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Indoor Temperature Prediction in an IoT Scenario.
Pedro Lima Monteiro1, Massimiliano Zanin2, Ernestina Menasalvas Ruiz3
1Department of Electrical and Computer Engineering, Faculty of Science and Technology, Universidade Nova de Lisboa, 2829-516 Lisboa, Portugal. pedro.monteiro@uninova.pt.
Sensors (Basel, Switzerland)
|October 26, 2018
Summary
This study demonstrates smart home capabilities by enabling refrigerators to forecast temperature using sensor data. Accurate predictions with minimal error show the potential for enhanced Internet of Things (IoT) device intelligence.
Area of Science:
- Internet of Things (IoT)
- Artificial Intelligence (AI)
Background:
- Developing smarter connected devices through local data processing is a key IoT research area.
- Current research faces challenges due to data access limitations and a lack of practical, high-value test cases.
Purpose of the Study:
- To explore knowledge acquisition in IoT using a practical home refrigerator use case.
- To assess the feasibility of temperature forecasting in a refrigerator using internal and external sensor data.
Main Methods:
- Comparison of various algorithms, including linear correlations and ARIMA models, for temperature prediction.
- Analysis of prediction precision and computational costs using real-world refrigerator data.
Main Results:
- Achieved small average temperature forecast errors, as low as approximately 0.09°C.
- Demonstrated the viability of using sensor data for predictive modeling in home appliances.
Conclusions:
- The study validates the potential for intelligent forecasting in IoT devices like refrigerators.
- Identified pathways for future improvements and broader applications of this predictive technology.
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