Name
Abstract | ||
|---|---|---|
Advanced metering infrastructures such as smart metering have begun to attract increasing attention; a considerable body of research is currently focusing on load profiling and forecasting at different scales on the grid. Electricity time series clustering is an effective tool for identifying useful information in various practical applications, including the forecasting of electricity usage, which is important for providing more data to smart meters. This paper presents a comprehensive study of clustering methods for residential electricity demand profiles and further applications focused on the creation of more accurate electricity forecasts for residential customers. The contributions of this paper are threefold: (1) using data from 46 homes in Austin, Texas, the similarity measures from different time series are analyzed; (2) the optimal number of clusters for representing residential electricity use profiles is determined; and (3) an extensive load forecasting study using different segmentation-enhanced forecasting algorithms is undertaken. Finally, from the operator's perspective, the implications of the results are discussed in terms of the use of clustering methods for grouping electrical load patterns. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1155/2018/3683969 | COMPLEXITY |
Field | DocType | Volume |
Electricity demand,Electrical load,Industrial engineering,Electricity,Profiling (computer programming),Load forecasting,Artificial intelligence,Cluster analysis,Metering mode,Mathematics,Grid,Machine learning | Journal | 2018 |
ISSN | Citations | PageRank |
1076-2787 | 1 | 0.36 |
References | Authors | |
9 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Krzysztof Gajowniczek | 1 | 19 | 6.14 |
| Tomasz Zabkowski | 2 | 32 | 11.28 |