MIESTC: A Multivariable Spatio-Temporal Model for Accurate Short-Term Wind Speed Forecasting
Wind speed forecasting is an essential part of weather prediction, with significant value in economics, business, and management. Utilizing multiple meteorological variables can improve prediction accuracy, but existing methods face challenges such as mixing and noise due to variable differences, as...
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| Format: | Article |
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MDPI AG
2025-01-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/16/1/67 |
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| author | Shaohan Li Min Chen Lu Yi Qifeng Lu Hao Yang |
| author_facet | Shaohan Li Min Chen Lu Yi Qifeng Lu Hao Yang |
| author_sort | Shaohan Li |
| collection | DOAJ |
| description | Wind speed forecasting is an essential part of weather prediction, with significant value in economics, business, and management. Utilizing multiple meteorological variables can improve prediction accuracy, but existing methods face challenges such as mixing and noise due to variable differences, as well as difficulty in capturing complex spatio-temporal dependencies. To address these issues, this study introduces a novel short-term wind speed forecasting model named as MIESTC. The proposed model employs an independent encoder to extract features from each meteorological variable, mitigating the issues of noise that are caused by variable mixing. Then, a multivariate spatio-temporal correlation module is used to capture the global spatio-temporal dependencies between variables and model their interactions. Experimental results on the ERA5-LAND dataset show that, compared to the ConvLSTM, UNET, and SimVP models, the MIESTC model reduces RMSE by 14.60%, 8.64%, and 10.41%, respectively, for a 1 h prediction duration. For a 6 h prediction duration, the corresponding reductions are 13.91%, 8.20%, and 6.95%, validating its superior performance in short-term wind speed forecasting. Furthermore, an analysis of variable impacts reveals that U10, V10, and T2M play dominant roles in wind speed prediction, while TP exhibits a relatively lower impact, aligning with the results of the correlation analysis. These findings underscore the potential of MIESTC as an effective and reliable tool for short-term wind speed prediction. |
| format | Article |
| id | doaj-art-8787759911f140c7865a05244d2f0d50 |
| institution | Kabale University |
| issn | 2073-4433 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj-art-8787759911f140c7865a05244d2f0d502025-01-24T13:21:54ZengMDPI AGAtmosphere2073-44332025-01-011616710.3390/atmos16010067MIESTC: A Multivariable Spatio-Temporal Model for Accurate Short-Term Wind Speed ForecastingShaohan Li0Min Chen1Lu Yi2Qifeng Lu3Hao Yang4School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, ChinaSchool of Computer Science, Chengdu University of Information Technology, Chengdu 610225, ChinaKey Laboratory of Coastal Environment and Resources Research of Zhejiang Province, School of Engineering, Westlake University, Hangzhou 310024, ChinaCMA Earth System Modeling and Prediction Centre (CEMC), China Meteorological Administration, Beijing 100081, ChinaSchool of Computer Science, Chengdu University of Information Technology, Chengdu 610225, ChinaWind speed forecasting is an essential part of weather prediction, with significant value in economics, business, and management. Utilizing multiple meteorological variables can improve prediction accuracy, but existing methods face challenges such as mixing and noise due to variable differences, as well as difficulty in capturing complex spatio-temporal dependencies. To address these issues, this study introduces a novel short-term wind speed forecasting model named as MIESTC. The proposed model employs an independent encoder to extract features from each meteorological variable, mitigating the issues of noise that are caused by variable mixing. Then, a multivariate spatio-temporal correlation module is used to capture the global spatio-temporal dependencies between variables and model their interactions. Experimental results on the ERA5-LAND dataset show that, compared to the ConvLSTM, UNET, and SimVP models, the MIESTC model reduces RMSE by 14.60%, 8.64%, and 10.41%, respectively, for a 1 h prediction duration. For a 6 h prediction duration, the corresponding reductions are 13.91%, 8.20%, and 6.95%, validating its superior performance in short-term wind speed forecasting. Furthermore, an analysis of variable impacts reveals that U10, V10, and T2M play dominant roles in wind speed prediction, while TP exhibits a relatively lower impact, aligning with the results of the correlation analysis. These findings underscore the potential of MIESTC as an effective and reliable tool for short-term wind speed prediction.https://www.mdpi.com/2073-4433/16/1/67wind speed forecastingmultiple meteorological variablesspatio-temporal dependenciesindependent encodingspatio-temporal forecasting |
| spellingShingle | Shaohan Li Min Chen Lu Yi Qifeng Lu Hao Yang MIESTC: A Multivariable Spatio-Temporal Model for Accurate Short-Term Wind Speed Forecasting Atmosphere wind speed forecasting multiple meteorological variables spatio-temporal dependencies independent encoding spatio-temporal forecasting |
| title | MIESTC: A Multivariable Spatio-Temporal Model for Accurate Short-Term Wind Speed Forecasting |
| title_full | MIESTC: A Multivariable Spatio-Temporal Model for Accurate Short-Term Wind Speed Forecasting |
| title_fullStr | MIESTC: A Multivariable Spatio-Temporal Model for Accurate Short-Term Wind Speed Forecasting |
| title_full_unstemmed | MIESTC: A Multivariable Spatio-Temporal Model for Accurate Short-Term Wind Speed Forecasting |
| title_short | MIESTC: A Multivariable Spatio-Temporal Model for Accurate Short-Term Wind Speed Forecasting |
| title_sort | miestc a multivariable spatio temporal model for accurate short term wind speed forecasting |
| topic | wind speed forecasting multiple meteorological variables spatio-temporal dependencies independent encoding spatio-temporal forecasting |
| url | https://www.mdpi.com/2073-4433/16/1/67 |
| work_keys_str_mv | AT shaohanli miestcamultivariablespatiotemporalmodelforaccurateshorttermwindspeedforecasting AT minchen miestcamultivariablespatiotemporalmodelforaccurateshorttermwindspeedforecasting AT luyi miestcamultivariablespatiotemporalmodelforaccurateshorttermwindspeedforecasting AT qifenglu miestcamultivariablespatiotemporalmodelforaccurateshorttermwindspeedforecasting AT haoyang miestcamultivariablespatiotemporalmodelforaccurateshorttermwindspeedforecasting |