## 5 Conclusions  
In this study, the development of a low-cost system for wind speed and direction measurement is reported. The sensing device is designed based on temperature distribution trends using a thermopile array, ambient temperature sensor, PTC heater and heat sink. Two ANN models are trained on the thermopile array and ambient temperature data for estimating wind speed and direction. The performance of the proposed system and the ANN model are tested for different sampling intervals and are found to be superior to SVM and LR regression models for estimating wind speed and direction. We can summarize the findings, using our experimental, results as follows:  
* Our proposed system could achieve an R\({}^{2}\) score of up to 0.99 and 0.941 for wind speed and direction estimation using the ANN regression model, respectively, at a 10-min sampling interval with MAE being 0.07 m/s and 13.657\({}^{\circ}\), respectively. * The R\({}^{2}\) score, RMSE and MAE of the proposed system at sampling intervals of 5 min, 2 min, 1 min and 20 s were well under acceptable limits. * The ANN method outperformed SVM regression by 2.2% in terms of R\({}^{2}\) score, 44.7% in RMSE, and 44.5% in MAE for wind speed estimation at a 10-min interval. * The ANN method outperformed LR by 9% in terms of R\({}^{2}\) score, 28.7% in RMSE and 24% in MAE for wind direction estimation at a 10-min interval. Therefore, it can be concluded that our low-cost solution for wind speed and direction sensing can be used for residential and other applications without sacrificing accuracy. The  
Figure 7: Comparison of predicted/estimated and observed wind direction at 10-s sampling interval for (**a**) ANN, (**b**) SVM and (**c**) LR. proposed device can be installed in a small space and has no rotating parts, which increases the reliability and decreases the maintenance requirements. The limitations of this study include:  
* The relatively lower accuracy for wind direction estimation at smaller sampling intervals, which is suspected to be due to the lower spatial resolution of the thermopile array and the design of the heat sink that restricts the airflow from some directions. * The wind speed and direction accuracy decrease with a shorter sampling rate because the changes in thermal distribution are not recorded as quickly as the changes in wind attributes. The proposed system can be potentially used as an outdoor wind sensor and can also be used as a compact and portable flow sensor for indoor and tunnel settings. Other use cases for the proposed device includes, heating, ventilation and air conditioning systems in large commercial buildings and underground tunnels. As future directions, the design of heat sinks and its fins will be studied and improved. In addition, a higher resolution thermopile array with resolutions greater than 8 \(\times\) 8 grid will be considered with faster sampling rates for collecting temperature distribution data. Other environmental factors, such as humidity and atmospheric pressure, will also be considered for estimation modeling. The application potential of this device for monitoring the air flow in ventilation, air conditioning vents and underground tunnels will be explored. Conceptualization, C.-Y.D. ; data curation, C.-Y.D. ; formal analysis, J.-C.T. ; investigation, S.-C.W. ; methodology, C.-Y.D. and J.-C.T. ; software, C.-Y.D. ; supervision, J.-C.T. and [PERSON]-[PERSON]; visualization, C.-Y.D. ; writing, original draft, C.-Y.D. ; writing, review and editing, J.-C.T. and S.-C.W. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Data Availability StatementData used for this study can be provided upon reasonable request. The authors thank the editor and anonymous reviewers for their valuable suggestions and comments that helped improved the quality of this manuscript. The authors declare no conflict of interest. Figure 12: Estimated vs. observed wind speed at 1-min sampling interval using ANN model.