File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1109/PowerTech46648.2021.9494815
- Scopus: eid_2-s2.0-85112367981
- WOS: WOS:000848778000066
Supplementary
- Citations:
- Appears in Collections:
Conference Paper: Probabilistic Aggregated Load Forecasting with Fine-grained Smart Meter Data
Title | Probabilistic Aggregated Load Forecasting with Fine-grained Smart Meter Data |
---|---|
Authors | |
Keywords | load aggregation Probabilistic load forecasting quantile regression smart meter |
Issue Date | 2021 |
Citation | 2021 IEEE Madrid PowerTech, PowerTech 2021 - Conference Proceedings, 2021, article no. 9494815 How to Cite? |
Abstract | Probabilistic load forecasting (PLF) has been extensively studied to characterize the uncertainties of future loads. Traditional PLF is implemented based on the historical load data itself and other relevant factors. However, the prevalence of smart meters provides more fine-grained consumption information. This paper proposes a novel probabilistic aggregated load forecasting algorithm that makes full use of fine-grained smart meter data. It first applies clustering-based methods for point aggregated load forecasting. By varying clustering algorithms, multiple point forecasts can be obtained. On this basis, different quantile regression models are implemented to combine these point forecasts in order to form the final probabilistic forecasts. Case studies on a real-world dataset demonstrate the superiority of our proposed method. |
Persistent Identifier | http://hdl.handle.net/10722/308926 |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Wang, Yi | - |
dc.contributor.author | Von Krannichfeldt, Leandro | - |
dc.contributor.author | Hug, Gabriela | - |
dc.date.accessioned | 2021-12-08T07:50:25Z | - |
dc.date.available | 2021-12-08T07:50:25Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | 2021 IEEE Madrid PowerTech, PowerTech 2021 - Conference Proceedings, 2021, article no. 9494815 | - |
dc.identifier.uri | http://hdl.handle.net/10722/308926 | - |
dc.description.abstract | Probabilistic load forecasting (PLF) has been extensively studied to characterize the uncertainties of future loads. Traditional PLF is implemented based on the historical load data itself and other relevant factors. However, the prevalence of smart meters provides more fine-grained consumption information. This paper proposes a novel probabilistic aggregated load forecasting algorithm that makes full use of fine-grained smart meter data. It first applies clustering-based methods for point aggregated load forecasting. By varying clustering algorithms, multiple point forecasts can be obtained. On this basis, different quantile regression models are implemented to combine these point forecasts in order to form the final probabilistic forecasts. Case studies on a real-world dataset demonstrate the superiority of our proposed method. | - |
dc.language | eng | - |
dc.relation.ispartof | 2021 IEEE Madrid PowerTech, PowerTech 2021 - Conference Proceedings | - |
dc.subject | load aggregation | - |
dc.subject | Probabilistic load forecasting | - |
dc.subject | quantile regression | - |
dc.subject | smart meter | - |
dc.title | Probabilistic Aggregated Load Forecasting with Fine-grained Smart Meter Data | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/PowerTech46648.2021.9494815 | - |
dc.identifier.scopus | eid_2-s2.0-85112367981 | - |
dc.identifier.spage | article no. 9494815 | - |
dc.identifier.epage | article no. 9494815 | - |
dc.identifier.isi | WOS:000848778000066 | - |