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This dataset contains daily maximum load data with the average demand, customer count and PV capacity at two substations Arkana and Muchea, Western Australia used in the accepted IEEE Transactions on Power Systemspaper titled “The Use of Extreme Value Theory for Forecasting Long-Term Substation Maximum Electricity Demand” by Li and Jones (2019).  The dataset spans from 01/01/2008 to 30/06/2022, part history (01/01/2008 to 16/09/2018) and part forecast (17/09/2018 to 30/06/2022).  The dataset is beneficial to various research such as long-term load forecast.

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This dataset contains daily maximum load data with the average demand, customer count and PV capacity at two substations Arkana and Muchea, Western Australia used in the accepted IEEE Transactions on Power Systemspaper titled “The Use of Extreme Value Theory for Forecasting Long-Term Substation Maximum Electricity Demand” by Li and Jones (2019).  The dataset spans from 01/01/2008 to 30/06/2022, part history (01/01/2008 to 16/09/2018) and part forecast (17/09/2018 to 30/06/2022).  The dataset is beneficial to various research such as long-term load forecast.

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248 Views

This dataset contains a sequence of network events extracted from a commercial network monitoring platform, Spectrum, by CA. These events, which are categorized by their severity, cover a wide range of events, from a link state change up to critical usages of CPU by certain devices. Regarding the layers they cover, they are focused on the physical, network and application layer. As such, the whole set gives a complete overview of the network’s general state.

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702 Views

This dataset details the state machine based experiments of PowerWatch.

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257 Views

 Measurements collected from R1 for root cause analyses of the network service states defined from quality and service design perspectives

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596 Views

The data through Figure 1~3 in the manuscript "Spatio-Temporal Correlation Analysis of Online Monitoring Data for Anomaly Detection and Location in Distribution Networks".

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244 Views

Accurate short-term load forecasting (STLF) plays an increasingly important role in reliable and economical power system operations. This dataset contains The University of Texas at Dallas (UTD) campus load data with 13 buildings, together with 20 weather and calendar features. The dataset spans from 01/01/2014 to 12/31/2015 with an hourly resolution. The dataset is beneficial to various research such as STLF.

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2975 Views

In an aging population, the demand for nurse workers increases to care for elders. Helping nurse workers make their work more efficient, will help increase elders quality of life, as the nurses can focus their efforts on care activities instead of other activities such as documentation.
Activity Recognition can be used for this goal. If we can recognize what activity a nurse is engaged in, we can partially automate documentation process to reduce time spent on this task, monitor care plan compliance to assure that all care activities have been done for each elder, among others.

Last Updated On: 
Fri, 12/06/2019 - 03:40

This dataset used in the experiment of paper "Bus Ridesharing Scheduling Problem". This is a real-world bus ridesharing scheduling problem of Chengdu city in China, which includes 10 depots, 2,000 trips.

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353 Views

This is the dataset used in the experiment of paper "Bus Pooling: A Large-Scale Bus Ridesharing Service". The dataset contains 60,822,634 trajectory data from 11,922 Shanghai taxis from one day (Apr 1, 2018). The 100 groups of coordinate sets containing three coordinates as experimental samples are used to compare the effectiveness and efficiency of location-allocation algorithms.

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764 Views

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