Monitoring Time-Varying Network Streams Using State-Space Models

Cao, Jin; Chen, A.; Bu, Tian; Buvaneswari, Arumugam

doi:10.1109/infcom.2009.5062219

Cited by 9 publications

(4 citation statements)

References 8 publications

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“…The SSM in [19] does not require offline training and updates the model parameters in real time as each reading comes in. This method has been successfully applied to the online monitoring of time‐varying network streams [4].…”

Section: Short‐term Load Forecasting Methodologymentioning

confidence: 99%

Demand Forecasting in Smart Grids

Mirowski¹,

Sining²,

Ho³

et al. 2014

Bell Labs Tech. J.

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Data analytics in smart grids can be leveraged to channel the data downpour from individual meters into knowledge valuable to electric power utilities and end‐consumers. Short‐term load forecasting (STLF) can address issues vital to a utility but it has traditionally been done mostly at system (city or country) level. In this case study, we exploit rich, multi‐year, and high‐frequency annotated data collected via a metering infrastructure to perform STLF on aggregates of power meters in a mid‐sized city. For smart meter aggregates complemented with geo‐specific weather data, we benchmark several state‐of‐the‐art forecasting algorithms, including kernel methods for nonlinear regression, seasonal and temperature‐adjusted auto‐regressive models, exponential smoothing and state‐space models. We show how STLF accuracy improves at larger meter aggregation (at feeder, substation, and system‐wide level). We provide an overview of our algorithms for load prediction and discuss system performance issues that impact real time STLF. © 2014 Alcatel‐Lucent.

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Section: Short‐term Load Forecasting Methodologymentioning

confidence: 99%

Demand Forecasting in Smart Grids

Mirowski¹,

Sining²,

Ho³

et al. 2014

Bell Labs Tech. J.

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“…The cumulative sum (CUSUM) method is a simple nonparametric statistical technique that can be used for detecting abrupt change points in time series [47]. This method has been shown to be computationally inexpensive while remaining statistically optimal [48]. For a given time series, x 1 , x 2, .…”

Section: Change Point Analysismentioning

confidence: 99%

A Remote Sensing-Based Assessment of Water Resources in the Arabian Peninsula

Wehbe

Temimi

2021

Remote Sensing

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A better understanding of the spatiotemporal distribution of water resources is crucial for the sustainable development of hyper-arid regions. Here, we focus on the Arabian Peninsula (AP) and use remotely sensed data to (i) analyze the local climatology of total water storage (TWS), precipitation, and soil moisture; (ii) characterize their temporal variability and spatial distribution; and (iii) infer recent trends and change points within their time series. Remote sensing data for TWS, precipitation, and soil moisture are obtained from the Gravity Recovery and Climate Experiment (GRACE), the Tropical Rainfall Measuring Mission (TRMM), and the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), respectively. The study relies on trend analysis, the modified Mann–Kendall test, and change point detection statistics. We first derive 10-year (2002–2011) seasonal averages from each of the datasets and intercompare their spatial organization. In the absence of large-scale in situ data, we then compare trends from GRACE TWS retrievals to in situ groundwater observations locally over the subdomain of the United Arab Emirates (UAE). TWS anomalies vary between −6.2 to 3.2 cm/month and −6.8 to −0.3 cm/month during the winter and summer periods, respectively. Trend analysis shows decreasing precipitation trends (−2.3 × 10−4 mm/day) spatially aligned with decreasing soil moisture trends (−1.5 × 10−4 g/cm3/month) over the southern part of the AP, whereas the highest decreasing TWS trends (−8.6 × 10−2 cm/month) are recorded over areas of excessive groundwater extraction in the northern AP. Interestingly, change point detection reveals increasing precipitation trends pre- and post-change point breaks over the entire AP region. Significant spatial dependencies are observed between TRMM and GRACE change points, particularly over Yemen during 2010, revealing the dominant impact of climatic changes on TWS depletion.

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“…The plausibility that observations within a batch can be modeled as a stationary normal process is a key assumption with this method. Cao et al developed a state‐space model to describe a time‐varying data network stream. After an application‐dependent transformation, monitoring statistics are considered as following a normal distribution with a constant variance.…”

Section: Monitoring Reliability Of Data Networkmentioning

confidence: 99%