| Summary: | Although indoor temperature was an important criterion for the evaluation of heating requirements, it was costly to install temperature-monitoring devices in every household for large-scale buildings. However, it was inexpensive to install the device at some representative locations, and the average temperature can be used to evaluate the heating requirement. In this case, it was obvious that the accuracy was limited by the location and number of installations and the calculation method. In this paper, first, the indoor temperature variation relationship between the object and adjacent households was analyzed. It was found that the correlation between the household situated above and the household in which the object was located was the strongest, which provides a new energy-saving regulation strategy. Then, the indoor temperature of households in different locations was classified using the <i>k</i>-means algorithm, and the installment location, number of representative points, and comprehensive indoor temperature calculation model were determined. Finally, the installment principle and calculation model were applied. The results show that, compared with the traditional method, the temperature obtained via the proposed method was closer to the actual temperature and was less affected by the instability of communication.
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