmental issues. As a significant part of smart grid, distributed microgrids (DMGs) have huge application prospects for its flexibility, high efficiency and fast recovery ability. In order to improve the effective penetration of renewable energy in distributed microgrids at low cost, we proposed a dynamic model-based configuration and scheduling coupling optimization method in this study. We established the multi-domain joint simulation model for DMGs based on the Modelica acausal modeling language to realize the coupling of distributed generation (DG), energy conversion/storage components and user loads with multiscale time-varying characteristics, and validated its accuracy by using the measured data. On the basis of the model established above, branch and bound method and meta-heuristic algorithm are used to optimize the double layer configuration and scheduling of microgrids in four typical scenarios. Moreover, a novel evaluating indicator, the effective penetration of renewable energy (EPRE), is proposed to improve the system self-utilization rate of renewable energy. Study results show that by using the newly developed model-based optimization method with the new indicator, the self-utilization rate in DMGs can be increased up to 58.05% and the average daily interactive power of electricity fed back to the regional grid is reduced significantly.

Demand response evaluation of RIES based on improved matter-element extension model

With the revolution of traditional economic and social patterns based on concentrated fossil energy intensive consumption, regional integrated energy systems (RIES) have gradually become an important trend for future energy consumption. As an important technical means of integrated energy demand side management, demand response helps to improve the smooth and reliable operation of the integrated energy system (IES). Meanwhile, it is of great significance to improve national energy security and the environment. This paper focuses on the demand response evaluation of RIES based on improved matter-element extension model. Firstly, with the introduction of the RIES demand response evaluation mechanism, the demand response index system of RIES is established from five dimensions: user load, user energy usage, user energy reliability, user energy consumption, and user energy habits. Based on the demand response evaluation index system, the potential demand response users of the integrated energy system are graded into four levels: rewarded user (I), encouraged user (II), general user (III), and non-encouraged user (IV).Secondly, based on the analytic hierarchy process and entropy method to obtain more accurate comprehensive weights, the RIES demand response evaluation model is established by the matter-element extension method. Finally, in order to illustrate the advantages of this method, 10 objects were selected from the regional integrated energy systems demand response project of a Chinese city for evaluation and analysis. The results show that the demand response abilities of the 10 evaluation objects are all at level I, indicating that the automatic demand response (ADR) of 10 evaluation objects is good.

DESIGN OF THE CEILING ASSISTANCE

Ceiling installation auxiliary tool is auxiliary tool for installing a ceiling with a simple method. Namely by placing the ceiling above the cross section and moving the gears to the ceiling can reach the desired height. This auxiliary tool is needed to help to lighten the workload and save the ceiling installation time. This auxiliary tool can reach places or rooms that have narrow access, because this tool can be dismantled pairs. This tool can hold the user load up to 80 kg and the weight of the ceiling up to 47.3 kg, so the total weight reached 127.3 kg. This auxiliary tool has four pedestal wheels that can move as desired.

Cache Selection in Dynamic D2D Multicast Networks Using Inhomogeneous Markov Model

This article presents a user spatio-temporal behavior aware cache selection framework to facilitate device-to-device multicast (D2MD) communication that minimizes the number of caches required while achieving a desired user load on the cellular network. Consequently, it alleviates the caching load on the cellular network. The optimization problem formulated to minimize the number of caches is combinatorial in nature with an exponential search space. Hence, a greedy algorithm for cache selection is proposed to reduce the search space. It has been shown that the greedy algorithm has a complexity O(K <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) where K is the number of users. A real-world location-based inhomogeneous Markov chain is presented to model the joint spatio-temporal behavior of the users. The diurnal variation of observable user load on the core network as well as sum-rate of non-caching users has been demonstrated for real-world location data traces. It has been shown that the proposed framework not only achieves the desired user load but also helps in improving the sum-rate of noncaching users as compared to the mobility-unaware selection of caches.

