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- Publisher Website: 10.1109/TSG.2020.2985070
- Scopus: eid_2-s2.0-85090131728
- WOS: WOS:000562305000076
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Article: Locational Marginal Price Forecasting: A Componential and Ensemble Approach
Title | Locational Marginal Price Forecasting: A Componential and Ensemble Approach |
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Authors | |
Keywords | electricity market Locational marginal price model stacking% short-term forecasting tree-based regression |
Issue Date | 2020 |
Citation | IEEE Transactions on Smart Grid, 2020, v. 11, n. 5, p. 4555-4564 How to Cite? |
Abstract | Short-term locational marginal price (LMP) forecasting is the traditional problem of market participants and other institutions maximizing their profit. Most electricity market organizers in the world release the data of LMP along with its three components, i.e., the energy, congestion, and loss components. The series of the three components have their own patterns and driving factors, and can be utilized to improve the accuracy of LMP forecasting. However, most existing studies have focused on direct LMP forecasting and have barely noticed this characteristic. In this paper, we aim to bridge the gap between the released data of the three components and LMP forecasting through a componential and ensemble approach. Three individual forecasting models are selected and trained for these components, and an ensemble framework that stacks the summation LMP results and the direct results is proposed to enhance the overall accuracy and robustness. Numerical experiments with real market data are conducted to show the good performance of this novel approach. |
Persistent Identifier | http://hdl.handle.net/10722/308823 |
ISSN | 2023 Impact Factor: 8.6 2023 SCImago Journal Rankings: 4.863 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Zheng, Kedi | - |
dc.contributor.author | Wang, Yi | - |
dc.contributor.author | Liu, Kai | - |
dc.contributor.author | Chen, Qixin | - |
dc.date.accessioned | 2021-12-08T07:50:12Z | - |
dc.date.available | 2021-12-08T07:50:12Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | IEEE Transactions on Smart Grid, 2020, v. 11, n. 5, p. 4555-4564 | - |
dc.identifier.issn | 1949-3053 | - |
dc.identifier.uri | http://hdl.handle.net/10722/308823 | - |
dc.description.abstract | Short-term locational marginal price (LMP) forecasting is the traditional problem of market participants and other institutions maximizing their profit. Most electricity market organizers in the world release the data of LMP along with its three components, i.e., the energy, congestion, and loss components. The series of the three components have their own patterns and driving factors, and can be utilized to improve the accuracy of LMP forecasting. However, most existing studies have focused on direct LMP forecasting and have barely noticed this characteristic. In this paper, we aim to bridge the gap between the released data of the three components and LMP forecasting through a componential and ensemble approach. Three individual forecasting models are selected and trained for these components, and an ensemble framework that stacks the summation LMP results and the direct results is proposed to enhance the overall accuracy and robustness. Numerical experiments with real market data are conducted to show the good performance of this novel approach. | - |
dc.language | eng | - |
dc.relation.ispartof | IEEE Transactions on Smart Grid | - |
dc.subject | electricity market | - |
dc.subject | Locational marginal price | - |
dc.subject | model stacking% | - |
dc.subject | short-term forecasting | - |
dc.subject | tree-based regression | - |
dc.title | Locational Marginal Price Forecasting: A Componential and Ensemble Approach | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TSG.2020.2985070 | - |
dc.identifier.scopus | eid_2-s2.0-85090131728 | - |
dc.identifier.volume | 11 | - |
dc.identifier.issue | 5 | - |
dc.identifier.spage | 4555 | - |
dc.identifier.epage | 4564 | - |
dc.identifier.eissn | 1949-3061 | - |
dc.identifier.isi | WOS:000562305000076 | - |