# Leveraging Deep Learning and Digital Twins to Improve Energy Performance of Buildings

arXiv: https://arxiv.org/abs/2305.04498 · HTML: https://mlsanity.com/paper/2305.04498/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 31 references that are in the map.
- First posted on arXiv 8 May 2023.

## Builds on (most cited first, 5 of 5)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)
- [N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting](https://mlsanity.com/paper/2201.12886/) (2022)
- [Social-IWSTCNN: A Social Interaction-Weighted Spatio-Temporal Convolutional Neural Network for Pedestrian Trajectory Prediction in Urban Traffic Scenarios](https://mlsanity.com/paper/2105.12436/) (2021)

## Built on by (most cited first, 1 of 1)

- [Edge-based Parametric Digital Twins for Intelligent Building Indoor Climate Modeling](https://mlsanity.com/paper/2403.04326/) (2024)

## Abstract (arXiv)

Digital transformation in buildings accumulates massive operational data, which calls for smart solutions to utilize these data to improve energy performance. This study has proposed a solution, namely Deep Energy Twin, for integrating deep learning and digital twins to better understand building energy use and identify the potential for improving energy efficiency. Ontology was adopted to create parametric digital twins to provide consistency of data format across different systems in a building. Based on created digital twins and collected data, deep learning methods were used for performing data analytics to identify patterns and provide insights for energy optimization. As a demonstration, a case study was conducted in a public historic building in Norrk"oping, Sweden, to compare the performance of state-of-the-art deep learning architectures in building energy forecasting.