Modeling and Detection of Future Cyber-Enabled DSM Data Attacks

Demand-Side Management (DSM) is an essential tool to ensure power system reliability and stability. In future smart grids, certain portions of a customer’s load usage could be under the automatic control of a cyber-enabled DSM program, which selectively schedules loads as a function of electricity prices to improve power balance and grid stability. In this scenario, the security of DSM cyberinfrastructure will be critical as advanced metering infrastructure and communication systems are susceptible to cyber-attacks. Such attacks, in the form of false data injections, can manipulate customer load profiles and cause metering chaos and energy losses in the grid. The feedback mechanism between load management on the consumer side and dynamic price schemes employed by independent system operators can further exacerbate attacks. To study how this feedback mechanism may worsen attacks in future cyber-enabled DSM programs, we propose a novel mathematical framework for (i) modeling the nonlinear relationship between load management and real-time pricing, (ii) simulating residential load data and prices, (iii) creating cyber-attacks, and (iv) detecting said attacks. In this framework, we first develop time-series forecasts to model load demand and use them as inputs to an elasticity model for the price-demand relationship in the DSM loop. This work then investigates the behavior of such a feedback loop under intentional cyber-attacks. We simulate and examine load-price data under different DSM-participation levels with three types of random additive attacks: ramp, sudden, and point attacks. We conduct two investigations for the detection of DSM attacks. The first studies a supervised learning approach, with various classification models, and the second studies the performance of parametric and nonparametric change point detectors. Results conclude that higher amounts of DSM participation can exacerbate ramp and sudden attacks leading to better detection of such attacks, especially with supervised learning classifiers. We also find that nonparametric detection outperforms parametric for smaller user pools, and random point attacks are the hardest to detect with any method.

Economic performance of multi-energy supply system in a zero-carbon house

Due to the improvement of people's living standards, uptake of multi-functional household appliances, electricity consumption accounts for a rising ratio in the proportion of primary energy usage. Currently, electricity accounts for more than 50% of primary energy usage and city gas is about 21% of total. The aim of this research focus on improving the efficiency of household energy usage and optimizing the economy of residential energy systems. This paper classified the detail electricity consumption and generation for a Japan typical household with Hybrid Fuel cell and Photovoltaic System (HFPS) based on the history monitored data. Then a simulation model of the Hybrid Heat pump and Photovoltaic System (HHPS) was designed under the same user load for analyzing the energy consumption. Finally, the economy of the two systems is compared and optimized by considering the carbon tax, equipment price and energy price fluctuation. In general, by comparing the environmental and economic performances of both two systems, the results showed that HFPS has a better environmental performance than HHPS, but HHPS presented an economic advantage over HFPS. In addition, with the change of equipment price and the development trend of energy price in the future, the economic performance of HFPS is gradually improved, which is better than HHPS and has more development prospects. The results of this work provide policy guidance for the future Japanese government's promotion of residential fuel cell systems, while choosing the optimal path between user energy characteristics and power contract scheme.

Manage Distributed Energy Storage Charging and Discharging Strategy: Models and Algorithms

The stable, efficient and low-cost operation of the grid is the basis for the economic development. The amount of power generation and power consumption must be balanced in real time. Traditionally the grid needs to quickly detect the electrical load of users in real time and adjust the power generation to maintain the balance between electrical supply and demand, which brings great cost from the output modulation of the generator set. This article focuses on the distributed battery energy storage systems (BESSs) and the power dispatch between the generators and distributed BESSs to supply electricity and reduce electrical supply costs. The cost analysis of electrical supply from the generators and BESSs is proposed. Then, this article introduces a consensus control algorithm (CCA) to dispatch the power output and track the load in a decentralized manner. A nonuniform CCA (NCCA) is proposed to improve the convergence speed especially when the power of BESSs reach upper/lower bounds. Finally, the case study implemented in a 5-bus system with the real load data of a district in Beijing verifies the effectiveness and efficiency of the proposed method.

