Simulation Study on Clustering Approaches for Short-Term Electricity Forecasting
Joint Authors
Gajowniczek, Krzysztof
Ząbkowski, Tomasz
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-21, 21 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-04-26
Country of Publication
Egypt
No. of Pages
21
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Gajowniczek, Krzysztof& Ząbkowski, Tomasz. 2018. Simulation Study on Clustering Approaches for Short-Term Electricity Forecasting. Complexity،Vol. 2018, no. 2018, pp.1-21.
https://search.emarefa.net/detail/BIM-1133730
Modern Language Association (MLA)
Gajowniczek, Krzysztof& Ząbkowski, Tomasz. Simulation Study on Clustering Approaches for Short-Term Electricity Forecasting. Complexity No. 2018 (2018), pp.1-21.
https://search.emarefa.net/detail/BIM-1133730
American Medical Association (AMA)
Gajowniczek, Krzysztof& Ząbkowski, Tomasz. Simulation Study on Clustering Approaches for Short-Term Electricity Forecasting. Complexity. 2018. Vol. 2018, no. 2018, pp.1-21.
https://search.emarefa.net/detail/BIM-1133730
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1133730