Design and Implementation of an LSTM Model with Embeddings on MCUs for Prediction of Meteorological Variables
The use of recurrent neural networks has proven effective in time series prediction tasks such as weather. However, their use in resource-limited systems such as MCUs presents difficulties in terms of both size and stability with longer prediction windows. In this context, we propose a variant of th...
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2025-06-01
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| author | Jhan Piero Paulo Merma Yucra David Juan Cerezo Quina German Alberto Echaiz Espinoza Manuel Alejandro Valderrama Solis Daniel Domingo Yanyachi Aco Cardenas Andrés Ortiz Salazar |
| author_facet | Jhan Piero Paulo Merma Yucra David Juan Cerezo Quina German Alberto Echaiz Espinoza Manuel Alejandro Valderrama Solis Daniel Domingo Yanyachi Aco Cardenas Andrés Ortiz Salazar |
| author_sort | Jhan Piero Paulo Merma Yucra |
| collection | DOAJ |
| description | The use of recurrent neural networks has proven effective in time series prediction tasks such as weather. However, their use in resource-limited systems such as MCUs presents difficulties in terms of both size and stability with longer prediction windows. In this context, we propose a variant of the LSTM model, which we call SE-LSTM (Single Embedding LSTM), which uses embedding techniques to vectorially represent seasonality and latent patterns through variables such as temperature and humidity. The proposal is systematically compared in two parts: The first compares it against other reference architectures such as CNN-LSTM, TCN, LMU, and TPA-LSTM. The second stage, which includes implementation, compares it against the CNN-LSTM, LSTM, and TCN networks. Metrics such as the MAE and MSE are used along with the network weight, a crucial aspect for MCUs such as an ESP32 or Raspberry Pi Pico. An analysis of the memory usage, energy consumption, and generalization across different regions is also included. The results show that the use of embedding optimizes the network space without sacrificing the performance, which is crucial for edge computing. This research is part of a larger project, which focuses on improving agricultural monitoring systems. |
| format | Article |
| id | doaj-art-181734161a4948eaba66eeb9fece1fbe |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
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| spelling | doaj-art-181734161a4948eaba66eeb9fece1fbe2025-08-20T03:29:35ZengMDPI AGSensors1424-82202025-06-012512360110.3390/s25123601Design and Implementation of an LSTM Model with Embeddings on MCUs for Prediction of Meteorological VariablesJhan Piero Paulo Merma Yucra0David Juan Cerezo Quina1German Alberto Echaiz Espinoza2Manuel Alejandro Valderrama Solis3Daniel Domingo Yanyachi Aco Cardenas4Andrés Ortiz Salazar5Professional School of Electronic Engineering, Universidad Nacional de San Agustín de Arequipa, Arequipa 04002, PeruProfessional School of Electronic Engineering, Universidad Nacional de San Agustín de Arequipa, Arequipa 04002, PeruProfessional School of Electronic Engineering, Universidad Nacional de San Agustín de Arequipa, Arequipa 04002, PeruProfessional School of Telecommunications Engineering, Universidad Nacional de San Agustín de Arequipa, Arequipa 04002, PeruProfessional School of Electronic Engineering, Universidad Nacional de San Agustín de Arequipa, Arequipa 04002, PeruDepartment of Computer Engineering and Automation, Federal University of Rio Grande do Norte (DCA-UFRN), Natal 59072-970, RN, BrazilThe use of recurrent neural networks has proven effective in time series prediction tasks such as weather. However, their use in resource-limited systems such as MCUs presents difficulties in terms of both size and stability with longer prediction windows. In this context, we propose a variant of the LSTM model, which we call SE-LSTM (Single Embedding LSTM), which uses embedding techniques to vectorially represent seasonality and latent patterns through variables such as temperature and humidity. The proposal is systematically compared in two parts: The first compares it against other reference architectures such as CNN-LSTM, TCN, LMU, and TPA-LSTM. The second stage, which includes implementation, compares it against the CNN-LSTM, LSTM, and TCN networks. Metrics such as the MAE and MSE are used along with the network weight, a crucial aspect for MCUs such as an ESP32 or Raspberry Pi Pico. An analysis of the memory usage, energy consumption, and generalization across different regions is also included. The results show that the use of embedding optimizes the network space without sacrificing the performance, which is crucial for edge computing. This research is part of a larger project, which focuses on improving agricultural monitoring systems.https://www.mdpi.com/1424-8220/25/12/3601edge computingembeddingsLSTMclimate prediction |
| spellingShingle | Jhan Piero Paulo Merma Yucra David Juan Cerezo Quina German Alberto Echaiz Espinoza Manuel Alejandro Valderrama Solis Daniel Domingo Yanyachi Aco Cardenas Andrés Ortiz Salazar Design and Implementation of an LSTM Model with Embeddings on MCUs for Prediction of Meteorological Variables Sensors edge computing embeddings LSTM climate prediction |
| title | Design and Implementation of an LSTM Model with Embeddings on MCUs for Prediction of Meteorological Variables |
| title_full | Design and Implementation of an LSTM Model with Embeddings on MCUs for Prediction of Meteorological Variables |
| title_fullStr | Design and Implementation of an LSTM Model with Embeddings on MCUs for Prediction of Meteorological Variables |
| title_full_unstemmed | Design and Implementation of an LSTM Model with Embeddings on MCUs for Prediction of Meteorological Variables |
| title_short | Design and Implementation of an LSTM Model with Embeddings on MCUs for Prediction of Meteorological Variables |
| title_sort | design and implementation of an lstm model with embeddings on mcus for prediction of meteorological variables |
| topic | edge computing embeddings LSTM climate prediction |
| url | https://www.mdpi.com/1424-8220/25/12/3601 |
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