research-article

Synthesizing Building Operation Data with Generative Models: VAEs, GANs, or Something In Between?

Authors:

Alessandro Salatiello,

Gordon Wichern,

Toshiaki Koike-Akino,

Yoshihiro Ohta,

Christopher Laughman,

Ankush ChakrabartyAuthors Info & Claims

e-Energy '23 Companion: Companion Proceedings of the 14th ACM International Conference on Future Energy Systems

Pages 125 - 133

https://doi.org/10.1145/3599733.3600260

Published: 28 June 2023 Publication History

Abstract

The generation of time-series profiles of building operation requires expensive and time-consuming data consolidation and modeling efforts that rely on extensive domain knowledge and need frequent revisions due to evolving energy systems, user behavior, and environmental conditions. Generative deep learning may be used to provide an automatic, scalable, data-source-agnostic, and efficient method to synthesize these artificial time-series profiles by learning the distribution of the original data. While a range of generative neural networks have been proposed, generative adversarial networks (GANs) and variational autoencoders (VAEs) are most popular models; GANs typically require considerable customization to stabilize the training procedure, while VAEs are often reported to generate lower-quality samples compared to GANs.

In this paper, we propose a network architecture and training procedure that combines the strengths of VAEs and GANs by incorporating Regularized Adversarial Fine-Tuning (RAFT). We imbue the architecture with conditional inputs to reflect ambient/outdoor conditions and operating conditions, and demonstrate its effectiveness by using operational data collected over 585 days from SUSTIE: Mitsubishi Electric’s net-zero energy building. Comparing against classical GAN, VAE, Wasserstein-GAN, and VAE-GAN, our proposed conditional RAFT-VAE-GAN outperforms its competitors in terms of mean accuracy, training stability, and several metrics that ascertain how close the synthetic distribution is to the measured data distribution.

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Cited By

Chakrabarty AVanfretti LTang WPaulson JZhan SBortoff SDeshpande VWang YLaughman C(2024)Assessing Building Control Performance Using Physics-Based Simulation Models and Deep Generative Networks2024 IEEE Conference on Control Technology and Applications (CCTA)10.1109/CCTA60707.2024.10666585(547-554)Online publication date: 21-Aug-2024
https://doi.org/10.1109/CCTA60707.2024.10666585
Sorouifar FPaulson JWang YQuirynen RLaughman CChakrabarty A(2024)Bayesian Forecasting with Deep Generative Disturbance Models in Stochastic MPC for Building Energy Systems2024 IEEE Conference on Control Technology and Applications (CCTA)10.1109/CCTA60707.2024.10666537(414-419)Online publication date: 21-Aug-2024
https://doi.org/10.1109/CCTA60707.2024.10666537

Index Terms

Synthesizing Building Operation Data with Generative Models: VAEs, GANs, or Something In Between?
1. Applied computing
  1. Physical sciences and engineering
    1. Engineering
      1. Computer-aided design
2. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
      1. Modeling methodologies

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cover image ACM Conferences

e-Energy '23 Companion: Companion Proceedings of the 14th ACM International Conference on Future Energy Systems

June 2023

157 pages

ISBN:9798400702273

DOI:10.1145/3599733

Copyright © 2023 ACM.

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Published: 28 June 2023

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e-Energy '23: The 14th ACM International Conference on Future Energy Systems

June 20 - 23, 2023

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Cited By

Chakrabarty AVanfretti LTang WPaulson JZhan SBortoff SDeshpande VWang YLaughman C(2024)Assessing Building Control Performance Using Physics-Based Simulation Models and Deep Generative Networks2024 IEEE Conference on Control Technology and Applications (CCTA)10.1109/CCTA60707.2024.10666585(547-554)Online publication date: 21-Aug-2024
https://doi.org/10.1109/CCTA60707.2024.10666585
Sorouifar FPaulson JWang YQuirynen RLaughman CChakrabarty A(2024)Bayesian Forecasting with Deep Generative Disturbance Models in Stochastic MPC for Building Energy Systems2024 IEEE Conference on Control Technology and Applications (CCTA)10.1109/CCTA60707.2024.10666537(414-419)Online publication date: 21-Aug-2024
https://doi.org/10.1109/CCTA60707.2024.10666537

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