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Article: Locational Marginal Price Forecasting: A Componential and Ensemble Approach

TitleLocational Marginal Price Forecasting: A Componential and Ensemble Approach
Authors
Keywordselectricity market
Locational marginal price
model stacking%
short-term forecasting
tree-based regression
Issue Date2020
Citation
IEEE Transactions on Smart Grid, 2020, v. 11, n. 5, p. 4555-4564 How to Cite?
AbstractShort-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 Identifierhttp://hdl.handle.net/10722/308823
ISSN
2023 Impact Factor: 8.6
2023 SCImago Journal Rankings: 4.863
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZheng, Kedi-
dc.contributor.authorWang, Yi-
dc.contributor.authorLiu, Kai-
dc.contributor.authorChen, Qixin-
dc.date.accessioned2021-12-08T07:50:12Z-
dc.date.available2021-12-08T07:50:12Z-
dc.date.issued2020-
dc.identifier.citationIEEE Transactions on Smart Grid, 2020, v. 11, n. 5, p. 4555-4564-
dc.identifier.issn1949-3053-
dc.identifier.urihttp://hdl.handle.net/10722/308823-
dc.description.abstractShort-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.languageeng-
dc.relation.ispartofIEEE Transactions on Smart Grid-
dc.subjectelectricity market-
dc.subjectLocational marginal price-
dc.subjectmodel stacking%-
dc.subjectshort-term forecasting-
dc.subjecttree-based regression-
dc.titleLocational Marginal Price Forecasting: A Componential and Ensemble Approach-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TSG.2020.2985070-
dc.identifier.scopuseid_2-s2.0-85090131728-
dc.identifier.volume11-
dc.identifier.issue5-
dc.identifier.spage4555-
dc.identifier.epage4564-
dc.identifier.eissn1949-3061-
dc.identifier.isiWOS:000562305000076-

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