Hardware-Centric Exploration of the Discrete Design Space in Transformer–LSTM Models for Wind Speed Prediction on Memory-Constrained Devices
Wind is one of the most important resources in the renewable energy basket. However, there are questions regarding wind as a sustainable solution, especially concerning its upfront costs, visual impact, noise pollution, and bird collisions. These challenges arise in commercial windmills, whereas for...
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| Online Access: | https://www.mdpi.com/1996-1073/18/9/2153 |
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| author | Laeeq Aslam Runmin Zou Ebrahim Shahzad Awan Sayyed Shahid Hussain Kashish Ara Shakil Mudasir Ahmad Wani Muhammad Asim |
| author_facet | Laeeq Aslam Runmin Zou Ebrahim Shahzad Awan Sayyed Shahid Hussain Kashish Ara Shakil Mudasir Ahmad Wani Muhammad Asim |
| author_sort | Laeeq Aslam |
| collection | DOAJ |
| description | Wind is one of the most important resources in the renewable energy basket. However, there are questions regarding wind as a sustainable solution, especially concerning its upfront costs, visual impact, noise pollution, and bird collisions. These challenges arise in commercial windmills, whereas for domestic small-scale windmills, these challenges are limited. On the other hand, accurate wind speed prediction (WSP) is crucial for optimizing power management in renewable energy systems. Existing research focuses on proposing model architectures and optimizing hyperparameters to improve model performance. This approach often results in larger models, which are hosted on cloud servers. Such models face challenges, including bandwidth utilization leading to data delays, increased costs, security risks, concerns about data privacy, and the necessity of continuous internet connectivity. Such resources are not available for domestic windmills. To overcome these obstacles, this work proposes a transformer model integrated with Long Short-Term Memory (LSTM) units, optimized for memory-constrained devices (MCDs). A contribution of this research is the development of a novel cost function that balances the reduction of mean squared error with the constraints of model size. This approach enables model deployment on low-power devices, avoiding the challenges of cloud-based deployment. The model, with its tuned hyperparameters, outperforms recent methodologies in terms of mean squared error, mean absolute error, model size, and R-squared scores across three different datasets. This advancement paves the way for more dynamic and secure on-device wind speed prediction (WSP) applications, representing a step forward in renewable energy management. |
| format | Article |
| id | doaj-art-e026bf3ee4ad4f1c8b966207ba1e3481 |
| institution | OA Journals |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-e026bf3ee4ad4f1c8b966207ba1e34812025-08-20T01:49:28ZengMDPI AGEnergies1996-10732025-04-01189215310.3390/en18092153Hardware-Centric Exploration of the Discrete Design Space in Transformer–LSTM Models for Wind Speed Prediction on Memory-Constrained DevicesLaeeq Aslam0Runmin Zou1Ebrahim Shahzad Awan2Sayyed Shahid Hussain3Kashish Ara Shakil4Mudasir Ahmad Wani5Muhammad Asim6School of Automation, Central South University, Changsha 410083, ChinaSchool of Automation, Central South University, Changsha 410083, ChinaSchool of Engineering, Design and Built Environment, Western Sydney University, Penrith, NSW 2747, AustraliaSchool of Automation, Central South University, Changsha 410083, ChinaDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaEIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi ArabiaEIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi ArabiaWind is one of the most important resources in the renewable energy basket. However, there are questions regarding wind as a sustainable solution, especially concerning its upfront costs, visual impact, noise pollution, and bird collisions. These challenges arise in commercial windmills, whereas for domestic small-scale windmills, these challenges are limited. On the other hand, accurate wind speed prediction (WSP) is crucial for optimizing power management in renewable energy systems. Existing research focuses on proposing model architectures and optimizing hyperparameters to improve model performance. This approach often results in larger models, which are hosted on cloud servers. Such models face challenges, including bandwidth utilization leading to data delays, increased costs, security risks, concerns about data privacy, and the necessity of continuous internet connectivity. Such resources are not available for domestic windmills. To overcome these obstacles, this work proposes a transformer model integrated with Long Short-Term Memory (LSTM) units, optimized for memory-constrained devices (MCDs). A contribution of this research is the development of a novel cost function that balances the reduction of mean squared error with the constraints of model size. This approach enables model deployment on low-power devices, avoiding the challenges of cloud-based deployment. The model, with its tuned hyperparameters, outperforms recent methodologies in terms of mean squared error, mean absolute error, model size, and R-squared scores across three different datasets. This advancement paves the way for more dynamic and secure on-device wind speed prediction (WSP) applications, representing a step forward in renewable energy management.https://www.mdpi.com/1996-1073/18/9/2153wind speed predictionpower forecastinghyperparameter tuningmodel size optimizationrenewable energy managementon-device deployment |
| spellingShingle | Laeeq Aslam Runmin Zou Ebrahim Shahzad Awan Sayyed Shahid Hussain Kashish Ara Shakil Mudasir Ahmad Wani Muhammad Asim Hardware-Centric Exploration of the Discrete Design Space in Transformer–LSTM Models for Wind Speed Prediction on Memory-Constrained Devices Energies wind speed prediction power forecasting hyperparameter tuning model size optimization renewable energy management on-device deployment |
| title | Hardware-Centric Exploration of the Discrete Design Space in Transformer–LSTM Models for Wind Speed Prediction on Memory-Constrained Devices |
| title_full | Hardware-Centric Exploration of the Discrete Design Space in Transformer–LSTM Models for Wind Speed Prediction on Memory-Constrained Devices |
| title_fullStr | Hardware-Centric Exploration of the Discrete Design Space in Transformer–LSTM Models for Wind Speed Prediction on Memory-Constrained Devices |
| title_full_unstemmed | Hardware-Centric Exploration of the Discrete Design Space in Transformer–LSTM Models for Wind Speed Prediction on Memory-Constrained Devices |
| title_short | Hardware-Centric Exploration of the Discrete Design Space in Transformer–LSTM Models for Wind Speed Prediction on Memory-Constrained Devices |
| title_sort | hardware centric exploration of the discrete design space in transformer lstm models for wind speed prediction on memory constrained devices |
| topic | wind speed prediction power forecasting hyperparameter tuning model size optimization renewable energy management on-device deployment |
| url | https://www.mdpi.com/1996-1073/18/9/2153 |
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