Detection, mining and forecasting of impact load in power load forecasting

Wang, Jianzhou; Ma, Zhixin; Lian, Li

doi:10.1016/j.amc.2004.08.030

Cited by 6 publications

(2 citation statements)

References 5 publications

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“…Often it is difficult to understand the natural states of these systems ( [1], [2], [3]). Better understanding of states of the network traffic and how these states change is accomplished by analyzing the network traffic data and by forecasting the impact of load on the network traffic flow [4]. In network performance analysis, the traffic data is measured over time and statistical methods are often used to detect whether thresholds are exceeded based on the standard deviations of observed variables.…”

Section: Introductionmentioning

confidence: 99%

Detection of stationary network load increase using univariate network aggregate traffic data by dynamic PCA

Xie

Ławniczak

2011

2011 IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA)

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Network operators are now facing bandwidth outages as well as a growing pressure to ensure good Quality of Service (QoS). An important practical issue for network service providers is to pay close attention to the load changes of network traffic, in particular, the stationary increase of load from a normal demand. Many network monitoring applications and performance analysis tools are based on the study of an aggregate measure of network traffic, e.g. number of packets in transit (NPT), which is a long-term univariate time series. To classify this type of network traffic data and detect any increase of network source load, we propose a dynamic principal component analysis (PCA) method, first to extract data features and then to detect a stationary load increase of network traffic. The proposed detection schemes are based on either the major or the minor principal components of network traffic data. To demonstrate the applications of the proposed feature extraction method and the detection schemes, we applied them to network traffic data simulated from the packet switching network (PSN) model. Additionally, we propose a combined detection scheme that uses both the major and the minor principal components. The proposed detection schemes, based on dynamic PCA, show enhanced performance in detecting an increase of network load for the simulated network traffic data. These results offer a new feature extraction method based on dynamic PCA that creates additional feature variables for event detection in a univariate time series.

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Section: Introductionmentioning

confidence: 99%

Detection of stationary network load increase using univariate network aggregate traffic data by dynamic PCA

Xie

Ławniczak

2011

2011 IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA)

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show abstract

“…The dynamics of many complex systems such as computer networks, financial systems, transportation systems, or power systems are mathematically intractable due to their complexity ( [1][2][3]). Better understanding of states of the complex systems and how these states change is accomplished by analyzing the data coming from the underlying complex systems [4]. In network system performance analysis, the traffic data is measured over time and statistical quality control techniques such as process control are often applied to detect whether thresholds are exceeded based on the standard deviations of observed variables.…”

Section: Introductionmentioning

confidence: 99%

Study of Stationary Load Increase of Computer-Network Traffic via Dynamic Principal-Component Analysis

Xie

Ławniczak

2012

ISRN Computational Mathematics

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Many network monitoring applications and performance analysis tools are based on the study of an aggregate measure of network traffic, for example, number of packets in transit (NPT). The simulation modeling and analysis of this type of performance indicator enables a theoretical investigation of the underlying complex system through different combination of network setups such as routing algorithms, network source loads or network topologies. To detect stationary increase of network source load, we propose a dynamic principal component analysis (PCA) method, first to extract data features and then to detect a stationary load increase. The proposed detection schemes are based on either the major or the minor principal components of network traffic data. To demonstrate the applications of the proposed method, we first applied them to some synthetic data and then to network traffic data simulated from the packet switching network (PSN) model. The proposed detection schemes, based on dynamic PCA, show enhanced performance in detecting an increase of network load for the simulated network traffic data. These results show usefulness of a new feature extraction method based on dynamic PCA that creates additional feature variables for event detection in a univariate time series.

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Research Trends in Power System Planning

Seifi

Sepasian²

2011

Power Systems

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Detection, mining and forecasting of impact load in power load forecasting

Cited by 6 publications

References 5 publications

Detection of stationary network load increase using univariate network aggregate traffic data by dynamic PCA

Detection of stationary network load increase using univariate network aggregate traffic data by dynamic PCA

Study of Stationary Load Increase of Computer-Network Traffic via Dynamic Principal-Component Analysis

Research Trends in Power System Planning

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