Name
Abstract | ||
|---|---|---|
In the future Internet of Things devices will generate massive amounts of data that will flow to enterprise systems and provide a timely view on the execution of business processes. Being able to estimate data generated by devices may have significant effects on planning and execution of business applications. We present some methodologies for mining data gathered from devices in the energy domain i.e. web service enabled smart meters and home appliances. We present here an approach that realise short-term prediction based on neural networks or support vector machines. We consider detailed information about energy consumption coming from service-enabled devices in the broader smart grid envisioned future infrastructure. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/UKSIM.2010.89 | UKSim |
Keywords | Field | DocType |
Web services,business data processing,data mining,energy consumption,energy management systems,energy measurement,grid computing,smart power grids,support vector machines,Internet of Things devices,Web service,business processes,data mining,energy consumption,energy measurement prediction,enterprise systems,home appliances,neural networks,service enabled devices,smart grid,smart meters,support vector machines,Internet of Things,complex event processing,event prediction,short--term prognosis,smart grid | Enterprise system,Grid computing,Smart grid,Systems engineering,Business process,Computer science,Computer network,Web service,Energy consumption,Service-oriented architecture,The Internet | Conference |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Domnic Savio | 1 | 475 | 36.25 |
| Lubomir Karlik | 2 | 0 | 0.34 |
| Stamatis Karnouskos | 3 | 1094 | 119.36 |