Name
Abstract | ||
|---|---|---|
The deployment of advanced metering infrastructures allows suppliers and consumers to better understand the utility supply and usage chain. Data from these systems are typically used to analyse utility usage in a large scale, but when observed at smaller scales, we can enable a number of interesting new application. In this work we use utility usage data collected from 300 households over three years and perform detailed analysis to understand per-household utility usage patterns. We show that per-household utility usage data introduces high variances and low correlations among different households even if they are co-located in similar geographical regions. Using our findings, we introduce AUUP, an adaptive utility usage prediction scheme that combines the output from different existing forecasting schemes to adaptively make smart small-scale utility usage predictions. Our evaluations show that AUUP effectively reduces the prediction errors of artificial neural networks, LMS and Kalman filter-based AR model prediction schemes. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1504/IJSNET.2016.076703 | IJSNet |
Keywords | Field | DocType |
household-scale utility management, adaptive utility usage prediction | Least mean squares filter,Data mining,Autoregressive model,Software deployment,Smart grid,Computer science,Kalman filter,Artificial intelligence,Usage data,Artificial neural network,Wireless sensor network,Machine learning | Journal |
Volume | Issue | ISSN |
20 | 4 | 1748-1279 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jong-Jun Park | 1 | 47 | 4.77 |
| Hyunhak Kim | 2 | 1 | 1.71 |
| Taewook Heo | 3 | 11 | 2.48 |
| Seung-Mok Yoo | 4 | 0 | 0.34 |
| JeongGil Ko | 5 | 674 | 64.60 |