Artificial Intelligence Based Air Conditioner Energy Saving Using a Novel Preference Map

Air Conditioning (AC) systems have contributed to a high percentage of the residential building energy consumption. Most of the recently released AC models are Internet of Things enabled. The data generated from all these ACs can be analyzed to understand the usage pattern and energy saving. In this paper, we have proposed a Cloud based Artificial Intelligence (AI) solution that uses the data from 37,748 ACs to analyze and generate a novel 2-D Preference Map. We have used the Preference Map in our AI solution to automatically generate Top-K energy saving recommendations. These recommendations will be provided to the user when the AC operational settings are selected. Our solution reduces the AC energy consumption by a median 57.38% (for the Top-1 recommendation) compared to the AC settings that were selected by the user. The use of the AC Preference Maps ensures that a wide range of energy saving recommendations are available. This solution provides recommendations that range from maximum energy saving to recommendations that are closer, in value, to the settings selected by the user.