Spark-based Parallel OS-ELM Algorithm Application for Short-term Load Forecasting for Massive User Data

The data type and quantity of user load data show an exponential growth, so that the traditional load forecasting methods can hardly meet the load forecasting requirements of massive users. Aiming at this problem, a parallel OS-ELM short-term load forecasting model based on Spark is proposed in this article. By analyzing the characteristics of the Spark framework and the MapReduce framework, the Spark big data processing framework is determined as the basic framework for processing massive user load data, and a parallel K-means load clustering model based on Spark is designed. The on-line sequential learning machine OS-ELM makes the hidden layer data of computing each incremental training dataset mutually independent, therefore, a Spark-based parallel OS-ELM (SBPOS-ELM) algorithm is put forward. The proposed model is applied under the smart electricity big data environment and the training samples are selected using the incremental training dataset to make a short-term prediction of the millions of users’ smart meter electricity load, which verifies the feasibility and effectiveness of the proposed model. At last, comparing with other commonly used short-term load forecasting algorithms, the experimental results show that SBPOS-ELM algorithm has higher accuracy and operation efficiency.

Closed-Loop Elastic Demand Control under Dynamic Pricing Program in Smart Microgrid Using Super Twisting Sliding Mode Controller.

Electricity demand is rising due to industrialisation, population growth and economic development. To meet this rising electricity demand, towns are renovated by smart cities, where the internet of things enabled devices, communication technologies, dynamic pricing servers and renewable energy sources are integrated. Internet of things (IoT) refers to scenarios where network connectivity and computing capability is extended to objects, sensors and other items not normally considered computers. IoT allows these devices to generate, exchange and consume data without or with minimum human intervention. This integrated environment of smart cities maintains a balance between demand and supply. In this work, we proposed a closed-loop super twisting sliding mode controller (STSMC) to handle the uncertain and fluctuating load to maintain the balance between demand and supply persistently. Demand-side load management (DSLM) consists of agents-based demand response (DR) programs that are designed to control, change and shift the load usage pattern according to the price of the energy of a smart grid community. In smart grids, evolved DR programs are implemented which facilitate controlling of consumer demand by effective regulation services. The DSLM under price-based DR programs perform load shifting, peak clipping and valley filling to maintain the balance between demand and supply. We demonstrate a theoretical control approach for persistent demand control by dynamic price-based closed-loop STSMC. A renewable energy integrated microgrid scenario is discussed numerically to show that the demand of consumers can be controlled through STSMC, which regulates the electricity price to the DSLM agents of the smart grid community. The overall demand elasticity of the current study is represented by a first-order dynamic price generation model having a piece-wise linear price-based DR program. The simulation environment for this whole scenario is developed in MATLAB/Simulink. The simulations validate that the closed-loop price-based elastic demand control technique can trace down the generation of a renewable energy integrated microgrid.

An intelligent navigation experimental system based on multi-mode fusion

At present, most experimental teaching systems lack guidance of an operator, and thus users often do not know what to do during an experiment. The user load is therefore increased, and the learning efficiency of the students is decreased. To solve the problem of insufficient system interactivity and guidance, an experimental navigation system based on multi-mode fusion is proposed in this paper. The system first obtains user information by sensing the hardware devices, intelligently perceives the user intention and progress of the experiment according to the information acquired, and finally carries out a multi-modal intelligent navigation process for users. As an innovative aspect of this study, an intelligent multi-mode navigation system is used to guide users in conducting experiments, thereby reducing the user load and enabling the users to effectively complete their experiments. The results prove that this system can guide users in completing their experiments, and can effectively reduce the user load during the interaction process and improve the efficiency.

MPDHD-A Handover Decision Scheme for Seamless Connectivity in Wireless Network

Handover is one of the major concerns arising in wireless network due to increasing demand of services by the customers. Different studies have been performed to attain a seamless handover. Researchers are implementing novel technologies so that efficient decision can be made to maintain effective communication. Multilayer feed forward artificial neural network has been implemented in a recent study in which Received Signal strength indicator (RSSI), monetary cost, Data rate and Velocity of mobile users in the network are taken into account for handover decision in wireless network. Due to several limitations of this technique, a novel method- Multiple parameters dependent Handover decision (MPDHD) is presented in which Sugeno fuzzy model is amalgamated with neural network to form an intelligent system. In the system, neural network is trained by the fuzzy model which reduced the complexity of the existing work. Also along with the parameters used in existing work, a new user metric-Load is introduced to check the availability of the base station with minimum load of users connected to it. The simulation of the proposed work is carried out in the MATLAB environment. From, the experimental results, it is concluded that MPDHD is better than existing approaches and reduced the handover probability in the network.