Transferring Decomposed Tensors for Scalable Energy Breakdown Across Regions


  • Nipun Batra University of Virginia
  • Yiling Jia University of Virginia
  • Hongning Wang University of Virginia
  • Kamin Whitehouse University of Virginia


Homes constitute roughly one-third of the total energy usage worldwide. Providing an energy breakdown – energy consumption per appliance, can help save up to 15% energy. Given the vast differences in energy consumption patterns across different regions, existing energy breakdown solutions require instrumentation and model training for each geographical region, which is prohibitively expensive and limits the scalability. In this paper, we propose a novel region independent energy breakdown model via statistical transfer learning. Our key intuition is that the heterogeneity in homes and weather across different regions most significantly impacts the energy consumption across regions; and if we can factor out such heterogeneity, we can learn region independent models or the homogeneous energy breakdown components for each individual appliance. Thus, the model learnt in one region can be transferred to another region. We evaluate our approach on two U.S. cities having distinct weather from a publicly available dataset. We find that our approach gives better energy breakdown estimates requiring the least amount of instrumented homes from the target region, when compared to the state-of-the-art.




How to Cite

Batra, N., Jia, Y., Wang, H., & Whitehouse, K. (2018). Transferring Decomposed Tensors for Scalable Energy Breakdown Across Regions. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from



Computational Sustainability and Artificial Intelligence