F. Kaveh-Yazdy; S. Zarifzadeh
Abstract
Due to their structure and usage condition, water meters face degradation, breaking, freezing, and leakage problems. There are various studies intended to determine the appropriate time to replace degraded ones. Earlier studies have used several features, such as user meteorological parameters, usage ...
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Due to their structure and usage condition, water meters face degradation, breaking, freezing, and leakage problems. There are various studies intended to determine the appropriate time to replace degraded ones. Earlier studies have used several features, such as user meteorological parameters, usage conditions, water network pressure, and structure of meters to detect failed water meters. This article proposes a recommendation framework that uses registered water consumption values as input data and provides meter replacement recommendations. This framework takes time series of registered consumption values and preprocesses them in two rounds to extract effective features. Then, multiple un-/semi-supervised outlier detection methods are applied to the processed data and assigns outlier/normal labels to them. At the final stage, a hypergraph-based ensemble method receives the labels and combines them to discover the suitable label. Due to the unavailability of ground truth labeled data for meter replacement, we compare our method with respect to its FPR and two internal metrics: Dunn index and Davies-Bouldin Index. Results of our comparative experiments show that the proposed framework detects more compact clusters with smaller variance.
D.1. General
N. Bigdeli; H. Sadegh Lafmejani
Abstract
The renewable energy resources such as wind power have recently attracted more researchers’ attention. It is mainly due to the aggressive energy consumption, high pollution and cost of fossil fuels. In this era, the future fluctuations of these time series should be predicted to increase the reliability ...
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The renewable energy resources such as wind power have recently attracted more researchers’ attention. It is mainly due to the aggressive energy consumption, high pollution and cost of fossil fuels. In this era, the future fluctuations of these time series should be predicted to increase the reliability of the power network. In this paper, the dynamic characteristics and short-term predictability of hourly wind speed and power time series are investigated via nonlinear time series analysis methods such as power spectral density analysis, time series histogram, phase space reconstruction, the slope of integral sums, the method, the recurrence plot and the recurrence quantification analysis. Moreover, the interactive behavior of the wind speed and wind power time series is studied via the cross correlation, the cross and joint recurrence plots as well as the cross and joint recurrence quantification analyses. The results imply stochastic nature of these time series. Besides, a measure of the short-term mimic predictability of the wind speed and the underlying wind power has been derived for the experimental data of Spain’s wind farm.
