Attention-Based Hydrogen Refueling Imputation Model for Efficient Hydrogen Refueling Stations
During hydrogen refueling, the data values determining the state of charge (SoC) of a vehicle can be missing due to internal and external factors. This causes inaccurate SoC estimation, resulting in oversupply or undersupply. To overcome this issue, an attention-based hydrogen refueling imputation (...
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MDPI AG
2024-11-01
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| Online Access: | https://www.mdpi.com/2076-3417/14/22/10332 |
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| author | Keunsoo Ko Changgyun Kim |
| author_facet | Keunsoo Ko Changgyun Kim |
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| collection | DOAJ |
| description | During hydrogen refueling, the data values determining the state of charge (SoC) of a vehicle can be missing due to internal and external factors. This causes inaccurate SoC estimation, resulting in oversupply or undersupply. To overcome this issue, an attention-based hydrogen refueling imputation (AHRI) model, which restores missing values, is proposed in this paper. In particular, considering that data variables can vary depending on the environmental conditions and equipment in a hydrogen refueling station (HRS), we use the attention mechanism. It determines the primary features, which improves the predictive performance and helps adapt to new conditions. Using the observed data during hydrogen refueling, we train the proposed AHRI model and verify its efficacy. Experimental results show that the proposed AHRI model outperforms existing imputation models significantly. Here, AHRI achieves 0.95 and 0.82 in terms of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi mathvariant="normal">R</mi><mn>2</mn></msup></semantics></math></inline-formula> when 20% and 40% of the values are missing, respectively. These results indicate that the proposed model can be used to solve the data missing problems in HSRs. |
| format | Article |
| id | doaj-art-2ab3cd43d9c44d679b0f6a58b03fec2e |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-11-01 |
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| spelling | doaj-art-2ab3cd43d9c44d679b0f6a58b03fec2e2024-11-26T17:48:20ZengMDPI AGApplied Sciences2076-34172024-11-0114221033210.3390/app142210332Attention-Based Hydrogen Refueling Imputation Model for Efficient Hydrogen Refueling StationsKeunsoo Ko0Changgyun Kim1Department of Artificial Intelligence, The Catholic University of Korea, Bucheon 14662, Republic of KoreaDepartment of Artificial Intelligence & Software, Kangwon National University, Samcheok 25949, Republic of KoreaDuring hydrogen refueling, the data values determining the state of charge (SoC) of a vehicle can be missing due to internal and external factors. This causes inaccurate SoC estimation, resulting in oversupply or undersupply. To overcome this issue, an attention-based hydrogen refueling imputation (AHRI) model, which restores missing values, is proposed in this paper. In particular, considering that data variables can vary depending on the environmental conditions and equipment in a hydrogen refueling station (HRS), we use the attention mechanism. It determines the primary features, which improves the predictive performance and helps adapt to new conditions. Using the observed data during hydrogen refueling, we train the proposed AHRI model and verify its efficacy. Experimental results show that the proposed AHRI model outperforms existing imputation models significantly. Here, AHRI achieves 0.95 and 0.82 in terms of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi mathvariant="normal">R</mi><mn>2</mn></msup></semantics></math></inline-formula> when 20% and 40% of the values are missing, respectively. These results indicate that the proposed model can be used to solve the data missing problems in HSRs.https://www.mdpi.com/2076-3417/14/22/10332efficient hydrogen refueling stationdata imputationdeep learningattention mechanismhydrogen optimizationprocess optimization |
| spellingShingle | Keunsoo Ko Changgyun Kim Attention-Based Hydrogen Refueling Imputation Model for Efficient Hydrogen Refueling Stations Applied Sciences efficient hydrogen refueling station data imputation deep learning attention mechanism hydrogen optimization process optimization |
| title | Attention-Based Hydrogen Refueling Imputation Model for Efficient Hydrogen Refueling Stations |
| title_full | Attention-Based Hydrogen Refueling Imputation Model for Efficient Hydrogen Refueling Stations |
| title_fullStr | Attention-Based Hydrogen Refueling Imputation Model for Efficient Hydrogen Refueling Stations |
| title_full_unstemmed | Attention-Based Hydrogen Refueling Imputation Model for Efficient Hydrogen Refueling Stations |
| title_short | Attention-Based Hydrogen Refueling Imputation Model for Efficient Hydrogen Refueling Stations |
| title_sort | attention based hydrogen refueling imputation model for efficient hydrogen refueling stations |
| topic | efficient hydrogen refueling station data imputation deep learning attention mechanism hydrogen optimization process optimization |
| url | https://www.mdpi.com/2076-3417/14/22/10332 |
| work_keys_str_mv | AT keunsooko attentionbasedhydrogenrefuelingimputationmodelforefficienthydrogenrefuelingstations AT changgyunkim attentionbasedhydrogenrefuelingimputationmodelforefficienthydrogenrefuelingstations |