Name
Abstract | ||
|---|---|---|
This paper addresses the use of smart-home sensor streams for continuous prediction of energy loads of individual households which participate as an agent in local markets. We introduces a new device level energy consumption dataset recorded over three years wich includes high resolution energy measurements from electrical devices collected within a pilot program. Using data from that pilot, we analyze the applicability of various machine learning mechanisms for continuous load prediction. Specifically, we address short-term load prediction that is required for load balancing in electrical micro-grids. We report on the prediction performance and the computational requirements of a broad range of prediction mechanisms. Furthermore we present an architecture and experimental evaluation when this prediction is applied in the stream. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Learning | Architecture,Electrical devices,Load balancing (computing),Data stream,Real-time computing,Home automation,Artificial intelligence,Energy consumption,Mathematics,Machine learning,Embedded system |
DocType | Volume | Citations |
Journal | abs/1708.04613 | 1 |
PageRank | References | Authors |
0.43 | 11 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christoph Doblander | 1 | 48 | 9.05 |
| Martin Strohbach | 2 | 1 | 0.77 |
| Holger Ziekow | 3 | 150 | 18.30 |
| Hans-Arno Jacobsen | 4 | 2989 | 231.63 |