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Ambient Temperature Prediction for Embedded Systems using Machine Learning



Selma Rahman , Mattias Olausson , Carlo Vitucci, Ioannis Avgouleas

Publication Type:

Conference/Workshop Paper


8th International Conference on Engineering of Computer-based Systems


In this work, we use two well-established machine learning algorithms i.e., Random Forest (RF) and XGBoost, to predict ambient temperature for a baseband's board. After providing an overview of the related work, we describe how we train the two ML models and identify the optimal training and test datasets to avoid the problems of data under- and over-fitting. Given this train/test split, the trained RF and XGBoost models provide temperature predictions with an accuracy lower than one degree Celsius, i.e., far better than any other approach that we used in the past. Our feature importance assessments reveal that the temperature sensors contribute significantly more towards predicting the ambient temperature compared to the power and voltage readings. Furthermore, the RF model appears less volatile than XGBoost using our training data. As the results demonstrate, our predictive temperature models allow for an accurate error prediction as a function of baseband board sensors.


author = {Selma Rahman and Mattias Olausson and Carlo Vitucci and Ioannis Avgouleas},
title = {Ambient Temperature Prediction for Embedded Systems using Machine Learning},
editor = {IEEE},
month = {October},
year = {2023},
booktitle = {8th International Conference on Engineering of Computer-based Systems},
url = {}