Recent research has demonstrated the fundamental potential of smart heat meter (SHM) data. However, it has also been shown that the usability of the data is reduced because SHM energy measurements are commonly rounded down (truncated) to kilowatt-hour values. This study therefore investigates, for the first time, the error introduced by truncation using a high-resolution dataset. Furthermore, a method is developed to reduce the loss of information in the truncated data by combining smoothing with a ruleset and scaling approach (SMPS). SMPS is shown to increase the pointwise accuracy and correlation of the truncated data with the full-resolution data.