Description: Residential buildings account for a significant proportion of overall energy consumption across the world. Decentralized room level Air Conditioners (ACs) are commonplace in developing countries such as India, contributing a significant share (34% in India) of the total residential energy consumption. Option to independently control each AC presents a prime opportunity for an energy saving system. Thus, we propose PACMAN to non-intrusively (using only the temperature information) predict AC energy consumption prior to usage and estimate energy consumption post-usage.
We discuss various possible applications and use cases of such feedback for the occupants. To empirically validate the performance of PACMAN, we conducted an in-situ study across seven homes in Delhi (India). We collected around 2200 hours of usage data from different ACs, room types, and thermostat temperatures. We achieved an average accuracy of 85.3% and 83.7% with the best accuracy of 97.0% and 93.3% for the estimation and prediction of AC energy consumption respectively, across all homes. Towards the end, we discuss various outlier scenarios, opening up multiple directions for further research in this domain. The code is available on my GitHub account. For more details about the approach, feel free to read the blog post.
Technologies Used: Python
Teammates: Dr. Amarjeet Singh, Dr. Vikas Chandan
- Milan Jain, Amarjeet Singh, Vikas Chandan; “Non-Intrusive Estimation and Prediction of Residential AC Energy Consumption”, Proceedings of the IEEE International Conference on Pervasive Computing and Communications (PerCom 2016) held in Sydney, Australia. [IEEE][Preprint][Slides]