相关论文: PQStream: A Data Stream Architecture for Electrica…
Data quality is fundamental to modern data science workflows, where data continuously flows as unbounded streams feeding critical downstream tasks, from elementary analytics to advanced artificial intelligence models. Existing data quality…
The growing integration of power electronic-based technologies has increased the necessity of power quality (PQ) monitoring in transmission systems. Although large datasets are collected by operators, their use is typically limited to…
Hospitals around the world collect massive amounts of physiological data from their patients every day. Recently, there has been an increase in research interest to subject this data to statistical analysis to gain more insights and provide…
The quality of electricity system modelling heavily depends on the input data used. Although a lot of data is publicly available, it is often dispersed, tedious to process and partly contains errors. We argue that a central provision of…
Today, we have to deal with many data (Big data) and we need to make decisions by choosing an architectural framework to analyze these data coming from different area. Due to this, it become problematic when we want to process these data,…
The prediction of electrical power in combined cycle power plants is a key challenge in the electrical power and energy systems field. This power output can vary depending on environmental variables, such as temperature, pressure, and…
The analogous deployment of phase measurement units (PMUs), the increase of data quantum and the deregulation of energy market, all call for the robust state evaluation in large scale power systems. Implementing model based estimators is…
During the production, distribution, and consumption of energy, a large quantity of data is generated. For efficiently using of energy resources other supplementary data such as building information, weather, and environmental data etc. are…
Increased adoption and deployment of phasor measurement units (PMU) has provided valuable fine-grained data over the grid. Analysis over these data can provide insight into the health of the grid, thereby improving control over operations.…
Traffic and channel-data rate combined with the stream oriented methodology can provide a scheme for offering optimized and guaranteed QoS. In this work a stream oriented modeled scheme is proposed based on each node's self-scheduling…
An algorithm for Electric Power System (EPS) quantum/relativistic security and efficiency computation for a day-ahead via perturbative renormalization of the EPS, finding the computation flowcharts, verification and validation is built in…
The challenges of power quality are an emerging topic for the past couple of years due to massive changes occurring in low voltage distribution networks, being even more emphasized in the years marked by the novel COVID-19 disease affecting…
This paper introduces H-STREAM, a big stream/data processing pipelines evaluation engine that proposes stream processing operators as micro-services to support the analysis and visualisation of Big Data streams stemming from IoT (Internet…
Proper quality control (QC) is time consuming when working with large-scale medical imaging datasets, yet necessary, as poor-quality data can lead to erroneous conclusions or poorly trained machine learning models. Most efforts to reduce…
Data stream processing systems (DSPSs) enable users to express and run stream applications to continuously process data streams. To achieve real-time data analytics, recent researches keep focusing on optimizing the system latency and…
The operation of water distribution networks simply aims at efficiently delivering consumers adequate water while maintaining safe water quality (WQ). However, this process entails a multi-scale interplay between hydraulic and WQ dynamics…
The transformation of the electrical energy system attracts much attention in diverse research communities. Novel approaches for modeling, control, and power grid architectures are widely proposed. Data from actual power system operation…
Ever-increasing amounts of data and requirements to process them in real time lead to more and more analytics platforms and software systems being designed according to the concept of stream processing. A common area of application is the…
This paper explores the use of quantum computing, specifically the use of HHL and VQLS algorithms, to solve optimal power flow problem in electrical grids. We investigate the effectiveness of these quantum algorithms in comparison to…
Power quality disturbances (PQDs) significantly impact the stability and reliability of power systems, necessitating accurate and efficient detection and recognition methods. While numerous classical algorithms for PQDs detection and…