Evaluation of temperature distribution estimation in deep freezers by using IDW and Kriging interpolation techniques
MEASUREMENT SCIENCE AND TECHNOLOGY, vol.37, no.28, 2026 (SCI-Expanded, Scopus)
- Publication Type: Article / Article
- Volume: 37 Issue: 28
- Publication Date: 2026
- Doi Number: 10.1088/1361-6501/ae7aa1
- Journal Name: MEASUREMENT SCIENCE AND TECHNOLOGY
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Applied Science & Technology Source, Chemical Abstracts Core, Compendex, INSPEC, Engineering Source (EBSCO)
- Open Archive Collection: AVESIS Open Access Collection
- Dokuz Eylül University Affiliated: Yes
Abstract
Accurate thermal mapping of home deep freezers is essential for optimizing performance and increasing energy efficiency. This study aims to generate comprehensive temperature distribution maps using sparse sensor data by adapting spatial interpolation techniques, inverse distance weighting (IDW) and Kriging, to closed-volume environments like refrigerators and deep freezers. Following IEC 62552 standards, the methods were evaluated under loaded and unloaded conditions using MATLAB-based simulations. Performance metrics, including root mean square error (RMSE) and mean absolute error, indicate that while both methods achieve high prediction accuracy, Kriging consistently aligns more closely with actual measurements. In unloaded tests, Kriging achieved an average RMSE reduction of 69.65%, compared to 47.73% for IDW, and maintained its advantage in loaded scenarios with a 50.52% improvement. However, a localized decrease in accuracy was observed at central positions under loaded conditions due to restricted thermal conduction within densely packed volumes. These results highlight the effectiveness of geostatistical interpolation for the economic and operational optimization of appliance testing, while suggesting the need for more complex models to represent internal thermal gradients in high-load configurations.