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• W2020305429 abstract "Building energy system retrofit and retro-commissioning projects present tremendous opportunities to save energy. Energy consumption in buildings, especially HVAC systems, is significantly impacted by weather conditions. However, short- or long-term climatic data are frequently missing because of data transmission problems, data quality assurance methods, sensor malfunction, or a host of other reasons. These gaps in climatic data continue to provide challenges for HVAC engineers in monitoring and verifying building energy performance. This article examines eight classical approaches that use Linear interpolation, Lagrange interpolation, and Cubic Spline interpolation techniques, and eleven approaches that use two newly developed methods, i.e., Angle-based interpolation and Corr-based interpolation, to restore up to 24 h of missing dry-bulb temperature data in a time series for use in building performance monitoring and analysis. Eleven one-year hourly data sets are used to evaluate the performance of these 19 different methods. Each method is applied to deal with artificial gaps that are generated randomly. In terms of the difference between estimated values and measured values, two types of comparisons are carried out. The first comparison is conducted with three evaluation indices: MAE, RMSE, and STDBIAS. The second comparison is based on the percentage of the total data that can be estimated by an approach within specific error thresholds, including 1°F (0.56°C), 2°F (1.11°C), 3°F (1.67°C), and 5°F (2.78°C), from measured values. The comparison results show that Linear interpolation performs best when filling 1–2 h gaps, Lagrange interpolation (Lag2L2R) outperforms other methods when gaps are 3–8 h long, and the Corr-based interpolation method (Corr1L1R24Avg) is a better technique for filling 9–24 h gaps. This article presents the first part of the research results through the ASHRAE 1413 research project. The second part of the results focuses on methods to filling long-term dry-bulb temperature gaps." @default.
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• W2020305429 date "2014-08-07" @default.
• W2020305429 modified "2023-10-18" @default.
• W2020305429 title "Restoration of 1–24 hour dry-bulb temperature gaps for use in building performance monitoring and analysis—Part I" @default.
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• W2020305429 doi "https://doi.org/10.1080/10789669.2014.925347" @default.
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