Regression-Based Temperature Prediction Using Humidity and Pressure Data for Smart Factories
Abstract views: 63
/ 
PDF downloads: 38
Keywords:
ndustry 4.0, Smart Factory, Sensor, RegressionAbstract
With the rapid growth of technology, numerous new innovations appear in our life. One of these
advancements is Industry 4.0, often known as the Fourth Industrial Revolution, which brings together the Internet
of Things (IoT) and cyber-physical systems. IoT enables the transport of sensor data via the internet, allowing for
data exchange and central control systems that do not require human participation. Smart Factories, which are
equipped with automation technologies that allow for real-time monitoring and remote control, improve production
efficiency by guaranteeing that development in each department is handled from a single location. The Smart
Factory Management Information System is a computer-based information system that generates management
reports by aggregating and summarizing transaction records obtained via sensors. This method improves energy
efficiency in response to rising energy demand. It also gives responsible workers with quick access to information,
cost savings, and system security. The primary goal of this study is to estimate temperature using humidity and
pressure data. The BME680 sensor was used to make pressure and humidity-based temperature forecasts.
Temperature predictions were made using Gaussian Process Regression, a machine learning methodology, based
on sensor data, and it was discovered that this method produces more accurate predictions of actual values. The
study demonstrates that a Smart Factory Management Information System loaded with Industry 4.0 innovations can
make substantial contributions to enhancing energy efficiency, lowering costs, and maximizing efficiency.
Downloads
References
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112, p. 18). New York: springer.
Gör, İ. (2014). Vektör nicemleme için geometrik bir öğrenme algoritmasının tasarımı ve uygulaması (Master's thesis, Adnan Menderes Üniversitesi).
Liao, Y., Weng, Y., Liu, G., & Rajagopal, R. (2018). Urban MV and LV distribution grid topology estimation via group lasso. IEEE Transactions on Power Systems, 34(1), 12-27.
Tang, L., Yang, F., & Ma, J. (2014, May). A survey on distribution system feeder reconfiguration: Objectives and solutions. In 2014 IEEE Innovative Smart Grid Technologies-Asia (ISGT ASIA) (pp. 62-67). IEEE.
Amanulla, B., Chakrabarti, S., & Singh, S. N. (2012). Reconfiguration of power distribution systems considering reliability and power loss. IEEE transactions on power delivery, 27(2), 918-926.
Han, J., Kamber, M., & Pei, J. (2011). Data Mining: Concepts and Techniques. Elsevier.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer.
