Advancing Smart Zero-Carbon Cities: High-Resolution Wind Energy Forecasting to 36 Hours Ahead
Accurate wind speed and power forecasting are key to optimizing renewable wind station management, which is essential for smart and zero-energy cities. This paper presents a novel integrated wind speed–power forecasting system (WSPFS) that operates across various time horizons, demonstrated through...
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| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/24/11918 |
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| author | Haytham Elmousalami Aljawharah A. Alnaser Felix Kin Peng Hui |
| author_facet | Haytham Elmousalami Aljawharah A. Alnaser Felix Kin Peng Hui |
| author_sort | Haytham Elmousalami |
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| description | Accurate wind speed and power forecasting are key to optimizing renewable wind station management, which is essential for smart and zero-energy cities. This paper presents a novel integrated wind speed–power forecasting system (WSPFS) that operates across various time horizons, demonstrated through a case study in a high-wind area within the Middle East. The WSPFS leverages 12 AI algorithms both individual and ensemble models to forecast wind speed (WSF) and wind power (WPF) at intervals of 10 min to 36 h. A multi-horizon prediction approach is proposed, using WSF model outputs as inputs for WPF modeling. Predictive accuracy was evaluated using mean absolute percentage error (MAPE) and mean square error (MSE). Additionally, WSPFS advances the smart wind energy deep decarbonization (SWEDD) framework by calculating the carbon city index (CCI) to define the carbon-city transformation curve (CCTC). Findings from this study have broad implications, from enabling zero-energy urban projects and mega-developments like NEOM and the Suez Canal to advancing global energy trading and supply management. |
| format | Article |
| id | doaj-art-13c0349b7eee40c0b39287a67124ea7c |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-13c0349b7eee40c0b39287a67124ea7c2025-08-20T02:00:59ZengMDPI AGApplied Sciences2076-34172024-12-0114241191810.3390/app142411918Advancing Smart Zero-Carbon Cities: High-Resolution Wind Energy Forecasting to 36 Hours AheadHaytham Elmousalami0Aljawharah A. Alnaser1Felix Kin Peng Hui2Department of Infrastructure Engineering, Faculty of Engineering and Information Technology, The University of Melbourne, Melbourne, VIC 3010, AustraliaDepartment of Architecture and Building Science, Collage of Architecture and Planning, King Saud University, Riyadh 11574, Saudi ArabiaDepartment of Infrastructure Engineering, Faculty of Engineering and Information Technology, The University of Melbourne, Melbourne, VIC 3010, AustraliaAccurate wind speed and power forecasting are key to optimizing renewable wind station management, which is essential for smart and zero-energy cities. This paper presents a novel integrated wind speed–power forecasting system (WSPFS) that operates across various time horizons, demonstrated through a case study in a high-wind area within the Middle East. The WSPFS leverages 12 AI algorithms both individual and ensemble models to forecast wind speed (WSF) and wind power (WPF) at intervals of 10 min to 36 h. A multi-horizon prediction approach is proposed, using WSF model outputs as inputs for WPF modeling. Predictive accuracy was evaluated using mean absolute percentage error (MAPE) and mean square error (MSE). Additionally, WSPFS advances the smart wind energy deep decarbonization (SWEDD) framework by calculating the carbon city index (CCI) to define the carbon-city transformation curve (CCTC). Findings from this study have broad implications, from enabling zero-energy urban projects and mega-developments like NEOM and the Suez Canal to advancing global energy trading and supply management.https://www.mdpi.com/2076-3417/14/24/11918smart wind farm managementsmart and zero-carbon citiesreal-time AIurban deep decarbonizationwind power forecastingsustainability |
| spellingShingle | Haytham Elmousalami Aljawharah A. Alnaser Felix Kin Peng Hui Advancing Smart Zero-Carbon Cities: High-Resolution Wind Energy Forecasting to 36 Hours Ahead Applied Sciences smart wind farm management smart and zero-carbon cities real-time AI urban deep decarbonization wind power forecasting sustainability |
| title | Advancing Smart Zero-Carbon Cities: High-Resolution Wind Energy Forecasting to 36 Hours Ahead |
| title_full | Advancing Smart Zero-Carbon Cities: High-Resolution Wind Energy Forecasting to 36 Hours Ahead |
| title_fullStr | Advancing Smart Zero-Carbon Cities: High-Resolution Wind Energy Forecasting to 36 Hours Ahead |
| title_full_unstemmed | Advancing Smart Zero-Carbon Cities: High-Resolution Wind Energy Forecasting to 36 Hours Ahead |
| title_short | Advancing Smart Zero-Carbon Cities: High-Resolution Wind Energy Forecasting to 36 Hours Ahead |
| title_sort | advancing smart zero carbon cities high resolution wind energy forecasting to 36 hours ahead |
| topic | smart wind farm management smart and zero-carbon cities real-time AI urban deep decarbonization wind power forecasting sustainability |
| url | https://www.mdpi.com/2076-3417/14/24/11918 |
| work_keys_str_mv | AT haythamelmousalami advancingsmartzerocarboncitieshighresolutionwindenergyforecastingto36hoursahead AT aljawharahaalnaser advancingsmartzerocarboncitieshighresolutionwindenergyforecastingto36hoursahead AT felixkinpenghui advancingsmartzerocarboncitieshighresolutionwindenergyforecastingto36hoursahead |