A Two-Stage Multistep-Ahead Electricity Load Forecasting Scheme Based on LightGBM and Attention-BiLSTM
DC Field | Value | Language |
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dc.contributor.author | Park, Jinwoong | - |
dc.contributor.author | Hwang, Eenjun | - |
dc.date.accessioned | 2022-02-16T09:42:23Z | - |
dc.date.available | 2022-02-16T09:42:23Z | - |
dc.date.created | 2022-01-19 | - |
dc.date.issued | 2021-11 | - |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/135968 | - |
dc.description.abstract | An efficient energy operation strategy for the smart grid requires accurate day-ahead electricity load forecasts with high time resolutions, such as 15 or 30 min. Most high-time resolution electricity load prediction techniques deal with a single output prediction, so their ability to cope with sudden load changes is limited. Multistep-ahead forecasting addresses this problem, but conventional multistep-ahead prediction models suffer from deterioration in prediction performance as the prediction range is expanded. In this paper, we propose a novel two-stage multistep-ahead forecasting model that combines a single-output forecasting model and a multistep-ahead forecasting model to solve the aforementioned problem. In the first stage, we perform a single-output prediction based on recent electricity load data using a light gradient boosting machine with time-series cross-validation, and feed it to the second stage. In the second stage, we construct a multistep-ahead forecasting model that applies an attention mechanism to sequence-to-sequence bidirectional long short-term memory (S2S ATT-BiLSTM). Compared to the single S2S ATT-BiLSTM model, our proposed model achieved improvements of 3.23% and 4.92% in mean absolute percentage error and normalized root mean square error, respectively. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.subject | NEURAL-NETWORK | - |
dc.subject | FEATURE-SELECTION | - |
dc.subject | SVR MODEL | - |
dc.subject | HYBRID | - |
dc.subject | DECOMPOSITION | - |
dc.subject | PREDICTION | - |
dc.subject | ALGORITHM | - |
dc.title | A Two-Stage Multistep-Ahead Electricity Load Forecasting Scheme Based on LightGBM and Attention-BiLSTM | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Hwang, Eenjun | - |
dc.identifier.doi | 10.3390/s21227697 | - |
dc.identifier.scopusid | 2-s2.0-85119334928 | - |
dc.identifier.wosid | 000725215300001 | - |
dc.identifier.bibliographicCitation | SENSORS, v.21, no.22 | - |
dc.relation.isPartOf | SENSORS | - |
dc.citation.title | SENSORS | - |
dc.citation.volume | 21 | - |
dc.citation.number | 22 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Instruments & Instrumentation | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Analytical | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Instruments & Instrumentation | - |
dc.subject.keywordPlus | ALGORITHM | - |
dc.subject.keywordPlus | DECOMPOSITION | - |
dc.subject.keywordPlus | FEATURE-SELECTION | - |
dc.subject.keywordPlus | HYBRID | - |
dc.subject.keywordPlus | NEURAL-NETWORK | - |
dc.subject.keywordPlus | PREDICTION | - |
dc.subject.keywordPlus | SVR MODEL | - |
dc.subject.keywordAuthor | attention mechanism | - |
dc.subject.keywordAuthor | electricity load forecasting | - |
dc.subject.keywordAuthor | light gradient boosting machine | - |
dc.subject.keywordAuthor | multistep-ahead forecasting | - |
dc.subject.keywordAuthor | smart grid | - |
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