peer reviewedThe power system field is presently facing an explosive growth of data. The data mining (DM) approach provides tools for making explicit some implicit subtle structure in data. Applying data mining to power system engineering is an iterative and interactive process, requiring an acquainted user with the application specifics. The paper describes data mining tools like statistical methos, visualization, machine learning and neural networks, exemplifying by results obtained with a DM software developed for dynamic security assessment studies. Power system engineering applications where data mining would be useful are reviewed in the second part of the paper
The increasing amount of data has taken a significant space of our expensive hard-disks. People are ...
In this paper, we present a flexible software environment that facilitates the use of machine learni...
Abstract: When it comes to software development, software companies generate enormous amounts of dat...
The growth of available data in the electric power industry motivates the adoption of data mining te...
This Special Issue was intended as a forum to advance research and apply machine-learning and data-m...
Presently power system operation produces huge volumes of data that is still treated in a very limit...
With increasingly environmental constraints the modern Power and Energy Systems are experiencing hug...
Abstract — This paper presents a power system dynamic scenario vi-sualization tool which is used to ...
Abstract—This paper is the outcome of an attempt in mining recorded power system operational data in...
Recent advances in computing technologies and the availability of large amounts of heterogeneous dat...
The complexity of electric power networks from generation, transmission, and distribution stations i...
peer reviewedThe paper discusses a framework that uses machine learning and other automatic-learning...
In this paper, we adopt a novel applied approach to fault analysis based on data mining theory. In o...
The study attempted to understand the condition of the insulation system and to classify the data ac...
The corrosive volume of available data in electric power systems motivate the adoption of data minin...
The increasing amount of data has taken a significant space of our expensive hard-disks. People are ...
In this paper, we present a flexible software environment that facilitates the use of machine learni...
Abstract: When it comes to software development, software companies generate enormous amounts of dat...
The growth of available data in the electric power industry motivates the adoption of data mining te...
This Special Issue was intended as a forum to advance research and apply machine-learning and data-m...
Presently power system operation produces huge volumes of data that is still treated in a very limit...
With increasingly environmental constraints the modern Power and Energy Systems are experiencing hug...
Abstract — This paper presents a power system dynamic scenario vi-sualization tool which is used to ...
Abstract—This paper is the outcome of an attempt in mining recorded power system operational data in...
Recent advances in computing technologies and the availability of large amounts of heterogeneous dat...
The complexity of electric power networks from generation, transmission, and distribution stations i...
peer reviewedThe paper discusses a framework that uses machine learning and other automatic-learning...
In this paper, we adopt a novel applied approach to fault analysis based on data mining theory. In o...
The study attempted to understand the condition of the insulation system and to classify the data ac...
The corrosive volume of available data in electric power systems motivate the adoption of data minin...
The increasing amount of data has taken a significant space of our expensive hard-disks. People are ...
In this paper, we present a flexible software environment that facilitates the use of machine learni...
Abstract: When it comes to software development, software companies generate enormous amounts of dat...