Nyquist-based adaptive sampling rate for wind measurement under varying wind conditions

Korprasertsak, Natapol; Leephakpreeda, Thananchai

doi:10.1016/j.renene.2017.12.018

Cited by 7 publications

(5 citation statements)

References 19 publications

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“…Other algorithms, relying on a pernode condition, i.e. avoiding the problems of imbalance, are presented in [18]- [22]. The proposed methods apart from the one in [22] do not use Fourier transforms for data reduction and none of them use the reduced data by Fourier processing as a means to accelerate inference or model training at a central controller.…”

Section: Related Workmentioning

confidence: 99%

“…avoiding the problems of imbalance, are presented in [18]- [22]. The proposed methods apart from the one in [22] do not use Fourier transforms for data reduction and none of them use the reduced data by Fourier processing as a means to accelerate inference or model training at a central controller. The approach in [22] relies on a heuristic Nyquist rate based sampling that is somewhat less flexible than our approach.…”

Section: Related Workmentioning

confidence: 99%

“…The proposed methods apart from the one in [22] do not use Fourier transforms for data reduction and none of them use the reduced data by Fourier processing as a means to accelerate inference or model training at a central controller. The approach in [22] relies on a heuristic Nyquist rate based sampling that is somewhat less flexible than our approach. While there is very little research on the use of Fourier transforms for data collection in distributed systems, previous research has explored its uses for correlation approximation and similarity search in databases that is comparable to the idea we propose [23]- [25].…”

Section: Related Workmentioning

confidence: 99%

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Fast-Fourier-Forecasting Resource Utilisation in Distributed Systems

Pritz¹,

Perez²,

Leung³

2020

Preprint

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Distributed computing systems often consist of hundreds of nodes (machines), executing tasks with different resource requirements. Efficient resource provisioning and task scheduling in such systems are non-trivial and require close monitoring and accurate forecasting of the state of the system, specifically resource utilisation at its constituent machines. Two challenges present themselves towards these objectives. First, collecting monitoring data entails substantial communication overhead. This overhead can be prohibitively high, especially in networks where bandwidth is limited. Second, forecasting models to predict resource utilisation should be accurate and also need to exhibit high inference speed. Mission critical scheduling and resource allocation algorithms use these predictions and rely on their immediate availability. To address the first challenge, we present a communicationefficient data collection mechanism. Resource utilisation data is collected at the individual machines in the system and transmitted to a central controller in batches. Each batch is processed by an adaptive data-reduction algorithm based on Fourier transforms and truncation in the frequency domain. We show that the proposed mechanism leads to a significant reduction in communication overhead while incurring only minimal error and adhering to accuracy guarantees. To address the second challenge, we propose a deep learning architecture using complex Gated Recurrent Units to forecast resource utilisation. This architecture is directly integrated with the above data collection mechanism to improve inference speed of the presented forecasting model. Using two real-world datasets, we demonstrate the effectiveness of our approach, both in terms of forecasting accuracy and inference speed. Our approach resolves several challenges encountered in resource provisioning frameworks and can also be generically applied to other forecasting problems.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Fast-Fourier-Forecasting Resource Utilisation in Distributed Systems

Pritz¹,

Perez²,

Leung³

2020

Preprint

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“…in a given cycle period T is determined based on the Nyquist sampling theorem [8]. The Fourier transformation is applied to time-series wind speed data in order to acknowledge its frequency component.…”

Section: Real Time Determination Of Sampling Rate For Wind Measurementmentioning

confidence: 99%

“…The Nyquist rate   N m s f , are different at each cycle period mT . With the statistical wind analysis in[8,9], the mean wind speed v , standard deviation  , power density P , and annual energy production AEP are determined in Eq. (1), Eq.…”

mentioning

confidence: 99%

Improving Accuracy of Wind Analysis with Multiple Sampling Rates of Wind Measurement

Korprasertsak

Leephakpreeda

2019

E3S Web Conf.

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The sampling rate in wind measurement has influences on accuracy of wind analysis. Missing wind data problem can be prevented with high sampling rates. However, a lot of data are unnecessarily required in wind analysis. In this work, optimal sampling rates are determined in real time by the Nyquist sampling theorem according to varying wind conditions. It is found that all statistical results in wind analysis are obtained with percentage errors of less than 1% while the amount of wind data is decreased significantly from the benchmark at fixed sampling rate of 10 Hz.

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Robust short-term prediction of wind power generation under uncertainty via statistical interpretation of multiple forecasting models

Korprasertsak

Leephakpreeda

2019

Energy

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Nyquist-based adaptive sampling rate for wind measurement under varying wind conditions

Cited by 7 publications

References 19 publications

Fast-Fourier-Forecasting Resource Utilisation in Distributed Systems

Fast-Fourier-Forecasting Resource Utilisation in Distributed Systems

Improving Accuracy of Wind Analysis with Multiple Sampling Rates of Wind Measurement

Robust short-term prediction of wind power generation under uncertainty via statistical interpretation of multiple forecasting models

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