The Impact of Data Granularity of Indoor Temperature Measurements on the Calculation of Degree Days

Abstract

Abstract Degree-days are to normalise energy consumption data and furthermore can generate forecasting predictions for energy demand being used to compare between different properties across different location and years. The base temperature is the main factor to consider the accuracy of degree days. The aim of this study was to evaluate the impact of data granularity to understand its effect on a correlation between energy consumption and Degree Days. Degree Days were calculated using the standard 18.3 °C base temperature as taking in the United States of America and compare the Degree Days calculations against the calculation based on hourly, daily and monthly data for base temperature. The methodology followed is based on the analysis of 23 houses located in Texas, Austin. The properties under study are from different construction periods and with a variety of total floor areas. This study had demonstrated the effect of the granularity of the data collected to generate Degree Days and its impact on the correlation between energy consumption and degree-days for different base temperatures. While the higher correlations are achieved using a monthly granularity, this approach is not recommended due to the small number of data points and a much more preferred approach that should be taken is a daily approach, which would generate a much more reliable correlation. In this study, higher correlation values were achieved when using the standard 18.3 °C base temperature for the Degree Days calculations, 70 % correlation in daily approach versus 56.67 % using indoor temperature, showing better results across the board against the use of indoor temperature at all granularity levels.