  Air conditioning (AC) accounts for a critical portion of the global energy
consumption. To improve its energy performance, it is important to fairly
benchmark its energy performance and provide the evaluation feedback to users.
However, this task has not been well tackled in the residential sector. In this
paper, we propose a data-driven approach to fairly benchmark the AC energy
performance of residential rooms. First, regression model is built for each
benchmarked room so that its power consumption can be predicted given different
weather conditions and AC settings. Then, all the rooms are clustered based on
their areas and usual AC temperature set points. Lastly, within each cluster,
rooms are benchmarked based on their predicted power consumption under uniform
weather conditions and AC settings. A real-world case study was conducted with
data collected from 44 residential rooms. Results show that the constructed
regression models have an average prediction accuracy of 85.1% in
cross-validation tests, and support vector regression with Gaussian kernel is
the overall most suitable model structure for building the regression model. In
the clustering step, 44 rooms are successfully clustered into seven clusters.
By comparing the benchmarking scores generated by the proposed approach with
two sets of scores computed from historical power consumption data, we
demonstrate that the proposed approach is able to eliminate the influences of
room areas, weather conditions, and AC settings on the benchmarking results.
Therefore, the proposed benchmarking approach is valid and fair. As a
by-product, the approach is also shown to be useful to investigate how room
areas, weather conditions, and AC settings affect the AC power consumption of
rooms in real life.
